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. 2026 Jan 8;16:1731446. doi: 10.3389/fpls.2025.1731446

Integrated metabolomic and transcriptomic profiling elucidates the tissue-specific biosynthesis and regulation of flavonoids in Machilus nanmu

Xiao Zhang 1, Changying Xia 1, Huan Zhang 1, Wenqiao Li 1, Zhe Zhang 1, Nana Long 1, Renxiu Yao 1, Jian Li 1, Hongping Deng 1,*
PMCID: PMC12823935  PMID: 41584679

Abstract

Introduction

Machilus nanmu is a significant arborescent species of the genus Machilus (Lauraceae), exhibiting considerable potential for applications in industrial materials and healthcare. However, systematic investigations into its flavonoid metabolites and associated biosynthetic mechanisms remain limited, which significantly hinders the efficient exploitation and sustainable utilization of this species.

Methods

This multi-omics study revealed the specific accumulation pattern of flavonoids in the tissues of M. nanmu and pinpointed key structural and regulatory genes underlying their biosynthesis by integrating widely targeted metabolomics and transcriptomics data from roots, stems, and leaves.

Results

A total of 425 flavonoid compounds and 35,671 differentially expressed genes were detected. Further screening revealed 41 structural genes encoding 19 key enzymes (including PAL, CHS, FLS, UGTs, etc.), among which two UGTs (Cluster-69292 and Cluster-71935) were subcellularly localized to the cytoplasm. Furthermore, the weighted gene co-expression network analysis (WGCNA) revealed four key modules exhibiting strong correlations with flavonoid content. From these modules, four core transcription factors (TFs) from the MYB and bHLH families were identified as putative regulators of flavonoid biosynthesis.

Discussion

Our findings offer the first comprehensive model of tissue-specific flavonoid accumulation in M. nanmu, enabling the dissection of its transcriptional machinery and advancing strategies for its genetic improvement and resource exploitation.

Keywords: Machilus nanmu, metabolomics, transcriptome, flavonoids, biosynthetic pathway, transcription factors

1. Introduction

Machilus nanmu is an arboreal species within the genus Machilus (Lauraceae) (Flora of China Editorial Committee, 2018). It is recognized for its rich profile of bioactive metabolites, which contribute to its efficacy in alleviating conditions such as dermatitis, edema, and diarrhea. Polysaccharides derived from its leaves have demonstrated notable antioxidant and antitumor activities (Zhao et al., 2011). The genus Machilus comprises a diverse array of species, many of which are valued for their high-quality timber and represent significant economic forest resources in southern China. These species exhibit broad application potential in industrial wood, landscaping, medicine, spice production, chemical engineering, and cosmetics (Xu et al., 2016; Thin and Thinh, 2024). Several species within the genus Machilus are also employed in traditional Chinese medicine for their anti-infective, anti-inflammatory, antimicrobial, and analgesic properties (Jiangsu New Medical College, 1977; Liu et al., 2012; Ma et al., 2009; Cheng et al., 2013). Despite its extensive traditional uses and industrial potential, the chemical constituents of M. nanmu remain systematically uncharacterized, which hinders the efficient exploitation and sustainable development of this resource. Therefore, a comprehensive investigation into its metabolome is of considerable scientific and practical importance. Recent studies have demonstrated that widely targeted metabolomics is characterized by high precision and broad coverage of metabolites. The integration of this method with high-throughput transcriptomics has been widely employed to investigate genes and metabolites, enabling a comprehensive understanding of biosynthetic pathways (Contrepois et al., 2020). The combined application of these approaches to M. nanmu is expected to provide a crucial explanation for the biosynthesis and regulation of its metabolites, thereby facilitating the sustainable utilization and genetic improvement of this valuable plant resource.

Plants of the genus Machilus contain flavonoids, lignans, terpenoids, alkaloids and so on (Yu et al., 2000; Gan et al., 2011; Lin et al., 2015; Xie et al., 2022). Flavonoids constitute an essential class of secondary metabolites and their structures can be classified as flavones, flavonols, isoflavones, anthocyanins, flavanols, flavanones, and chalcones (Shen et al., 2022). In humans, flavonoids have the functions of free radical scavengers, antimicrobial agents, and antioxidants (Kusano et al., 2011; Crozier et al., 2009). For instance, Apigenin (flavones) inhibits tumor angiogenesis by suppressing ARHGEF1-mediated microvesicle biogenesis (Zhang et al., 2024). It also ameliorates hyperuricemic nephropathy via inhibition of URAT1 and GLUT9 (Li et al., 2021). Myricetin (flavonols) exhibits cytotoxic effects on SNU-790 HPTC cells (Ha et al., 2017). Isoflavones are found predominantly in leguminous plants. Daidzein and genistein are known for their phytoestrogenic effects, which help alleviate menopausal symptoms and prevent osteoporosis (Tousen et al., 2013; Zhou et al., 2025). Serving as precursors in flavonoid biosynthesis, chalcones display antibacterial, antitumor, and antiviral activities (Ouyang et al., 2021), and their derivatives have shown potential in the treatment of Alzheimer’s disease (Haas et al., 2024). As an important tree species of the genus Machilus (Lauraceae), M. nanmu is widely distributed with abundant resources. However, current research on the species, content, pharmaceutical activities, and biosynthetic mechanisms of flavonoids in M. nanmu remains largely scarce, which has severely hindered the development and utilization of its pharmaceutical value. Therefore, conducting studies related to flavonoids in M. nanmu to fill these existing research gaps is of great significance for exploring its pharmaceutical potential and promoting innovations in natural medicinal resources.

Flavonoid compounds play a crucial role in the growth, development and stress responses of plants (Zhang et al., 2017). The enzymes that regulate the biosynthesis of flavonoids are also of great significance. For instance, overexpression of BcPAL1 and BcPAL2 in non-heading Chinese cabbage enhanced thermotolerance, accompanied by increased phenylalanine ammonia-lyase (PAL) activity (Gao et al., 2025). Under light stress conditions, CsCHS in tea plants can regulate the biosynthesis process of flavonoids (Li et al., 2024). bHLHL74 negatively regulates the flavonoid biosynthesis process in rose by repressing CHS1 expression under salt stress (Ren et al., 2024). In Ginkgo biloba, the antisense LncNAT11 negatively regulates flavonol biosynthesis and reactive oxygen species (ROS) accumulation under salinity by suppressing GbMYB11 expression and subsequently downregulating GbF3’H and GbFLS (Liu et al., 2025). Additionally, UDP-glycosyltransferases influence grain size and abiotic stress tolerance in rice by redirecting metabolic flux (Dong et al., 2020). CHS (TT4) and CHI (TT5) are central to flavonoid-mediated UV-B protection in Arabidopsis thaliana (Kusano et al., 2011). Collectively, these genes form a critical chemical defense network that enables plants to adapt to various environmental challenges. Notably, the expression of these structural genes is not autonomous but is precisely orchestrated by a sophisticated transcriptional regulatory network. For instance, the MBW complex is an important regulatory factor in the biosynthesis of anthocyanins in numerous plant species. It is composed of MYB, bHLH and WD40 proteins (Xie et al., 2016; Liu et al., 2021; Chen et al., 2023). The MtMYB134 coordinates flavonol biosynthesis in Medicago truncatula (Naik et al., 2021). The MdMYB305–MdbHLH33–MdMYB10 complex modulates anthocyanin homeostasis in apple by binding to MdF3H, MdDFR, and MdUFGT (Zhang et al., 2023). These findings illustrate how transcriptional regulators integrate stress signals with metabolic responses to regulate flavonoid biosynthesis. However, to date, research on the coordinated regulation of growth, development, and stress resistance by flavonoids and TFs in M. nanmu remains largely unexplored. The underlying regulatory pathways, key genes, and interaction modes involved in this process are still unclear. Filling this research gap will not only improve the molecular theoretical system governing the regulation of flavonoids and TFs in Lauraceae plants, but also provide a scientific basis for stress-resistant breeding and efficient resource utilization of M. nanmu. Furthermore, it holds great significance for advancing research on plant molecular regulatory mechanisms and promoting innovations in the forestry industry.

Therefore, we employed transcriptomics and metabolomics in this study to investigate the biosynthetic pathways in various tissues of M. nanmu. To clarify the mechanisms of the accumulation of tissue-specific flavonoids, we identified the genes that encode enzymes and TFs involved in flavonoid biosynthesis. Furthermore, qRT-PCR was conducted to validate the transcriptomic level. Our results systematically characterize the composition and spatial distribution of flavonoid metabolites in M. nanmu, revealing putative associations between critical metabolites and regulatory genes. These findings establish a molecular framework for exploring the biosynthesis and accumulation process of flavonoids in this species, thereby supporting future research into their regulatory mechanisms and biological functions.

2. Materials and methods

2.1. Materials and chemicals

In the present study, roots (R), stems (S) and leaves (L) were collected from three-year-old healthy M. nanmu plants growing naturally on Jinyun Mountain, Beibei, Chongqing, China. Specifically, roots (3–5 cm from the apices of taproots and lateral roots), stems (3–5 cm from the shoot apex), and the 1st to 3rd fully expanded leaves from the top of the plants were immediately wrapped in aluminum foil, labeled, and quickly placed in liquid nitrogen for subsequent metabolomic and transcriptomic analyses. Three individual trees were combined into a group, with three groups. HPLC reagents: formic acid, Aladdin Reagent Co., Ltd. (Shanghai, China); acetonitrile and methanol, Merck Group (Darmstadt, Germany).

2.2. Metabolite extraction, qualitative and quantitative, and screening analysis

M. nanmu samples were vacuum freeze-dried, then ground and crushed. 50 mg of the powder was weighed and extracted with 1200 μL of 70% methanol-water solution (containing an internal standard) for subsequent UPLC-MS/MS analysis. By comparing the MS/MS spectra from the experiments with the self-constructed Metware database (MWDB), metabolites were identified. Quantitative analysis was performed using the multiple reaction monitoring (MRM) method on a triple quadrupole mass spectrometer. Unsupervised PCA was conducted using the prcomp function in R. Sample and metabolite clustering patterns were visualized through HCA heatmaps. Inter-sample correlations were calculated using PCC and displayed in heatmaps. In the comparison groups, the differentially expressed metabolites were identified based on VIP > 1 and |Log2FC| ≥ 1.0.

2.3. RNA extraction, transcriptome sequencing and analysis

Total RNA was extracted from each tissue of M. nanmu tissues using a plant RNA extraction kit (Hua Yueyang, Beijing, China). Following a rigorous assessment of RNA quality, sequencing was performed on the Illumina HiSeq platform. The resultant raw reads were processed with fastp to obtain high-quality clean data (Chen et al., 2018). Then, Trinity software was used for de novo transcriptome assembly (Grabherr et al., 2011). The assembly results were refined by clustering and removing redundancies of the transcripts using Corset (Davidson and Oshlack, 2014). Putative coding regions (CDS) within these transcripts were identified using TransDecoder, which also facilitated the deduction of corresponding amino acid sequences. The assembled transcript sequences were compared with the KEGG, NR, Swiss-Prot, GO, COG/KOG, and Trembl databases using DIAMON (Buchfink et al., 2015). Protein domain prediction was conducted by searching the Pfam database with HMMER. To quantify gene expression, the transcript abundance was estimated by RSEM and normalized as FPKM values (Li and Dewey, 2011). DESeq2 was used to analyze the differential expression between sample groups (Love et al., 2014; Varet et al., 2016), with significance thresholds set at an adjusted p-value and |Log2FC|. Subsequently, enrichment analyses for KEGG pathways (Kanehisa et al., 2008) and GO terms (Ashburner et al., 2000) were carried out based on a hypergeometric distribution test. Finally, the iTAK software was used to screen for potential TFs (Zheng et al., 2016).

2.4. Weighted gene co-expression network analysis

The WGCNA R software package is used for weighted gene co-expression network analysis. PCA was conducted between the module eigengenes and the abundance of key flavonoids, and the relationships between modules and metabolites were visualized. Lastly, a regulatory network integrating metabolites, TFs, and structural genes was reconstructed and visualized using Cytoscape.

2.5. Quantitative real-time PCR validation

Twelve DEGs were randomly selected for qRT-PCR analysis to verify the accuracy and reliability of the transcriptome sequencing results. RNA extraction and reverse transcription of the roots, stems and leaves of M. nanmu were carried out using an RNA Kit (Quanshijin, Beijing, China) and a cDNA Synthesis Kit (TQ2501, OMEGA Bio-Tek). qRT-PCR was carried out using SYBR green master mix (TQ2300, OMEGA Bio-Tek). The reactions were executed on the BIO-RAD CFX Connect Real-Time System (Bio-Rad) under the following PCR conditions: 95°C for 3 min, followed by 39 cycles of 95°C for 5 sec, 60°C for 30 sec, and 60°C for 5 sec. The internal control was the actin (ACT, KM086738.1) of Cinnamomum camphora. The primer sequences are listed in Supplementary Table S1. The corresponding expression levels were calculated using the 2−ΔΔCT method. Each reaction included three biological replicates and three technical replicates.

2.6. Subcellular localization

The subcellular localization of the proteins expressed transiently in N. benthamiana protoplasts was previously described by Rolland (2018). Briefly, Agrobacterium (Agrobacterium tumefaciens) strain EHA105 that contained constructs 35S-EGFP, 35S-Cluster-69292-EGFP and 35S-Cluster-71935-EGFP was infiltrated into N. benthamiana leaves. Protoplasts were isolated 72h after infiltration. Images were obtained by the Zeiss 980 laser scanning confocal microscope (Zeiss GmbH, Oberkochen, Germany).

2.7. Statistical analysis

Microsoft Excel 2010 was used for data preprocessing, and subsequently, the GraphPad Prism software was employed to generate the graphical representations (GraphPad PRISM, Version 10.1.2).

3. Result

3.1. Metabolite analysis in different tissues of M. nanmu

UPLC-MS/MS was used to profile metabolites in different tissues of M. nanm. The total ion chromatograms (TIC) represented the summed intensity of all ions at each time point, which was obtained from quality control (QC) samples (Supplementary Figures S1A, B). In the extracted ion chromatograms (XIC), peaks of different colors corresponded to distinct metabolite classes (Supplementary Figures S1C, D). Overlaid TIC from QC samples showed that the data were reliable, as there is a significant overlap in both retention time and peak response intensity (Supplementary Figures S1E, F). In the roots, stems and leaves of M. nanmu, a total of 1937 metabolites were detected. Among them, 1036 were identified in the positive ion mode and 901 in the negative ion mode. (Supplementary Table S2). These metabolites were categorized into 11 major classes (Figure 1A), including flavonoids (21.94%), phenolic acids (21.27%), alkaloids (9.91%), amino acids and derivatives (7.02%), lignans and coumarins (6.66%), lipids (7.9%), nucleotides and derivatives (3.51%), organic acids (5.21%), tannins (2.12%), terpenoids (4.39%), and others (10.07%).

Figure 1.

A donut chart labeled “A” shows the class distribution of compounds, with Phenolic acids being the largest at 21.27% and Tannins the smallest at 2.12%. Chart B is a 2D PCA plot showing group clustering, with PC1 and PC2 axes. Plot C is a circular heatmap with hierarchical clustering, displaying varying color intensities. D is a triangular correlation heatmap, showing values as color gradients, with a legendindicating the scale from 0 to 1.

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Metabolite composition, abundance, and correlation across different tissues of M. nanmu. (A) Metabolite classification in different tissues of M. nanmu. (B) PCA score plot derived from metabolite relative abundance. (C) HCA of all detected metabolites. Data are organized from the center to the edge by name, with green indicating relatively low intensity and red indicating relatively high intensity. (D) Pairwise Pearson correlation matrix among samples from different tissues. The color scale represents correlation coefficients. Red shades: positive correlations; Blue shades: negative correlations; Green shades: weaker correlations. Specific coefficient values are displayed within each quadrant.

PCA revealed that PC1 and PC2 accounted for 39.91% and 23.72% of the total variance, respectively (Figure 1B), with a cumulative contribution rate of 63.63%. The three biological replicates of each tissue type formed tight clusters, and clear separations were observed among roots, stems, and leaves, indicating distinct metabolite profiles among the three tissues. OPLS-DA was applied to pairwise comparisons (R vs S, R vs L, S vs L) for identifying differentially accumulated metabolites. Score plots for each OPLS-DA model are shown in Supplementary Figures S2A–C. To validate the models, 200 permutation tests were performed. The values of R2X and R2Y represent the explained variance of the X and Y matrices, and Q2 indicates the predictive ability. Values closer to 1 indicate more stable and reliable models. If Q2 > 0.9, it is considered to be extremely excellent. In this study, the following model parameters were obtained: R vs S (R2X = 0.722, R2Y = 1, Q2 = 0.974), R vs L (R2X = 0.748, R2Y = 1, Q2 = 0.985), and S vs L (R2X = 0.684, R2Y = 1, Q2 = 0.97) (Supplementary Figures S2D–F). All R2Y and Q2 values exceeded 0.9, confirming the stability and appropriateness of the models. HCA, based on the relative abundance of all metabolites, showed that the majority of detected metabolites exhibited significant concentration differences among the different plant parts (Figure 1C). Furthermore, a correlation heatmap indicated high reproducibility among replicates within each tissue type (Figure 1D).

Among all detected metabolites, flavonoids represented the most abundant category, with a total of 425 compounds identified. These included 154 flavones, 140 flavonols, 45 flavanones, 32 flavanols, 23 chalcones, 14 flavanonols, 4 isoflavones, and 13 other flavonoids (Supplementary Figure S3).

3.2. Differential accumulated metabolites analysis in different tissues of M. nanmu

To gain a deeper understanding of the metabolic differences between R vs S, R vs L, and S vs L, we identified DAMs using thresholds of Fold Change (FC) ≥ 2 or ≤ 0.5 and VIP ≥ 1. We had detected a total of 1,364 DAMs (Supplementary Table S3). The screening results are visualized in volcano plots (Figures 2A–C) and a Venn diagram (Figure 2D). Specifically, 950 DAMs (729 up- and 221 down-regulated) were identified between R vs S (Figure 2A); 1,057 DAMs (799 up- and 258 down-regulated) between R vs L (Figure 2B); and 820 DAMs (457 up- and 363 down-regulated) between S vs L (Figure 2C). To reveal major trends and tissue-specific accumulation patterns of these metabolites, all DAMs were subjected to K-means clustering analysis and grouped into 10 subclasses (subclasses 1 to 10, Figure 3). Subclasses 1, 4, and 6-containing 199, 63, and 26 DAMs, respectively,-showed the highest abundance in roots and lower levels in other tissues. These were predominantly alkaloids (32.66%), amino acids and derivatives (22.22%), and alkaloids (23.08%), respectively. Subclasses 2, 3, and 10, comprising 96, 160, and 147 DAMs, exhibited peak accumulation in stems, with the most abundant classes being flavonoids (26.04%), phenolic acids (32.50%), and phenolic acids (33.33%). Subclasses 5, 7, 8, and 9, consisting of 42, 231, 276, and 124 DAMs, were most abundant in leaves, and were mainly composed of phenolic acids (26.19%), flavonoids (34.63%), flavonoids (39.49%), and flavonoids (30.56%). Among these, subclass 8 contained the highest number of DAMs (Supplementary Table S3). DAMs from the three comparison groups (R vs S, R vs L, and S vs L) were categorized into 11 classes (Supplementary Figure S4). HCA indicated that flavonoids constituted the majority of DAMs (Supplementary Figure S5). Therefore, subsequent analysis focused on flavonoid variations across different tissues.

Figure 2.

Four panels show data visualizations. Panel A: Volcano plot with Log(Fold Change) and Variable Importance, indicating 729 upregulated, 221 downregulated, and 984 insignificant features. Panel B: Similar plot with 799 up, 258 down, and 873 insignificant features. Panel C: Another plot with 457 up, 363 down, and 1110 insignificant features. Panel D: Venn diagram comparing overlap among three conditions, labeled R_vs_S, R_vs_L, and S_vs_L, with sections showing varying degrees of overlap.

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DAMs in different tissues of M. nanmu. (A–C) Volcano plots displaying DAMs between tissue comparisons: R vs S (A), R vs L (B), and S vs L (C). Red points: up-regulated metabolites; Green points: down-regulated metabolites; Gray points: no significant differences. (D) Venn diagram illustrating the overlap and tissue-specific DAMs across the three comparison groups.

Figure 3.

Twelve line graphs display standardized values across categories R, S, and L for ten subclasses, with totals indicated for each subclass. Variations in trends and shading are shown in different colors, indicating diverse data patterns across the subclasses.

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K-means clustering of differential metabolites across three M. nanmu tissues based on accumulation patterns. The x-axis indicates tissue type, while the y-axis shows Z-score normalized relative metabolite abundance.

3.3. Variation in flavonoid compounds in different parts of M. nanmu

Flavonoids play a crucial role in plant adaptation and defense against environmental stresses. Besides their physiological functions in plants, they have notable medicinal and nutritional benefits (Zhang et al., 2017). Therefore, we further examined the composition and accumulation patterns of flavonoids in the roots, stems, and leaves of M. nanmu. Based on their accumulation patterns, flavonoids were generally most abundant in leaves, followed by stems, with lower levels in roots (Supplementary Figure S6). We further conducted pairwise comparisons of flavonoid metabolites for R vs S, R vs L, and S vs L (Table 1). Between R vs S, 125 flavonoids showed differential accumulation (121 up- and 4 down-regulated), with fold-change values ranging from 0.46 to 1,097,450.34 (Figure 4A). The top 3 flavonoids with the highest fold changes are Eriodictyol-7-O-glucoside (1,097,450.34-fold), 6-C-Methylquercetin-3-O-glucoside (332,494.62-fold), and Amoenin (249,958.15-fold). A total of 154 flavonoids exhibited differential accumulation between R vs L (148 up-regulated and 6 down-regulated), with fold changes ranging from 0.00 to 2,267,330.96 (Figure 4B). The 3 most differentially accumulated flavonoids are Vitexin-2’’-O-rhamnoside (2,267,330.96-fold), Apigenin-7-O-Gentiobioside (806,805.00-fold), and Luteolin-7-O-gentiobioside (621,552.05-fold). 112 flavonoids differentially accumulated between S vs L (83 up- and 29 down-regulated), exhibiting fold change values ranging from 0.00 to 621,552.05 (Figure 4C). The top 3 flavonoids ranked by fold change are Luteolin-7-O-gentiobioside (621,552.05-fold), Patuletin-3-O-glucoside (14,165.29-fold), and Apigenin-7-O-glucuronide (2,772.42-fold). Venn diagram analysis further identified 81 tissue-specific DAMs and 36 shared DAMs across all three tissue comparisons (Figure 4D).

Table 1.

Statistics of DAM types across comparison groups.

Classification Numbers R vs S R vs L S vs L
Flavones 75 35 53 43
Flavanones 22 11 18 13
Flavonols 72 42 50 19
Flavanols 20 17 12 14
Flavanonols 8 5 6 6
Other Flavonoids 9 8 5 6
Chalcones 9 6 7 8
Isoflavones 3 1 3 3
Total 218 125 154 112
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Figure 4.

Panel A shows a bar chart with various compounds and their positive Log₂FC values, highlighting eriodictyol-7-O-glucoside as the highest at 20.07. Panel B features another bar chart with vitexin-2''-O-rhamnoside leading the positive Log₂FC values at 21.11, and some negative values depicted in green. Panel C displays a mix of positive and negative Log₂FC values, with luteolin-7-O-gentiobioside highest at 19.25 and eriodictyol-7-O-glucoside lowest at -20.07. Panel D is a Venn diagram illustrating the intersection and unique elements among three comparisons: S_vs_L, R_vs_L, and R_vs_S.

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DAMs across M. nanmu tissues. (A-C) Top 20 flavonoids ranked by fold change in pairwise comparisons: R vs S (A), R vs L (B), and S vs L (C). Red bars: up-regulated metabolites; Green bars: down-regulated metabolites. (D) Venn diagram of DAMs.

3.4. Transcriptome sequencing and functional characterization of DEGs

To investigate the transcriptomic profiles of different tissues in M. nanmu, we performed RNA sequencing on nine samples. The RNA-seq dataset yielded 75.46 Gb of clean data, with each sample containing ≥ 6 Gb. The Q30 base percentage exceeded 94%, and GC content ranged from 45.15% to 47.14% (Supplementary Table S4). Clean reads were assembled using Trinity, and the resulting transcript sequences served as the reference for subsequent analyses. As shown in Supplementary Figure S7, the assembly exhibited high completeness; the longest cluster sequences obtained after Corset-based hierarchical clustering were defined as unigenes for downstream analysis. A total of 319,687 transcripts and 168,717 unigenes were generated (Supplementary Table S5), with length distributions displayed in Supplementary Figure S8. Unigene sequences were annotated by aligning them against the KEGG, NR, Swiss-Prot, TrEMBL, COG/KOG, and GO databases using DIAMOND BLASTX. Amino acid sequences predicted from unigenes were further analyzed with HMMER against the Pfam database. The annotation results were as follows: 71,313 (42.27%), 98,130 (58.16%), 66,303 (39.30%), 99,381 (58.90%), 57,048 (33.81%), 86,000 (50.97%), and 67,499 (40.01%) unigenes were annotated to the respective databases (Supplementary Table S6). PCA indicated that PC1 and PC2 accounted for 36.34% and 17.29% of the gene expression variance among samples, respectively (Figure 5A). Correlation analysis between biological replicates showed that an |r| value closer to 1 (represented by red color) indicates stronger reproducibility between replicates (Figure 5B). Gene expression levels spanned six orders of magnitude, from 10-2 to 104 (Figure 5C). These results demonstrate that the sequencing quality was sufficient for further analysis.

Figure 5.

A composite image of six panels depicting various data visualizations: A: A 2D PCA plot showing three groups labeled R, S, and L, separated by principal components PC1 and PC2. B: A correlation heatmap with pie charts overlaying each cell, indicating relationships among samples R-1 to L-3. C: A box plot showing the distribution of log-transformed FPKM values across samples R-1 to L-3. D: A heatmap with hierarchical clustering of samples R-1 to L-3, colored by Z-scores. E: A Venn diagram illustrating the overlap of elements between comparisons R_vs_S, R_vs_L, and S_vs_L. F: A bar graph showing counts of total, downregulated, and upregulated elements across comparisons R_vs_L, R_vs_S, and S_vs_L.

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Transcriptomic analysis and identification of DEGs across M. nanmu tissues. (A) PCA of each sample. (B) Pairwise Pearson correlation analysis of gene expression profiles. (C) Box plot of all samples’ gene expression levels. (D) Expression of DEGs in different tissues. (E) Venn diagram. (F) Number of DEGs in each tissue.

To identify differentially expressed genes (DEGs) associated with flavonoid biosynthesis in different tissues of M. nanmu, we performed differential expression analysis between sample groups using DESeq2, with screening thresholds set at |log2FC| ≥ 1 and FDR < 0.05. A total of 35,671 DEGs were detected, showing significant expression variation among R, S, and L (Figure 5D). Pairwise comparisons revealed 12,172 DEGs (4,429 up-regulated, 7,743 down-regulated) between R vs S, including genes encoding UDP-glycosyltransferase, flavonol synthase, isoflavone 2’-hydroxylase, and anthocyanidin 3-O-glucosyltransferase. Between R vs L, a total of 29,523 DEGs (12,599 up-regulated, 16,924 down-regulated) were detected, encompassing leucoanthocyanidin reductase, UDP-glycosyltransferase, anthocyanidin synthase, flavonol synthase, chalcone synthase, flavonoid 3’-hydroxylase, flavanone 3-hydroxylase, and isoflavone 2’-hydroxylase. Between S vs L, 22,866 DEGs (10,802 up-regulated, 12,064 down-regulated) involving anthocyanidin reductase, leucoanthocyanidin reductase, UDP-glycosyltransferase, anthocyanidin synthase, flavonoid 3’-hydroxylase, flavonol synthase, anthocyanidin 3-O-glucosyltransferase, and isoflavone 2’-hydroxylase (Figures 5E, F). These DEGs are likely to play crucial roles in regulating tissue-specific flavonoid biosynthesis in M. nanmu.

We conducted GO and KEGG pathway enrichment analyses to elucidate the functional implications of the identified DEGs. The GO annotation categorized DEGs into three principal categories: Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). Across the R vs S, R vs L, and S vs L comparisons, GO classification identified 44, 45, and 44 subcategories, respectively. These DEGs were further grouped into 32 functional subcategories based on homology mapping (Supplementary Figures S9A–C). In the BP categories, DEGs were predominantly associated with cellular processes, metabolic processes, and response to stimulus. Within the CC categories, the most represented terms encompassed cellular anatomical entities and protein-containing complexes. For MF, the majority of DEGs were implicated in binding, catalytic activity, and transcription regulator activity. KEGG pathway enrichment analysis revealed that the DEGs were mapped to 146, 147, and 145 pathways in the R vs S, R vs L, and S vs L comparisons, respectively. Notably, the biosynthesis of flavonoids (ko00941) and isoflavonoids (ko00943) was significantly enriched among these pathways (Figures 6A–C).

Figure 6.

Three bubble charts labeled A, B, and C display various metabolic pathways against a rich factor. Each chart includes pathways such as biosynthesis and metabolism types. The bubble size represents count, and the color indicates the Q-value, with a gradient from blue (1.00) to red (0.00). Chart A ranges from 0.2 to 0.5, B ranges from 0.3 to 0.6, and C ranges from 0.3 to 0.4 on the rich factor scale.

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KEGG pathway enrichment analysis of DEGs across three tissue comparisons. R vs S (A), R vs L (B) and S vs L (C) display the top 20 significantly enriched KEGG pathways for each comparison. The y-axis denotes pathway names, and the x-axis represents the richness factor, with greater values indicating higher enrichment levels. Point size corresponds to the number of DEGs mapped to a given pathway, and color intensity reflects the statistical significance of the enrichment.

3.5. Key structural genes and pathway mapping in the flavonoid biosynthetic pathway

Based on KEGG enrichment analysis and functional annotation of DEGs, a total of 41 genes encoding 19 key enzymes involved in flavonoid biosynthesis were identified (Supplementary Table S7). The flavonoid biosynthetic pathway in different tissues of M. nanmu was reconstructed based on these 41 genes and key enzymes from the KEGG pathway (Figure 7). The 19 key enzymes are as follows: phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), chalcone synthase (CHS), chalcone isomerase (CHI), isoflavone 2’-hydroxylase (I2’H), flavanone 3-hydroxylase (F3H), flavonoid 3’-hydroxylase (F3’H), flavonoid 3’,5’-hydroxylase (F3’5’H), flavonol synthase (FLS), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin reductase (LAR), anthocyanidin synthase (ANS), anthocyanidin reductase (ANR), anthocyanidin 3-O-glucosyltransferase (BZ1), flavonoid 6-hydroxylase (CYP71D9), 2-hydroxyisoflavanone synthase (CYP93C), flavonol-3-O-glucoside L-rhamnosyltransferase (FG2), anthocyanidin 3-O-glucoside 5-O-glucosyltransferase (UGT75C1), and phlorizin synthase (PGT1). Among these enzymes, UDP-glycosyltransferases (UGTs) were the most abundant, with 10 genes identified, including BZ1, FG2, UGT75C1, and PGT1, suggesting their crucial role in regulating tissue-specific flavonoid biosynthesis in M. nanmu.

Figure 7.

Diagram illustrating the biosynthesis pathways of isoflavonoids, flavones, flavonols, flavonoids, and anthocyanins. It details the conversion of phenylalanine to various compounds such as daidzein, naringenin, and pelargonidin through specific enzymes. The image includes dotted lines for enzyme-catalyzed reactions, and color-coded heatmaps representing gene clusters involved in these pathways. A legend indicates the color scale for expression levels and distinguishes between genes and metabolites.

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Coordinated expression patterns of structural genes and associated metabolites in the flavonoid biosynthetic pathway. Red: up-regulation; Blue: down-regulation; Squares: DEGs; Circles: DAMs.

Previous studies have shown that UGTs play crucial biological roles in phytohormone homeostasis, detoxification, secondary metabolism, and stress responses (Ren et al., 2025). As shown in Figure 8C, Cluster-69292 and Cluster-71935 exhibited strong positive correlations with TFs and metabolites. Notably, Cluster-71935 showed the highest expression level among all differentially expressed UGTs. To investigate its potential biological function, we performed subcellular localization analysis for this gene. The amino acid sequences of Cluster-69292 and Cluster-71935 were analyzed using the online prediction tool TargetP-2.0, which indicated cytoplasmic localization for both proteins. This prediction was consistent with our experimental observations, as shown in Supplementary Figure S10, strong GFP fluorescence signals for 35S-69292-EGFP and 35S-71935-EGFP were predominantly detected in the cytoplasm.

Figure 8.

Diagram illustrating a cluster dendrogram (A), a correlation heatmap (B), and a network interaction model (C). The dendrogram shows hierarchical clustering with module colors at the base. The heatmap visualizes correlations of module colors with metabolites, using a gradient from blue to red. The network diagram depicts interactions among structure genes, transcription factors (TFs), and metabolites, represented by triangles, squares, and circles, respectively. The legend identifies each symbol type.

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Gene co-expression network analysis. (A) Cluster dendrogram of genes grouped into 20 distinct co-expression modules, each designated by a unique color. (B) Correlation heatmap between module eigengenes and flavonoid metabolite levels. Red shades: positive correlations; Blue shades: negative correlations. (C) Relevance network diagram. Yellow triangles: structural genes. Orange squares: transcription factors. Blue circles: metabolites. Node size reflects the connectivity degree (number of significant correlations per node).

3.6. Gene co-expression network analysis

Through WGCNA, 20 distinct modules exhibiting similar gene expression patterns were identified (Figure 8A). The number of genes per module ranged from 86 to 14,984, with four modules (blue, brown, turquoise, and yellow) all containing over 1,000 genes (Figure 8B). PCA between module eigengenes and the content of eight key flavonoids (Eriodictyol-7-O-glucoside, 6-C-Methylquercetin-3-O-glucoside, Amoenin, Vitexin-2’’-O-rhamnoside, Apigenin-7-O-Gentiobioside, Luteolin-7-O-gentiobioside, Patuletin-3-O-glucoside, and Apigenin-7-O-glucuronide, the top three significantly accumulated differential metabolites in each tissue) revealed that two modules (blue and pink) were positively correlated with flavonoid content, while two others (turquoise and black) were negatively correlated. This indicates that the eigengenes of these four modules are closely associated with flavonoid accumulation in the roots, stems, and leaves of M. nanmu. Therefore, the blue, pink, turquoise, and black modules were selected for further investigation. Previous studies have shown that MYB and bHLH TFs are involved in the regulation of flavonoid biosynthesis (Fang et al., 2022; Su et al., 2025). Accordingly, we performed correlation analysis on structural genes positively correlated with metabolite levels and MYB/bHLH TFs within these four modules, identifying 13 structural genes, 3 MYB-related genes, and 3 bHLH TFs (Supplementary Table S8). As shown in Figure 8C, two MYB (Cluster-80252, Cluster-89108) and two bHLH (Cluster-92801, Cluster-100521) genes showed strong positive correlations (r > 0.8, p < 0.05) with key structural genes, including 4CL (Cluster-85842, Cluster-61524), CHS (Cluster-63826), F3H (Cluster-82106), I2’H (Cluster-41824), LAR (Cluster-97008), PGT1 (Cluster-69292, Cluster-71935), and BZ1 (Cluster-66706). These results suggest that these TFs act as core regulators coordinating flavonoid biosynthesis.

3.7. Validation by qRT-PCR

To validate the accuracy of the transcriptomic data, twelve differentially expressed genes were selected and analyzed using qRT-PCR. The results demonstrated that the expression trends observed by qRT-PCR were largely consistent with those from the transcriptome sequencing, confirming the reliability and validity of the transcriptomic data (Supplementary Figure S11).

4. Discussion

M. nanmu, an ecologically and economically important timber species, has long been recognized for its commercial wood value (Flora of China Editorial Committee, 2018). Previous studies have shown that M. nanmu possesses bioactive properties, including antioxidant, antitumor, and free radical scavenging activities, suggesting considerable potential for healthcare, nutraceutical, and pharmaceutical applications (Liang et al., 2023). And flavonoid secondary metabolites play a significant role in the growth and development of plants as well as in their resistance to both biotic and abiotic stresses (Zhang et al., 2017). However, the tissue-specific distribution and biosynthetic mechanisms of flavonoids and other secondary metabolites in M. nanmu remain largely uncharacterized. Here, we present the first comprehensive study integrating widely targeted metabolomics with transcriptomics to systematically investigate metabolite accumulation and gene expression patterns in roots, stems, and leaves, three distinct tissues of M. nanmu. The research results not only provide multi-omics evidence for a deeper understanding of the tissue-specific differentiation of M. nanmu’s secondary metabolism, but also offer theoretical support and key targets for the development of its medicinal value and genetic improvement.

4.1. Metabolite characteristics of different tissues in M. nanmu

Metabolomic profiling revealed the identification of a total of 1937 metabolites across three tissues of M. nanmu, which were categorized into 11 major classes, including flavonoids, phenolic acids and alkaloids. Among these, flavonoids (21.94%) and phenolic acids (21.27%) represented the two most abundant categories, a distribution pattern consistent with that observed in most plant species such as Taraxacum mongolicum and Lactuca indica L (Hao et al., 2023; Zhao et al., 2024). Both PCA and OPLS-DA models (with Q² > 0.9) verified a marked metabolic divergence among roots, stems and leaves, coupled with a high correlation between biological replicates. These results indicated the tissue-specificity of metabolite accumulation in M. nanmu. Tissue-specific metabolic differentiation was further validated by K-means clustering analysis, alkaloids and amino acids and derivatives were preferentially enriched in roots, phenolic acids accounted for a dominant proportion in stems, while flavonoids served as the core differential metabolites in leaves. These findings suggest that the metabolites may have different biological functions in different tissue types (Dong et al., 2014). As underground organs, roots can interact with soil microorganisms and various compounds. Alkaloids typically act as antimicrobial, insect-resistant, and allelopathic agents in plants (Ziegler and Facchini, 2008). Thus, the accumulation of alkaloids at high concentrations in roots may facilitate stress resistance and the regulation of rhizosphere microorganisms. Phenolic acids and their derivatives (such as lignin precursors) are closely associated with plant mechanical strength, vascular system development, and antioxidant protection during long-distance transport (Vanholme et al., 2010). The enrichment of these metabolites in stems is likely an adaptive trait that has evolved in response to the roles of the stems in nutrient translocation, mechanical support, and defense. The significantly enriched flavonoids in leaves align with the dual function of this organ, which is the primary site of photosynthesis and the first line of defense against environmental stresses (such as ultraviolet radiation, herbivores, and pathogens). Luteolin-7-O-glucoside (cynaroside), the most abundant flavonoid in leaves, serves as an ultraviolet screen, antioxidant, and antimicrobial compound (Agati et al., 2012).

As the most numerous classes of secondary metabolites in M. nanmu, flavonoids accounted for 425 identified compounds across roots, stems, and leaves. These metabolites were predominantly flavones and flavonols, followed by flavanones, flavanols, and chalcones (Supplementary Figure S3). The most abundant flavonoid in roots was 3’,4,4’,5,7-Pentahydroxyflavan (Luteoforol), while stems exhibited the highest content of epicatechin gallate, a catechin derivative known for its antitumor, anti-inflammatory, and antioxidant properties, including the inhibition of cancer cells (Li et al., 2022). In leaves, luteolin-7-O-glucoside (cynaroside) was the predominant flavonoid. This compound has been reported to possess antimicrobial, anticancer, antifungal, hepatoprotective, antidiabetic, antioxidant, and anti-inflammatory activities, and may also participate in drought stress responses in plants (Bouyahya et al., 2023; Rao et al., 2024). Pairwise comparisons between tissue groups revealed 218 differentially accumulated flavonoids (Table 1). These metabolites exhibited distinct tissue-specific accumulation patterns, with the highest overall flavonoid content observed in leaves, followed by stems, and the lowest in roots (Supplementary Figure S5). This suggests that leaves are the primary site of flavonoid biosynthesis and accumulation in M. nanmu, a pattern consistent with findings in Areca catechu, Artemisia argyi, and Ginkgo biloba (Fu et al., 2022; Miao et al., 2022; Lai et al., 2023). In contrast, other species, such as Hibiscus Manihot, accumulate the highest flavonoid levels in flowers (Zhou et al., 2022), indicating species-specific metabolic allocation. It should be noted that the present study focused on roots, stems and leaves, future work incorporating floral and fruit tissues will provide a more comprehensive understanding of flavonoid accumulation patterns in M. nanmu.

4.2. Enzymes and key structural genes related to flavonoid biosynthesis

By integrating transcriptomic and metabolomic analyses, we identified 19 key enzymes (including PAL, 4CL, CHI, UGTs, etc.) encoded by 41 genes, and reconstructed the flavonoid biosynthetic pathway in M. nanmu (Figure 7). Among these, the CHS encoding gene Cluster-71750 was significantly up-regulated in leaves, consistent with the high accumulation of flavonoids in this tissue. This aligns with the established role of CHS as a rate-limiting enzyme in flavonoid biosynthesis (Dixon and Paiva, 1995), and corroborates findings in Citrus species (Wang et al., 2018; Borredá et al., 2022), indicating the conservation of this regulatory mechanism in plants. In future studies, molecular approaches such as gene editing could be employed to modulate the expression of this gene, to enhance total flavonoid content in leaves and provide a key target for breeding high-flavonoid M. nanmu varieties. Notably, UGTs constituted the largest group among the differentially expressed structural genes. Their encoded products catalyze glycosylation, which enhances the solubility and stability of flavonoids (Le Roy et al., 2016), underscoring glycosylation as a key biochemical mechanism underlying flavonoid structural diversity in plants (Heiling et al., 2021). This observation is consistent with the expression pattern of flavonoids in M. nanmu. Among the top 20 flavonoids with the highest fold changes in the comparative analysis of roots, stems and leaves, the majority were glycosylated flavonoids (such as O-glycosides) exhibiting substantial fold differences. These results indicate that glycosylation modification may serve as a key biochemical switch governing the tissue-specific accumulation, stability and biological activity of flavonoids in M. nanmu (Bowles et al., 2005). WGCNA further revealed a strong correlation between two specific UGT genes (Cluster-69292 and Cluster-71935), flavonoid accumulation and core TFs. Subsequent subcellular localization assays confirmed that these two key UGT proteins were localized to the cytoplasm (Supplementary Figure S10). This subcellular localization is of great significance, as it suggests that the glycosylation of flavonoid aglycones occurs in the cytoplasm; the glycosylated products may then be transported to vacuoles for storage or secreted into the extracellular space. This process not only prevents the potential cytotoxicity of aglycones to plant cells, but also enables their specific distribution and functional differentiation across distinct cellular compartments or tissues (Zhao, 2015). Collectively, these findings demonstrate that UGT-mediated glycosylation acts not only as a major driver of flavonoid structural diversification, but also as a core regulatory node underlying the tissue-specific accumulation and functional specialization of flavonoids, which is consistent with the findings of previous studies. Previous studies have demonstrated that UGTs are generally considered soluble cytosolic enzymes, as they lack clear transmembrane domains or membrane-targeting signals and often exhibit activity in the cytoplasmic compartment (Jones and Vogt, 2001). However, some UGTs have been localized to the endoplasmic reticulum lumen or vacuoles (Ullmann et al., 1993; D’Alessio et al., 2010), possibly to facilitate glycosylation of specific substrates (Offen et al., 2006). The substrate specificity and catalytic properties of UGT genes, as well as the biological functions of their flavonoid glycoside products, need to be further elucidated through in vitro enzyme activity assays, gene knockout/overexpression experiments and other related approaches.

4.3. TFs and candidate genes involved in the biosynthesis of flavonoids

TFs are DNA-binding proteins that can interact with promoter regions of target genes and other protein domains to execute specific regulatory functions. They serve as critical regulators influencing growth, development, physiological processes, and secondary metabolism in higher plants (Zhang et al., 2022). WGCNA has been widely adopted as a robust approach for identifying candidate genes and transcriptional regulators from transcriptomic datasets (Pei et al., 2017). WGCNA, in our study, was employed to delineate co-expression modules correlated with flavonoid accumulation, leading to the identification of four key modules (blue, pink, turquoise, and black) and four core TFs (two MYB and two bHLH factors) putatively involved in the regulation of flavonoid biosynthesis (Figure 8C). Previous studies have established the central role of specific TFs in flavonoid pathway regulation. For instance, SbMYB3 in Scutellaria baicalensis directly binds to and activates the promoter of SbFNSII-2, promoting root-specific accumulation of flavones such as baicalein and wogonin (Fang et al., 2023). Similarly, SlbHLH95 in tomato acts as a bifunctional regulator, directly binding the promoters of SlF3H and SlFLS, interacting with SlMYB12 to co-regulate their expression, and enhancing resistance to Botrytis cinerea by repressing SlBG10 (Su et al., 2025). Consistent with the findings of the present study, MYB and bHLH FTs in M. nanmu exhibited a strong co-expression correlation with both upstream phenylpropanoid pathway genes (such as 4CL, CHS) and downstream modification genes (such as UGT, CHS, F3H, I2’H, and LAR), which were directly linked to the flavonoid glycosides highly accumulated in various tissues. These results revealed the potential regulatory pathway underlying tissue-specific flavonoid biosynthesis in M. nanmu, the activation of MYB and bHLH TFs upregulates a series of structural genes ranging from the synthesis of universal precursors to glycosylation modification, ultimately modulating the efficient biosynthesis and accumulation of flavonoid glycosides in specific tissues (Xu et al., 2015). However, the molecular characteristics of the transporters and the regulatory mechanisms governing how flavonoid glycosides are translocated from their sites of synthesis to storage compartments or transported long-distance between organs remain largely unknown. It should be emphasized that the TFs identified in this study were primarily screened through computational and statistical approaches. Further experimental validation can use molecular techniques, such as yeast one-hybrid assays to confirm DNA-binding activity and yeast two-hybrid systems to examine protein-protein interactions, which will be essential to verify their regulatory functions and physical interactions with target gene promoters. Interestingly, no WD40 component of the canonical MYB-bHLH-WD40 (MBW) complex was detected among the core TFs identified here. This contrasts with the well-established MBW-dependent regulatory model in Arabidopsis thaliana (Xie et al., 2016) and suggests that flavonoid regulation in M. nanmu may involve alternative mechanisms, potentially related to its specific morphogenesis, stress adaptation, hormone signaling, or metabolic regulation. This hypothesis warrants further investigation through physiological and stress-response experiments.

5. Conclusion

M. nanmu is rich in diverse bioactive metabolites and exhibits notable biological activities such as antioxidant and antitumor effects. This study provides a systematic elucidation of the metabolic and transcriptional basis underlying tissue-specific flavonoid biosynthesis in M. nanmu. Integrated metabolomic and transcriptomic profiling of roots, stems, and leaves identified 218 differentially accumulated flavonoids and 35,671 DEGs. Reconstruction of the flavonoid biosynthetic pathway revealed key structural genes, with UGTs representing the most abundant family. Subcellular localization in tobacco mesophyll protoplasts demonstrated cytoplasmic localization of two candidate proteins (Cluster-69292 and Cluster-71935). WGCNA further identified four core TFs (MYB and bHLH) as putative regulators of flavonoid biosynthesis. Finally, the reliability of the transcriptomic data was confirmed by qRT-PCR validation. These findings offer the first multi-omics insight into the regulatory mechanisms of flavonoid biosynthesis in M. nanmu, establishing a theoretical foundation for functional gene characterization and molecular breeding aimed at trait improvement.

Funding Statement

The author(s) declared that financial support was received for this work and/or its publication. The metabolomics and transcriptomics sequencing in this study were funded by the Central Forestry Reform and Development Fund - Representative National Key Protected Wild Plants Rescue and Protection in the Upper Reaches of the Yangtze River (zlg2021- cq20211210), and the Central Forestry Reform and Development Fund - Chengdu-Chongqing economic circle Joint Protection National Key Protected Wild Plants Rescue and Protection (zlg2022-cq20220907).

Footnotes

Edited by: Weiwei Zhang, Yangtze University, China

Reviewed by: Yinglang Wan, Hainan University, China

Lu Chen, Jiangxi University of Traditional Chinese Medicine, China

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

XZ: Validation, Visualization, Writing – original draft, Writing – review & editing. CX: Methodology, Software, Visualization, Writing – review & editing. HZ: Methodology, Software, Visualization, Writing – review & editing. WL: Methodology, Software, Visualization, Writing – review & editing. ZZ: Methodology, Software, Visualization, Writing – review & editing. NL: Data curation, Investigation, Writing – review & editing. RY: Data curation, Investigation, Writing – review & editing. JL: Data curation, Investigation, Writing – review & editing. HD: Conceptualization, Writing – review & editing.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not 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.2025.1731446/full#supplementary-material

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Associated Data

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

Supplementary Materials

DataSheet1.docx (4.4MB, docx)

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.

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