Integrated transcriptome and metabolome analysis reveals the accumulation of secondary metabolites in Camphora glanduliferum ‘Honganzhang’ at different harvest times

Integrated transcriptome and metabolome analysis reveals the accumulation of secondary metabolites in Camphora glanduliferum ‘Honganzhang’ at different harvest times | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated transcriptome and metabolome analysis reveals the accumulation of secondary metabolites in Camphora glanduliferum ‘Honganzhang’ at different harvest times Yang Yang, Qiuting Xiang, Yanling Zeng, Yinan Yang, Jianan Li, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8200539/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Camphora glanduliferum ‘Honganzhang’ is an economically important woody plant valued for its eucalyptol-rich essential oils. The yield and composition of its secondary metabolites are highly dynamic and sensitive to harvest timing. However, the molecular mechanisms governing the seasonal reprogramming of terpenoid and flavonoid biosynthesis in this species remain largely unexplored. To elucidate these regulatory networks, we performed an integrated metabolomic and transcriptomic analysis of leaves collected at three distinct harvest times: June (H1), August (H2), and October (H3). Results Metabolomic profiling identified 2,704 metabolites, with terpenoids (33.17%) and flavonoids (31.52%) being the predominant classes. A clear temporal divergence was observed: total terpenoid accumulation peaked in H1 and declined in H3, whereas flavonoid content increased significantly from H1 to H2. Transcriptomic analysis identified 9,824 − 13,513 differentially expressed genes (DEGs) across comparisons. Notably, we observed a transcript-metabolite discordance in terpenoid biosynthesis; key genes (e.g., DXS , GPPS , TPS04 ) were upregulated in H2 and H3 despite lower total terpenoid accumulation, suggesting a compensatory mechanism driven by high volatilization or metabolic flux diversion towards gibberellin biosynthesis. Conversely, flavonoid biosynthesis exhibited a strictly coordinated stage-specific flux redirection: H1 and H3 favored flavonol accumulation via the upregulation of FLS and CYP75B1 , while H2 prioritized proanthocyanidin synthesis through the specific upregulation of LAR . Furthermore, a co-expression regulatory network was constructed to link key metabolites and structural genes to transcription factors from the MYB, bHLH, NAC, and WRKY families, highlighting their roles as central hubs in orchestrating these metabolic shifts. Conclusions This study reveals the dynamic transcriptional reprogramming that drives the seasonal accumulation of secondary metabolites in C. glanduliferum ‘Honganzhang’. It investigates the specific regulatory modules controlling the trade-off between flavonol and proanthocyanidin pathways and proposes a mechanism for the seasonal fluctuation of terpenoids. These findings provide a theoretical foundation for determining optimal harvest windows to maximize specific bioactive compounds and facilitate the molecular breeding of elite Lauraceae varieties. Camphora glanduliferum ‘Honganzhang’ Secondary metabolites Harvest time Transcriptome Metabolome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Plants of the Lauraceae family (e.g., Camphora , Cinnamomum , and Litsea ) are rich in a wide range of volatile and non-volatile secondary metabolites, primarily including terpenoids, phenylpropanoids, flavonoids and alkaloids, among others[ 1 ]. These compounds perform critical ecological functions in plant defense, allelopathy, and signal transduction[ 2 ]. Secondary metabolites in Lauraceae plants are mainly synthesized through three core pathways: the Terpenoid pathway [mevalonate pathway (MVA), 2-C-methyl-D-erythritol 4-phosphate pathway (MEP)], the phenylpropanoid pathway, and the flavonoid pathway branch. Each pathway involves specific enzyme families [e.g., terpene synthase (TPS), phenylalanine ammonia-lyases (PAL), and chalcone synthases (CHS)] that catalyze key reactions, while variations in their gene expression or enzyme activities directly affect metabolic fluxes[ 3 – 7 ]. Transcription factors (TFs) play pivotal roles in regulating plant secondary metabolism, including the MYB, bHLH, WRKY, and ERF families, which modulate metabolite accumulation by regulating the expression of structural genes[ 8 ]. TPS genes in Camphora officinarum exhibit distinct tissue specificity and circadian rhythmic expression [ 9 ]. In Cinnamomum cassia , genes involved in the phenylpropanoid pathway (e.g., PAL , C4H , and 4CL ) are significantly upregulated during bark maturation[ 10 ]. Therefore, elucidating the transcriptional regulatory patterns of key enzymes and transcriptional regulators is of great significance for achieving targeted regulation of secondary metabolism in Lauraceae plants and breeding of superior varieties. With the rapid advancement of omics technologies in life science research, metabolomics and transcriptomics have become powerful approaches for elucidating regulatory mechanisms of plant secondary metabolism. These integrated omics approaches uncover the synergistic relationships between structural genes and transcription factors through gene-metabolite correlation analysis and construction of transcriptional regulatory networks [ 11 , 12 ]. In studies on medicinal plants (e.g., Salvia, Mentha, and Ocimum), multi-omics approaches have revealed the activation patterns of metabolic pathways across different developmental stages, tissues, and phenotypes, and identified key regulatory genes governing the biosynthesis of various secondary metabolites[ 13 – 15 ]. In Lauraceae plants, Peng et al. used integrated omics to characterize the dynamic flavonoid biosynthesis and regulatory mechanisms in developing leaves of C. officinarum , identifying CHS , DFR , and FLS as key genes influencing flavonoid metabolism and leaf color variation[ 16 ]. Similarly, Zhao et al. found significant differences in secondary metabolite accumulation among Camphora longepaniculata varieties, primarily involving terpenoids and flavonoids; 23 differentially expressed genes were related to secondary metabolism, with 12 participating in terpenoid and flavonoid biosynthetic pathways[ 17 ]. However, compared with herbaceous plants, multi-omics studies on perennial woody plants remain relatively limited, primarily constrained by their complex genomes, long life cycles, and pronounced seasonal variations[ 18 – 20 ]. Camphora glanduliferum (syn. Cinnamomum glanduliferum ), native to southwestern China at altitudes of 1500–2500 m, is an economically important tree species valued for both timber and aromatic oil production. Camphora glanduliferum ‘Honganzhang’ is an elite variety selected from natural variants of C. glanduliferum , characterized by high essential oil yield and abundant eucalyptol content. Its essential oil contains 46.6% eucalyptol and is also rich in terpenoid compounds such as α-terpineol, sabinene, and β-pinene, and exhibits notable antibacterial activity[ 21 ]. Compared with other Camphora species, studies on C. glanduliferum ‘Honganzhang’ have mainly focused on essential oil composition and antibacterial properties, whereas the biosynthesis and molecular regulation of its secondary metabolites remain largely unexplored. Moreover, there is a growing increasing demand for eucalyptol-rich essential oils in pharmaceuticals, aromatherapy, and eco-friendly pesticides, yet the seasonal regulatory mechanisms underlying eucalyptol biosynthesis in Lauraceae species remain unclear[ 22 – 25 ]. The yield and composition of secondary metabolites in woody aromatic plants are highly dynamic, collectively influenced by developmental stage, genotype, environmental conditions, and harvest time[ 26 ]. Among these factors, the harvest period plays a decisive role in essential oil quality; inappropriate timing can lead to reduced yield, decreased proportions of key constituents, and diminished bioactivity[ 10 ]. Therefore, elucidating the effects of harvest timing on metabolite accumulation is crucial for optimizing product quality and improving economic returns. This study represents the first comprehensive investigation to characterize the dynamic reprogramming of terpenoid and flavonoid metabolic fluxes across seasonal harvest times in C. glanduliferum ‘Honganzhang’ using an integrated transcriptomic and metabolomic framework. Leaf samples collected at three distinct harvest periods were analyzed to elucidate temporal changes in major secondary metabolites, identify key genes and transcription factors involved in metabolic regulation, and construct gene–metabolite–TF association networks. Our findings fill a critical knowledge gap in the metabolic regulatory mechanisms of C. glanduliferum ‘Honganzhang’, advance the understanding of temporal regulation of secondary metabolism in Lauraceae species, and provide a theoretical foundation for improving essential oil quality and supporting molecular breeding of elite varieties. In this study, leaves of C. glanduliferum ‘Honganzhang’ collected at three different harvest periods were analyzed using integrated transcriptomic and metabolomic approaches. The aim was to reveal dynamic changes in major secondary metabolites across harvest times, identify key genes and transcription factors involved in metabolic regulation, and construct gene–metabolite association networks. This work not only fills the research gap in metabolic regulation of C. glanduliferum ‘Honganzhang’ and enriches the understanding of temporal regulation of secondary metabolism in Lauraceae species, but also providing a theoretical basis for essential oil quality improvement and molecular breeding of elite varieties. Methods Plant material Fresh leaves of C. glanduliferum ‘Honganzhang’ were collected from 4–5-year-old trees cultivated under standard field management conditions in the Hunan Botanical Garden, Yuhua District, Changsha, Hunan Province, China (N 28°6′26′′, E 113°1′51′′). Sampling was performed at 9:00 a.m. on clear days on June 30 (H1), August 30 (H2), and October 30 (H3), 2024. Functionally mature, healthy leaves without any signs of pest infestation or disease were selected from the middle canopy, while newly emerged leaves were avoided. For each sampling event, ten leaves were randomly collected and pooled to constitute one biological replicate, resulting in a total of nine biological replicates. Among them, six replicates were used for metabolomic analysis and three for transcriptomic analysis. Immediately after collection, the leaves were flash-frozen in liquid nitrogen for 10 minutes and subsequently stored at − 80°C until further use. Sample preparation and extraction 100 mg solid sample was added to a 2 mL centrifuge tube and a 6 mm diameter grinding bead was added. 800 µL of extraction solution (methanol: water = 4:1, v/v) containing four internal standards (0.02 mg/mL L-2-chlorophenylalanine, etc.) were used for metabolite extraction. Samples were ground by the Wonbio-96c (Shanghai wanbo biotechnology co., LTD) frozen tissue grinder for 6 min (-10°C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5°C, 40 kHz). The samples were left at-20°C for 30 min, centrifuged for 15 min (4°C, 13000 g), and the supernatant was transferred to the injection vial for LC-MS/MS analysis. As a part of the system conditioning and quality control process, a pooled quality control sample (QC) was prepared by mixing equal volumes of all samples. The QC samples were disposed and tested in the same manner as the analytic samples. It helped to represent the whole sample set, which would be injected at regular intervals (every 6 samples) in order to monitor the stability of the analysis[ 27 ]. UPLC-MS/MS analysis The ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis of sample was conducted on a Thermo UHPLC-Q Exactive system equipped with an ACQUITY HSS T3 column (100 mm × 2.1 mm i.d., 1.8 µm; Waters, USA). The mobile phases consisted of 0.1% formic acid in water: acetonitrile (2:98, v/v) (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The gradient conditions are as follows: 0-0.5 min, mobile phase B was maintained at 2%; 0.5–7.5 min, mobile phase B was increased from 2% to 35%; 7.5–13 min, mobile phase B was increased from 35% to 95%; 13-14.4 min, mobile phase B was maintained at 95%; 14.4–14.5 min, mobile phase B was decreased from 95% to 2%; 14.5–16 min, mobile phase B was maintained at 2%. The flow rate was 0.40 mL/min and the column temperature was 40°C. The UPLC system was coupled to a Thermo UHPLC-Q Exactive Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in positive mode and negative mode. The optimal conditions were set as followed: source temperature at 400 o C; sheath gas flow rate at 40 arb; Aux gas flow rate at 10 arb; ion-spray voltage floating (ISVF) at-2800 V in negative mode and 3500 V in positive mode, respectively; Normalized collision energy, 20-40-60 V rolling for MS/MS. Full MS resolution was 70000, and MS/MS resolution was 17500. Data acquisition was performed with the Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70-1050 m/z. Raw LC/MS data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included: sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix, deredundant and peak pooled. At the same time, the metabolites were identified by searching Self-built plant-specific metabolite database (MJDBPM)[ 28 ]. Metabolome data analysis The data were analyzed using the free online platform of Majorbio Cloud Platform (cloud.majorbio.com). After obtaining the analyzable data matrix, the R package "ropls" (Version 1.6.2) was used to perform principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Based on the variable importance in projection (VIP) values derived from the PLS-DA model and the p-values from Student’s t-test, metabolites with VIP > 1, p-value < 0.05, and |log 2 FC| ≥ 1 were identified as significantly differential metabolites. These differential metabolites were annotated for metabolic pathways using the KEGG database. RNA extraction, library construction and sequencing Total RNA was extracted from the leaves of C. glanduliferum ‘Honganzhang’ using the QIAzol Lysis Reagent Kit (Qiagen, Beijing, China). The integrity and purity of the extracted RNA were determined using a NanoDrop2000 spectrophotometer (Thermo Scientific, Waltham, USA) and an Agilent5300 Bioanalyzer (Agilent Technologies, California, USA), respectively. cDNA libraries were constructed from the mRNA of each sample using the Illumina® Stranded mRNA Prep, Ligation (San Diego, CA) for subsequent sequencing. Raw data were generated via bridge PCR amplification and sequencing using the NovaSeq X Plus platform. Subsequently, quality control (QC) filtering was performed on the raw data: adaptors and insert-free sequences were removed; low-quality bases at the ends were trimmed, and reads with excessively low overall quality, high N content, or insufficient length were discarded. After quality assessment, clean data were obtained. Finally, de novo assembly of the clean data was conducted using Trinity software, followed by optimization and filtering with TransRate and CD-HIT. The quality of the final assembly was evaluated via BUSCO. Gene function annotation and expression analysis All transcripts generated from transcriptome sequencing were aligned against six major databases (NR, Swiss-Prot, Pfam, COG, GO, and KEGG), and annotation information in each database was obtained with the annotation status statistically summarized. The RSEM software was used to quantitatively analyze the expression levels of genes and transcripts separately, facilitating subsequent analysis of differential expression of genes/transcripts among different samples and enabling the revelation of gene regulatory mechanisms by integrating sequence functional information. Following the acquisition of gene ReadCounts, differential expression analysis of genes across samples was performed to identify differentially expressed genes (DEGs), which further allowed the investigation of DEG functions. The DESeq2 software was employed for differential expression analysis, with the default screening criteria for significantly differentially expressed genes set as follows: FDR < 0.05 and |log 2 FC| ≥ 1. Annotation analysis of DEGs was conducted using the GO and KEGG databases. GO enrichment analysis and KEGG enrichment analysis were performed via Goatools and Python scipy software, respectively[ 29 ]. Statistical analyses Significance was tested by ANOVA and multiple comparisons were performed using IBM SPSS Statistics 24.0 software. GraphPad prism 5.0 software was used to create figures. Values marked with different lowercase letters are significantly different (p < 0.05). Results Metabolomic Analysis of C. glanduliferum ‘Honganzhang’ leaves at different harvesting times To investigate the metabolomic differences among C. glanduliferum ‘Honganzhang’ leaves collected at different harvest periods, UPLC-MS/MS was employed. Both positive and negative electrospray ionization (ESI) modes were used to ensure a comprehensive detection of metabolites across harvest times. The total ion chromatograms (TIC) exhibited stable signal responses and distinct separation among the different samples (Fig. 1 A–C), indicating the reliability of the analytical system and suitability of the data for subsequent analyses. After signal-to-noise ratio filtering and database matching, a total of 2,704 metabolites were identified, including 1,453 secondary metabolites, 669 primary metabolites, and 582 compounds classified as other metabolite types. Principal component analysis (PCA) was employed to classify correlated variables, reduce data dimensionality, and visualize metabolic variations both among and within groups. As shown in Fig. 1 D, samples within the same group clustered closely together, indicating high reproducibility among biological replicates, whereas samples from different groups were clearly separated, suggesting substantial metabolic differences among the groups. The first principal component (PC1) accounted for 35.60% of the total variance, while the second principal component (PC2) explained 28.90%, demonstrating pronounced metabolite differences between groups. These results collectively confirm the stability and reliability of the metabolomic dataset. The identified secondary metabolites were annotated to facilitate their classification and functional characterization. These metabolites were categorized into twelve major classes (Fig. 1 E). Among them, terpenoids represented the most abundant group, comprising 482 compounds (33.17%), followed by flavonoids with 458 compounds (31.52%), and steroids and steroid derivatives with 157 compounds (10.81%). Other categories included phenolic acids and derivatives (96, 6.61%), lignans and derivatives (75, 5.16%), coumarins and derivatives (55, 3.79%), organic acids and derivatives (45, 3.10%), alkaloids and derivatives (32, 2.20%), stilbenes (18, 1.24%), tannins (13, 0.89%), quinones (12, 0.83%), and indoles and derivatives (10, 0.69%).These results substantially advance the understanding of the metabolomic landscape of C. glanduliferum ‘Honganzhang’ and provide a scientific foundation for the rational utilization of this aromatic tree resource. To further investigate the differential metabolites among groups, partial least squares discriminant analysis (PLS-DA) was applied to the experimental data. As shown in Fig. 2 B, samples from the same harvest time exhibited tighter clustering and higher within-group homogeneity, while the separation between different harvest times became more distinct. The heatmap visualization of metabolite expression patterns and hierarchical clustering analysis (Fig. 2 A) revealed that secondary metabolites displayed stage-specific expression profiles across different groups. Among the identified secondary metabolites, differential metabolites (DAMs) were determined based on the criteria |log₂FC| ≥ 1 and p < 0.05. A substantial number of DAMs were identified across the comparison groups, with notable variations in the number and direction (up- or down-regulation) of changes. Specifically, in the comparison between H1 and H2, a total of 527 DAMs were identified, including 190 upregulated and 337 downregulated metabolites. Between H1 and H3, the number of DAMs increased to 544, with 185 upregulated and 359 downregulated metabolites, indicating a distinct trend in metabolic regulation. In contrast, the comparison between H2 and H3 revealed 448 DAMs, of which 217 were upregulated and 231 were downregulated. Furthermore, Venn diagram analysis (Fig. 2 C) showed that 71 differential metabolites (DAMs) were shared among the three comparison groups, suggesting the presence of a stable core metabolic network that is likely governed by conserved regulatory mechanisms. KEGG enrichment analysis was performed to investigate the classification and functional pathways of the differential metabolites. The results revealed that these metabolites were significantly enriched in pathways related to flavonoid biosynthesis, terpenoid biosynthesis, and alkaloid biosynthesis. Specifically, in the comparison between H1 and H2, the most significantly enriched pathways were Flavone and flavonol biosynthesis (map00944) and Biosynthesis of various plant secondary metabolites (map00999), while Isoquinoline alkaloid biosynthesis (map00950) and Diterpenoid biosynthesis (map00904) also showed varying degrees of enrichment (Fig. 2 E). In the H1 vs H3 comparison, Flavone and flavonol biosynthesis (map00944) and Biosynthesis of various plant secondary metabolites (map00999) were again prominently enriched, accompanied by additional enrichment in Isoquinoline alkaloid biosynthesis (map00950) and Sesquiterpenoid and triterpenoid biosynthesis (map00909) (Fig. 2 F). For the H2 vs H3 comparison, the most significantly enriched pathway was Isoflavonoid biosynthesis (map00943), while Flavonoid biosynthesis (map00941), Isoquinoline alkaloid biosynthesis (map00950), and Diterpenoid biosynthesis (map00904) were also notably enriched (Fig. 2 G). To further characterize the dynamic changes in secondary metabolism across different harvesting times, time-series analyses of differential metabolites were performed within the major enriched pathways, including flavonoid, terpenoid, and alkaloid biosynthesis. The results revealed that most flavonoid compounds (such as quercetin and kaempferol derivatives) showed an increasing trend from H1 to H2 and remained stable from H2 to H3, suggesting enhanced antioxidant activity in the later harvest times. The relative content of total terpenoids was highest in H1, remained relatively stable in H2, and declined markedly in H3. Several monoterpenoid compounds (such as valdiate and cinnzeylanol) showed an increasing trend from H1 to H2 and remained stable from H2 to H3. In contrast, alkaloid-related metabolites displayed inconsistent trends across harvest times, indicating stage-specific regulatory mechanisms. Notably, the content of eucalyptol—the principal component of C. glanduliferum ‘Honganzhang’—remained relatively stable across all harvesting periods, suggesting that its biosynthesis is maintained at a consistent level during seasonal progression. Overall, these findings indicate that secondary metabolism in C. glanduliferum ‘Honganzhang’ leaves is highly dynamic across harvesting periods, with pronounced changes in flavonoid and terpenoid pathways. Transcriptome analysis A total of 486,115,312 raw sequencing reads were generated from the transcriptome sequencing. After removing low-quality reads, 486,113,300 high-quality clean reads were retained, yielding a total data volume of 72.52 Gb. The average GC content was 45.61%, and the mean Q20 and Q30 values exceeded 99% and 97%, respectively, with an overall sequencing error rate below 0.1%. These metrics indicate that the sequencing data were of high quality and suitable for downstream assembly (Table 1 ). After read assembly, 112,791 unigenes were obtained, exhibiting good assembly quality. Most unigenes ranged from 200–500 bp (36%) and 501–1000 bp (29%), with an additional 13,567 unigenes between 1001–2000 bp and 7,918 unigenes longer than 2000 bp (Fig. 3 A), supporting their use in subsequent analyses. Principal component analysis (PCA) showed clear clustering of transcriptome samples within the same harvesting period and distinct separation among different periods, indicating good reproducibility and suitability of the dataset for further transcriptomic comparisons (Fig. 3 B). To elucidate transcriptomic differences across harvest times in C. glanduliferum ‘Honganzhang’, differentially expressed genes (DEGs) were identified for each pairwise comparison using the criteria p -value < 0.05 and |log 2 FC| ≥ 1. The number of DEGs varied substantially among comparisons: the H1 vs. H2 comparison yielded the fewest DEGs (9,824), including 4,850 upregulated and 4,974 downregulated genes; the H1 vs. H3 comparison identified 11,935 DEGs, of which 7,791 were upregulated and 4,144 were downregulated; and the H2 vs. H3 comparison produced the largest number of DEGs (13,513), with 7,991 upregulated and 5,522 downregulated genes (Fig. 3 C). A total of 1,058 DEGs were shared across all three comparisons (H1 vs. H2 vs. H3; Fig. 3 D). These results indicate pronounced transcriptional divergence among the different harvest times. Table 1 Quality control analysis of transcriptome sequencing data Sample Raw reads Raw bases Clean reads Clean bases Error rate (%) Q20(%) Q30(%) GC content (%) H1-1 59780710 8967106500 59780442 8938260469 0.0197 99.34 97.95 45.57 H1-2 51219664 7682949600 51219474 7658144542 0.0196 99.37 98.05 45.49 H1-3 48723322 7308498300 48723100 7289126109 0.0196 99.36 98.01 45.46 H2-1 61368276 9205241400 61368012 9181334857 0.0198 99.34 97.92 45.71 H2-2 53405572 8010835800 53405394 7988253507 0.0198 99.33 97.89 45.8 H2-3 48089916 7213487400 48089756 7196489833 0.0198 99.33 97.91 45.53 H3-1 59468042 8920206300 59467816 8888776348 0.0199 99.33 97.85 45.52 H3-2 52663376 7899506400 52663112 7876328195 0.0198 99.35 97.95 45.73 H3-3 51396434 7709465100 51396194 7686534712 0.0197 99.35 97.95 45.64 Average 45.61 Total 486115312 72917296800 486113300 72703248572 Differentially expressed genes (DEGs) analysis To elucidate the molecular and metabolic regulatory differences among the three harvest periods, KEGG pathway enrichment analysis and hierarchical clustering were performed on the DEGs. As shown in the KEGG bubble plots (Fig. 4 ), the H1 vs. H2 comparison group exhibited significant enrichment in pathways related to secondary metabolism and carbohydrate metabolism, such as phenylpropanoid biosynthesis, starch and sucrose metabolism, and flavonoid biosynthesis. These pathways showed low p-values, high gene counts, and relatively high rich factors, indicating that secondary metabolite biosynthesis and carbon metabolic processes were particularly active during this harvest time (Fig. 4 A). In the H1 vs. H3 comparison, significantly enriched pathways included protein processing in the endoplasmic reticulum, monoterpenoid biosynthesis, and phenylpropanoid biosynthesis, suggesting that protein homeostasis and secondary metabolic processes contribute to the molecular distinctions between these two harvesting periods (Fig. 4 B). For the H2 vs. H3 comparison, phenylpropanoid biosynthesis, plant hormone signal transduction, and the biosynthesis of various plant secondary metabolites were significantly enriched, underscoring the involvement of secondary metabolic pathways and hormone-mediated regulation in shaping the metabolic differences between H2 and H3 (Fig. 4 C). In addition, hierarchical clustering of DEGs (Fig. 4 D) revealed distinct expression patterns across the harvest periods, reflecting stage-specific transcriptional regulation. Collectively, these results demonstrate that the enriched DEGs across different harvest periods are predominantly associated with pathways involved in the biosynthesis of diverse classes of secondary metabolites. A total of 863 transcription factors (TFs) were identified from the assembled transcripts, which were assigned to multiple TF families. These included the MYB superfamily (154 members), AP2/ERF (93), C2C2 (70), NAC (69), bHLH (67), WRKY (49), GRAS (33), and bZIP (30), along with several additional, less abundant families (Fig. 4 E). Integrative analysis of metabolome and transcriptome of terpenoid biosynthesis In the terpenoid biosynthesis pathway (Fig. 5), a total of 42 DEGs were identified, including five 1-deoxy-D-xylulose-5-phosphate synthase ( DXS ) genes, one 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate reductase ( IspH ) gene, four isoprene synthase ( IspS ) genes, one acetoacetyl-CoA thiolase ( ACAT ) gene, one 3-hydroxy-3-methylglutaryl-CoA reductase ( HMGCR ) gene, two geranylgeranyl diphosphate synthase ( GPPS ) genes, one geranyl diphosphate synthase ( GPS ) gene, two farnesyl diphosphate synthase ( FDPS ) genes, one farnesol dehydrogenase ( FLDH ) gene, one α-farnesene synthase 1 ( AFS1 ) gene, three terpene synthase 04 ( TPS04 ) genes, one ent-copalyl diphosphate synthase ( CPS ) gene, one sandaracopimaradiene synthase ( SPS ) gene, one ent-kaurenoic acid oxidase ( KAO ) gene, one GA20-oxidase ( GA20ox ) gene, five GA2-oxidase ( GA2ox ) genes, two GA3-oxidase ( GA3ox ) genes, seven 10-hydroxygeraniol oxidoreductase ( 10HGO ) genes, and two α-terpene synthase ( αTPS ) genes. Isopentenyl pyrophosphate (IPP), the universal C5 precursor for the biosynthesis of all terpenoids, is produced via both the MVA and MEP pathways. The key gene HMGCR in the MVA pathway, together with the core genes DXS (2 unigenes) and IspH in the MEP pathway, exhibited significantly elevated transcript levels in harvest times H2 and H3 relative to H1. This suggests that, along with seasonal progression, the metabolic capacity to supply IPP and dimethylallyl diphosphate (DMAPP) for downstream terpenoid biosynthesis is markedly enhanced. Most genes encoding enzymes responsible for catalyzing the formation of GPP, FPP, and GGPP (e.g., GPPS , FDPS ) as well as multiple terpene synthase genes ( FLDH , TPS04 , αTPS , IspS , 10HGO ) showed either a continuously increasing transcription pattern from H1 to H3 or maintained high expression in H2 and H3. Similarly, genes involved in gibberellin (GA) biosynthesis, such as CPS , KAO , GA20ox , and GA3ox , were substantially upregulated in H2 and H3 compared with H1, whereas the GA-degrading gene GA2ox was highly expressed in H1 but suppressed in H2 and H3. This coordinated antagonistic regulation implies a rapid and pronounced accumulation of bioactive GA during the transition from H1 to H2 and H3. Overall, the majority of terpenoid-related genes displayed low transcript abundance in H1 but were significantly upregulated during H2 and H3, indicating that terpenoid biosynthesis exhibits temporal regulation and shows increased transcriptional activity at later harvest dates. Figure 5 Terpenoid biosynthetic pathway constructed based on differentially expressed genes (DEGs). The color scale from blue to red represents the expression levels from low to high (normalized FPKM). To elucidate the complex interplay between transcriptional regulation and metabolite accumulation within the terpenoid biosynthetic pathway, a co-expression regulatory network was constructed based on Pearson correlation analysis (|r| > 0.8, p < 0.05), incorporating 22 DEGs, 50 TFs, and 56 DAMs (Fig. 6 ). This network provides an intuitive visualization of the interaction architecture linking transcription factors, structural genes, and metabolites. Notably, the vast majority of TFs exhibited strong positive correlations with the core biosynthetic genes, including members of the MYB, WRKY, NAC, and bHLH families, which were tightly associated with several key genes such as CgTPS04 , CgGA20ox3 , and Cg10HGO5 . These associations suggest that these transcription factors are likely candidates to regulate terpenoid biosynthesis by influencing the transcriptional activity of essential structural genes. Moreover, the expression levels of these core genes were strongly positively correlated with the accumulation of downstream metabolites. For example, the gibberellin biosynthesis gene CgGA20ox3 showed a high correlation with the accumulation of Gibberellin A37 and Gibberellin A44, whereas the terpene synthase gene CgTPS04 was significantly associated with multiple terpenoid compounds, including cannabidiol and ganoderic acid W. Collectively, these findings indicate that multiple transcription factors act as upstream regulatory switches that positively activate a cascade of downstream crucial genes, ultimately driving the substantial accumulation of structurally diverse terpenoid metabolites. Integrative analysis of metabolome and transcriptome of flavonoid biosynthesis The expression profiles of key genes involved in flavonoid biosynthesis (Fig. 7 ) revealed a total of 43 differentially expressed genes (DEGs), including five phenylalanine ammonia lyase ( PAL ) genes, three 4-coumarate-CoA ligase ( 4CL ) genes, three chalcone synthase ( CHS ) genes, two chalcone isomerase ( CHI ) genes, two cinnamate 4-hydroxylase ( C4H ) genes, two flavanone 3-hydroxylase ( F3H ) genes, twelve hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase ( HCT ) genes, two cytochrome P450 monooxygenase 98A subfamily ( CYP98A ) genes, seven caffeic acid O-methyltransferase ( COMT ) genes, one cytochrome P450 monooxygenase 75B1 subfamily ( CYP75B1 ) gene, two flavonol synthase ( FLS ) genes, and two leucoanthocyanidin reductase ( LAR ) genes. The majority of transcripts encoding the initial enzymes of the phenylpropanoid pathway ( PAL , C4H , and 4CL ) exhibited a fluctuating expression pattern characterized by high abundance in H1 and H3 but markedly reduced levels in H2, suggesting a sufficient supply of upstream flavonoid precursors during H1 and H3, whereas this supply is suppressed in H2. Conversely, genes associated with the synthesis of the core flavonoid skeleton ( CHS and CHI ) displayed the opposite pattern, with significantly elevated expression in H2 and reduced levels in H1 and H3; meanwhile, the expression of the key flavanone hydroxylase gene F3H followed the same trend as the initial enzymes (higher expression in H1/H3 and lower in H2). These results indicate that biosynthetic activity for the core skeleton is strongest in H2, whereas the conversion of flavanones to dihydroflavonols is more active during H1 and H3. Regarding branch-pathway enzymes, the flavonol synthase gene FLS exhibited high expression in H1 and H3, promoting flavonol biosynthesis, while the LAR gene encoding leucoanthocyanidin reductase was upregulated specifically in H2, facilitating proanthocyanidin accumulation. Additionally, modification-associated enzyme genes ( HCT , CYP98A , CYP75B1 , and COMT ) were co-expressed with the initial enzymes (high in H1/H3 and low in H2), enhancing structural diversity of flavonols during H1 and H3. Collectively, these expression patterns reveal a stage-specific metabolic flux of flavonoid biosynthesis: the metabolic direction in H1 and H3 favors flavonol production (driven by active precursor supply, modification steps, and FLS ), whereas H2 preferentially promotes proanthocyanidin accumulation (supported by enhanced core skeleton formation and LAR activity). This provides the molecular basis for compositional differences in flavonoids across harvest times. Through the integrated co-expression network analysis of DEGs, TFs, and DAMs, a highly coordinated transcriptional regulatory system underlying flavonoid biosynthesis in C. glanduliferum ‘Honganzhang’ was revealed. A total of 26 structural genes, 72 flavonoid metabolites and 50 transcription factors formed a dense association network based on Pearson correlations (|r| > 0.8, p < 0.05) (Fig. 8 ). Several key structural genes, including CHS , CHI , F3H , FLS , LAR , and CYP75B1 , were identified as central hub genes with the highest connectivity. CHS showed strong positive correlations with major flavonol compounds such as quercetin-3-O-glucoside, quercetin-7-O-glucoside, kaempferol-3-O-glucoside and pinobanksin-5-O-glucoside, confirming its dominant role in directing metabolic flux toward flavonoid biosynthesis. Similarly, FLS and CYP75B1 exhibited significant correlations with kaempferol-3-O-rhamnoside, myricetin-3-O-rutinoside and quercetin derivatives, indicating their involvement in downstream flavonol diversification. In addition, LAR showed strong correlations with (+)-catechin and epicatechin, highlighting its role in proanthocyanidin biosynthesis. Moreover, transcription factors MYB, bHLH, WRKY and NAC displayed extensive co-expression relationships with these structural genes, forming regulatory modules such as CgMYB1 / CgMYB13 – CHS / FLS –quercetin/kaempferol and CgWRKY33–CYP75B1/LAR–catechin, suggesting that flavonoid accumulation is predominantly regulated at the transcriptional level. These results demonstrate that the enhanced expression of key biosynthetic genes and transcription factors contributes to the high accumulation of flavonoids observed at the H2 harvest times. Discussion Plants synthesize and accumulate a wide array of metabolites throughout their growth and development, including primary metabolites essential for basic physiological processes and secondary metabolites involved in defense, adaptation, and signal regulation. Systematic metabolomic profiling has estimated that terrestrial plants may produce more than 200,000 natural small-molecule compounds, a level of chemical diversity that far exceeds most other biological groups [ 30 , 31 ]. These metabolites not only sustain physiological homeostasis and ecological interactions in plants but also constitute critical resources for human foods, pharmaceuticals, and bioactive natural products, thereby holding substantial significance for agricultural improvement and human health [ 32 , 33 ]. From a biosynthetic and evolutionary perspective, plants have developed diverse secondary metabolic pathways through gene duplication, functional divergence, and recruitment of enzymatic systems. Convergent evolution frequently occurs across distinct lineages, enabling different species to generate structurally similar compounds in response to shared selective pressures such as herbivory and pathogen attack. The remodeling of gene–enzyme–metabolite networks is regarded as a major driving force underlying the diversification of secondary metabolism [ 34 ]. Lauraceae species are characterized by abundant secondary metabolites—including terpenoids, phenylpropanoids, and flavonoids—and within the genus Camphora , terpenoids (including monoterpenes, sesquiterpenes, and diterpenes) represent the predominant class. The major constituents of essential oils commonly include camphor, eucalyptol, citral, and linalool [ 35 ]. Numerous studies have demonstrated that Camphora plants can be classified into distinct chemotypes, such as camphor-type, cineole-type, and linalool-type. These chemotypes differ markedly not only in chemical composition but also in biological activities such as antimicrobial, anti-inflammatory, and antioxidant properties, resulting in substantial variation in the structure and activity of essential oils derived from different species or varieties within the genus [ 36 ]. Multi-omics approaches have played a pivotal role in elucidating the mechanisms underlying the accumulation of plant secondary metabolites and have become essential tools for investigating secondary metabolic regulation in Camphora species. In Paeonia lactiflora , Xu et al. integrated transcriptomic and metabolomic datasets and uncovered distinct tissue-specific accumulation patterns of monoterpene glycosides, gallaglycosides, and flavonoids across roots, leaves, and flowers. Through differential gene expression (DEGs) analysis, they further identified transcriptional regulatory networks associated with the biosynthesis of these metabolites[ 37 ]. In addition, Liu et al. employed an integrated omics strategy in Jinsi Huangju (Chrysanthemum), systematically characterizing the molecular mechanisms driving flavone biosynthesis and identifying several key genes involved in flavonoid formation[ 38 ]. Multi-omics integration has also been extensively used to explore the dynamic changes in volatile metabolites and carotenoids during developmental processes. For example, in different floral developmental stages of Camellia huana , Chen et al. combined transcriptomic and metabolomic analyses to investigate biosynthetic pathways of volatiles and carotenoids, revealing temporally and spatially coordinated patterns between gene expression and metabolite accumulation[ 39 ]. In this study, we applied an integrated transcriptomic and metabolomic framework to systematically elucidate the dynamic accumulation patterns of secondary metabolites and their molecular regulatory mechanisms in the leaves of C. glanduliferum ‘Honganzhang’ harvested at three harvest times (H1: June 30, H2: August 30, H3: October 30). Our results demonstrate pronounced temporal dynamics in secondary metabolism across harvest periods, particularly within terpenoid and flavonoid biosynthetic pathways. Such metabolic plasticity likely represents an adaptive response of woody aromatic plants to seasonal climatic changes and environmental fluctuations, and further highlights the decisive role of harvest timing in determining essential oil quality. Terpenoids constitute the principal components of the essential oil in C. glanduliferum ‘Honganzhang’ and represent the most abundant class of secondary metabolites in its leaves. Metabolomic profiling in this study revealed a distinct seasonal variation pattern: the relative content of total terpenoids was highest in the early harvest (H1, June), remained relatively stable in the mid-season (H2, August), and declined markedly in the late season (H3, October). Interestingly, despite the general decline in total accumulation towards the late season, specific monoterpenoid compounds—such as valdiate and cinnzeylanol—showed an increasing trend from H1 to H2 and remained stable from H2 to H3. These temporal patterns likely reflect metabolic adjustments aligned with seasonal physiological demands and environmental fluctuations across the sampling dates. Notably, an intriguing discrepancy was observed between transcriptional activity and metabolic accumulation. Although the transcript levels of key biosynthetic genes (e.g., DXS , GPPS , TPS04 ) were significantly upregulated in H2 and H3 compared to H1, the total terpenoid content in leaves did not increase proportionally; instead, it remained stable or declined. This non-linear correlation, often referred to as "transcript-metabolite discordance," implies that gene expression levels reflect the biosynthetic potential or flux rate rather than the final accumulated pool size [ 40 ]. In the context of C. glanduliferum ‘Honganzhang’, this phenomenon can be explained by three seasonal adaptation mechanisms. First, the dynamic equilibrium between volatilization and biosynthesis plays a critical role. The H2 sampling period (August) coincides with peak summer temperatures. High temperatures are known to exponentially increase the emission rates of volatile monoterpenes and sesquiterpenes from leaf glands. Therefore, the upregulated expression of terpenoid biosynthetic genes in H2 and H3 likely reflects a high metabolic flux—a compensatory mechanism to replenish the volatile pool depleted by heat-induced emission, rather than to increase the net storage [ 41 ]. This "high flux, low storage" state suggests the plant is actively maintaining metabolic homeostasis under environmental stress. Second, metabolic flux diversion may limit the accumulation of essential oil constituents. Terpenoid precursors, such as GGPP, serve as common substrates for both secondary metabolites and primary growth regulators like gibberellins (GAs). Our transcriptome data confirmed that GA biosynthetic genes ( CPS , KAO , GA20ox ) were significantly upregulated in H2 and H3. This suggests that metabolic flux may be competitively diverted towards hormone biosynthesis to regulate seasonal growth or stress adaptation, thereby reducing the carbon flow available for the accumulation of essential oil-specific terpenoids. Additionally, the coupling between hormone signaling and terpenoid metabolism provides further insight into this seasonal reprogramming. GA signaling has been shown to regulate cell elongation and differentiation and to modulate TPS expression or protein activity through interactions with secondary metabolism[ 43 ]. We propose that dynamic GA fluctuations from June to October may directly or indirectly influence terpenoid biosynthetic flux, mediating the shift from broad-spectrum terpenoid production in H1 to the selective retention of specific metabolites (e.g., valdiate and cinnzeylanol) in H2 and H3. Finally, feedback regulation loops and transcriptional regulatory networks orchestrate this dynamic. The peak accumulation of terpenoids observed in H1 (June) could trigger a negative feedback mechanism that suppresses the expression of upstream biosynthetic genes. Conversely, as the internal metabolite pool is depleted by volatilization or catabolism in H3 (October), this suppression is relieved, resulting in the observed upregulation of transcripts. Network-based transcriptional regulatory analysis further revealed that multiple TFs—including MYB, WRKY, NAC, and bHLH—showed strong positive correlations with key terpenoid structural genes. These TFs likely act as integrators of environmental signals (e.g., photoperiod and temperature), serving as upstream regulatory switches that fine-tune pathway-specific metabolic output [ 44 – 46 ]. Flavonoids from Camphora species exhibit substantial bioactivities, including antioxidant, anti-inflammatory, antimicrobial, and potential antidiabetic or metabolic regulatory effects. In vitro functional assays demonstrated that flavonoid mixtures extracted from C. camphora leaves exhibit notable activity in free-radical scavenging assays (DPPH, ABTS) and antibacterial tests. Moreover, several studies have reported inhibitory effects of these compounds on metabolic enzymes such as α-glucosidase, suggesting their therapeutic potential for metabolic disorders[ 47 ]. Flavonoids represent the second most abundant class of secondary metabolites in the leaves of C. glanduliferum ‘Honganzhang’. Temporal metabolic profiling revealed that the majority of flavonoids increased markedly from H1 to H2, followed by stabilization from H2 to H3. However, transcriptional regulation of the flavonoid biosynthetic pathway exhibited a temporally alternating pattern. During H1 and H3, metabolic flux was preferentially directed toward flavonol production. The upstream phenylpropanoid pathway genes ( PAL , C4H , 4CL ) were highly expressed at H1 and H3. Similarly, the flavonol-specific branching gene FLS , together with modification-related enzyme genes ( HCT , CYP98A , CYP75B1 , COMT ), exhibited elevated transcript abundance in these harvest times, suggesting enhanced biosynthesis and structural diversification of flavonols such as quercetin- and kaempferol-derived compounds. In contrast, H2 favored the preferential accumulation of proanthocyanidins. Core flavonoid scaffold genes ( CHS , CHI ) displayed peak expression at H2, and the LAR gene (leucoanthocyanidin reductase) was specifically upregulated, promoting accumulation of proanthocyanidins such as (+)-catechin and epicatechin. This alternation is consistent with prior observations in Cinnamomum cassia , where phenylpropanoid pathway genes are upregulated during late harvest times (120 MAP), promoting volatile formation such as cinnamaldehyde[ 10 ]. LAR , which catalyzes the conversion of leucocyanidin to (+)-catechin, has been widely recognized as a key enzyme controlling proanthocyanidin biosynthesis, showing strong correlation with catechin accumulation in Camellia sinensis , grapevine, and Lithocarpus polystachyus , among others[ 48 – 51 ]. Studies on flavonoid metabolism in C. camphora have revealed that major flavonols such as rutin accumulate predominantly in leaves, while the biosynthetic gene CcPAL_1 is predominantly expressed in roots, implying the involvement of metabolite translocation between synthesis and storage tissues [ 9 ]. Such stage-dependent metabolic switching reflects the dynamic balance between ultraviolet protection (flavonols) and structural defense (proanthocyanidins) required for adaptation to different seasonal stressors (e.g., high temperature in summer, lower temperature in autumn), resembling the mechanism observed in Cryptomeria fortunei , wherein flavonoids accumulate during winter to enhance cold resistance [ 52 ]. Similar to terpene biosynthesis, regulation of flavonoid pathways is controlled by transcriptional networks dominated by MYB, bHLH, WRKY, and NAC transcription factors. These TFs form tightly coordinated regulatory modules with key structural genes such as CHS , FLS , LAR , and CYP75B1 . The associations identified in this study for CgMYB1 , CgMYB13 , and related TFs with flavonoid biosynthetic genes are consistent with previous findings describing MYB- and bHLH-mediated regulation of flavonoid accumulation in C. camphora leaves. This study elucidated the dynamic accumulation patterns of secondary metabolites in C. glanduliferum ‘Honganzhang’ across different harvest periods through integrated multi-omics analysis and uncovered the associated gene regulatory networks. Nevertheless, several limitations remain. First, the analyses were performed at the whole-leaf level and therefore did not resolve the spatiotemporal metabolic profiles of specific tissues such as secretory glands; future investigations incorporating single-cell and spatial omics technologies are required to dissect tissue-specific regulatory mechanisms. Second, the functional roles of key structural genes and transcription factors were inferred primarily from correlative evidence, and their causal relationships must be validated through gene editing, enzymatic assays, and metabolic flux analyses. In addition, seasonal environmental variability may contribute to harvest-dependent differences; controlled-environment experiments with environmental-factor gradients are needed to decouple developmental effects from stress responses. Integrating glandular anatomical characterization with large-scale germplasm screening, GWAS/QTL mapping, and related approaches may ultimately provide precise molecular targets and a theoretical foundation for molecular breeding and industrial development of essential oils in Lauraceae species. Conclusions This study employed integrated transcriptomic and metabolomic analyses to systematically characterize the accumulation patterns of secondary metabolites and their underlying regulatory mechanisms across three harvest periods (H1, H2, H3) in leaves of C. glanduliferum ‘Honganzhang’. A total of 2,704 metabolites were identified, including 1,453 secondary metabolites predominantly belonging to terpenoids (33.17%) and flavonoids (31.52%). Secondary metabolism exhibited pronounced temporal dynamics, with major differentially accumulated metabolites significantly enriched in flavonoid, terpenoid, and alkaloid biosynthetic pathways. Total terpenoid abundance was highest in H1, remained stable in H2, and declined sharply in H3; however, several monoterpenoids retained high abundance from H2 to H3, and the principal essential-oil constituent, eucalyptol, showed no significant variation across harvest periods. Flavonoid levels increased from H1 to H2 and subsequently stabilized, indicating stage-specific metabolic prioritization. Transcriptome sequencing revealed extensive, harvest-dependent transcriptional reprogramming, with 9,824–13,513 differentially expressed genes (DEGs) identified across comparisons. KEGG enrichment analysis indicated that pathways related to phenylpropanoid biosynthesis, flavonoid biosynthesis, monoterpenoid biosynthesis, and hormone signaling were major contributors to the stage-specific metabolic differences. Integrated network analysis further demonstrated coordinated regulation among key structural genes (e.g., ACAT , DXS , GPS , FLS , LAR , GA20ox ), secondary metabolites, and transcription factors from the MYB, bHLH, NAC, and WRKY families, forming tightly connected co-expression modules that orchestrate terpenoid and flavonoid metabolic flux. Collectively, this study elucidates the molecular regulatory framework governing secondary-metabolite dynamics across harvest periods in C. glanduliferum ‘Honganzhang’. The findings provide practical guidance for optimizing harvest timing to maximize target compounds—particularly monoterpenoid-rich essential oils and bioactive flavonoids—and offer a molecular foundation for future metabolic engineering and breeding efforts aimed at improving essential-oil composition and product quality. Declarations Ethics approval and consent to participate The collection of plant materials and all experimental procedures complied with relevant institutional, national, and international guidelines and legislation. Plant materials of Camphora glanduliferum ‘Honganzhang’ were collected in Changsha, Hunan Province, China, in accordance with local regulations. Permission to collect the plant materials was obtained from the Administration of the Hunan Botanical Garden . Voucher specimens have been deposited in the Herbarium of the Institute of Resource Plants, Hunan Provincial Botanical Garden, Changsha, Hunan, China (Voucher number: HBG-CGH-20240530) . The specimens were identified by Professor Xinhai Peng, Hunan Botanical Garden. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Author details 1 College of Forestry, Central South University of Forestry & Technology, Changsha, 410004, China. 2 Hunan Botanical Garden, Changsha, 410116, China. 3 Golden Land Ecological Agriculture Co., Ltd., Hongjiang, 418200, China. Funding This work was supported by the Hunan Innovation Platform and Talent Project (Number: 2022NK4194). Author Contribution Y.Y. and X.Q. conceived and designed the study. Y.Y. and X.Q. performed the experiments and conducted the transcriptomic and metabolomic analyses. Z.Y. and Y.Y.N. assisted with sample collection and metabolite profiling. L.J. and P.J. supervised the project, acquired funding, and critically revised the manuscript. Y.Y. and X.Q. drafted the manuscript. All authors read and approved the final version of the manuscript. 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ACS Omega. 2022;7:19437–53. https://doi.org/10.1021/acsomega.2c01125 . Zhang Y, Yang L, Yang J, Hu H, Wei G, Cui J, et al. Transcriptome and metabolome analyses reveal differences in terpenoid and flavonoid biosynthesis in cryptomeria fortunei needles across different seasons. Front Plant Sci. 2022;13:862746. https://doi.org/10.3389/fpls.2022.862746 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Jan, 2026 Reviews received at journal 27 Jan, 2026 Reviewers agreed at journal 08 Jan, 2026 Reviewers agreed at journal 29 Dec, 2025 Reviewers invited by journal 26 Dec, 2025 Editor assigned by journal 25 Dec, 2025 Submission checks completed at journal 25 Dec, 2025 First submitted to journal 25 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8200539","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":565873430,"identity":"862a160c-4245-4853-a020-56f83937ea45","order_by":0,"name":"Yang Yang","email":"","orcid":"","institution":"Central South University of Forestry and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":565873431,"identity":"7bdad9e8-045a-442b-be45-e423bd7b0d48","order_by":1,"name":"Qiuting Xiang","email":"","orcid":"","institution":"Central South University of Forestry and 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13:01:06","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167204,"visible":true,"origin":"","legend":"","description":"","filename":"9cf9308a29f34a73b84eaa887305bb591structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/3608e12abe5ec9bb5a6b6084.xml"},{"id":99160917,"identity":"0a8449d1-fe11-45b3-91d0-6c374a2146d2","added_by":"auto","created_at":"2025-12-29 13:01:06","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186201,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/de8d75db91c911b28bbfe7cd.html"},{"id":99316866,"identity":"f3c99c8d-b58c-4950-86c3-e031cde5df1f","added_by":"auto","created_at":"2025-12-31 16:29:22","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":999089,"visible":true,"origin":"","legend":"\u003cp\u003eDetection and identification of specialized metabolites of \u003cem\u003eC. glanduliferum \u003c/em\u003e‘Honganzhang’ in different harvest times by UPLC-MS/MS. \u003cstrong\u003eA–C\u003c/strong\u003e Total ion chromatograms (TICs) of the H1, H2, and H3, respectively; \u003cstrong\u003eD\u003c/strong\u003e PCA score plot of the samples; \u003cstrong\u003eE \u003c/strong\u003eClassification of identified secondary metabolites.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/c9b5a08082032b062a78c0bf.jpeg"},{"id":99315561,"identity":"c3e9b563-b772-4078-bb5e-f3c6c6bfdc78","added_by":"auto","created_at":"2025-12-31 16:27:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1713359,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DAMs of \u003cem\u003eC. glanduliferum \u003c/em\u003e‘Honganzhang’ in different harvest times. \u003cstrong\u003eA\u003c/strong\u003e Metabolite clustering heatmap;\u003cstrong\u003e B\u003c/strong\u003e PLS-DA score plot of the samples; \u003cstrong\u003eC\u003c/strong\u003e Venn diagram of DAMs; \u003cstrong\u003eD\u003c/strong\u003e Number of DAMs in different comparisons; \u003cstrong\u003eE-G \u003c/strong\u003eKEGG multidimensional enrichment circos plots of DAMs in H1, H2, and H3, respectively. From outer to inner circles: (1) KEGG pathway classification and compound counts; (2) pathway enrichment significance indicated by bar length (compound number) and color gradient (-log10 P value); (3) proportion of up-regulated (red) and down-regulated (green or cyan) metabolites in each pathway; (4) Rich factor values showing the ratio of differential to background metabolites. Pathways are arranged clockwise according to enrichment values.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/a1d7bd7a327b0f2f51985ff1.jpeg"},{"id":99315889,"identity":"5b28c85d-f871-4667-9ede-cf13e3f13ff1","added_by":"auto","created_at":"2025-12-31 16:27:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":448096,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of DEGs of \u003cem\u003eC. glanduliferum \u003c/em\u003e‘Honganzhang’ in different harvest times. \u003cstrong\u003eA\u003c/strong\u003eDistribution of Unigenes length;\u003cstrong\u003e B\u003c/strong\u003e PCA of transcriptome data; \u003cstrong\u003eC\u003c/strong\u003eNumbers of DEGs in pairwise comparisons; \u003cstrong\u003eD\u003c/strong\u003e Venn diagrams of DEGs among four comparisons.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/0864b4e2f4a144d13cb86920.jpeg"},{"id":99160898,"identity":"46c013d3-c213-40ee-9e20-10ce00889964","added_by":"auto","created_at":"2025-12-29 13:01:04","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1181925,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of DEGs of \u003cem\u003eC. glanduliferum \u003c/em\u003e‘Honganzhang’ in different harvest times. \u003cstrong\u003eA-C\u003c/strong\u003eKEGG pathway enrichment bubble plots (H1 vs H2, H1 vs H3, H2 vs H3); \u003cstrong\u003eD\u003c/strong\u003e Differentially expressed genes clustering heatmap.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/52561fe1d8992492d5c6cd6e.jpeg"},{"id":99315758,"identity":"c60b88e5-9bf5-40c8-afe6-1ab0d70cc95f","added_by":"auto","created_at":"2025-12-31 16:27:19","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":920494,"visible":true,"origin":"","legend":"\u003cp\u003eTerpenoid biosynthetic pathway constructed based on differentially expressed genes (DEGs). The color scale from blue to red represents the expression levels from low to high (normalized FPKM).\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/eff768c50ffe321bc68cb933.jpeg"},{"id":99316225,"identity":"a726cced-6b58-4056-ade9-c2ed3d632bb4","added_by":"auto","created_at":"2025-12-31 16:27:55","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2273490,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression regulatory network integrating terpenoids DAMs, relevant TFs, and DEGs.\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/6639dde7cb61449ef32a38ed.jpeg"},{"id":99160916,"identity":"2348eb3d-8598-43ad-a5aa-171798c5581e","added_by":"auto","created_at":"2025-12-29 13:01:06","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1091994,"visible":true,"origin":"","legend":"\u003cp\u003eFlavonoid biosynthetic pathway constructed based on differentially expressed genes (DEGs). The color scale from blue to red represents the expression levels from low to high (normalized FPKM).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/dfb3b40b56d725cbf39aeca5.jpeg"},{"id":99316743,"identity":"fff291e7-4ee2-4b13-b543-20ae4e68403e","added_by":"auto","created_at":"2025-12-31 16:29:07","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2378923,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression regulatory network integrating flavonoids DAMs, relevant TFs, and DEGs.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/9b0afa406736a573af1d904e.jpeg"},{"id":99788289,"identity":"32d3fd45-b81f-42f0-838d-127e51a25740","added_by":"auto","created_at":"2026-01-08 12:46:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12073616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8200539/v1/41faed37-920a-4e4b-b115-24e235d71473.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated transcriptome and metabolome analysis reveals the accumulation of secondary metabolites in Camphora glanduliferum ‘Honganzhang’ at different harvest times","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlants of the Lauraceae family (e.g., \u003cem\u003eCamphora\u003c/em\u003e, \u003cem\u003eCinnamomum\u003c/em\u003e, and \u003cem\u003eLitsea\u003c/em\u003e) are rich in a wide range of volatile and non-volatile secondary metabolites, primarily including terpenoids, phenylpropanoids, flavonoids and alkaloids, among others[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These compounds perform critical ecological functions in plant defense, allelopathy, and signal transduction[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Secondary metabolites in Lauraceae plants are mainly synthesized through three core pathways: the Terpenoid pathway [mevalonate pathway (MVA), 2-C-methyl-D-erythritol 4-phosphate pathway (MEP)], the phenylpropanoid pathway, and the flavonoid pathway branch. Each pathway involves specific enzyme families [e.g., terpene synthase (TPS), phenylalanine ammonia-lyases (PAL), and chalcone synthases (CHS)] that catalyze key reactions, while variations in their gene expression or enzyme activities directly affect metabolic fluxes[\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Transcription factors (TFs) play pivotal roles in regulating plant secondary metabolism, including the MYB, bHLH, WRKY, and ERF families, which modulate metabolite accumulation by regulating the expression of structural genes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eTPS\u003c/em\u003e genes in \u003cem\u003eCamphora officinarum\u003c/em\u003e exhibit distinct tissue specificity and circadian rhythmic expression [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In \u003cem\u003eCinnamomum cassia\u003c/em\u003e, genes involved in the phenylpropanoid pathway (e.g., \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, and \u003cem\u003e4CL\u003c/em\u003e) are significantly upregulated during bark maturation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, elucidating the transcriptional regulatory patterns of key enzymes and transcriptional regulators is of great significance for achieving targeted regulation of secondary metabolism in Lauraceae plants and breeding of superior varieties.\u003c/p\u003e \u003cp\u003eWith the rapid advancement of omics technologies in life science research, metabolomics and transcriptomics have become powerful approaches for elucidating regulatory mechanisms of plant secondary metabolism. These integrated omics approaches uncover the synergistic relationships between structural genes and transcription factors through gene-metabolite correlation analysis and construction of transcriptional regulatory networks [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In studies on medicinal plants (e.g., Salvia, Mentha, and Ocimum), multi-omics approaches have revealed the activation patterns of metabolic pathways across different developmental stages, tissues, and phenotypes, and identified key regulatory genes governing the biosynthesis of various secondary metabolites[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In Lauraceae plants, Peng et al. used integrated omics to characterize the dynamic flavonoid biosynthesis and regulatory mechanisms in developing leaves of \u003cem\u003eC. officinarum\u003c/em\u003e, identifying \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eDFR\u003c/em\u003e, and \u003cem\u003eFLS\u003c/em\u003e as key genes influencing flavonoid metabolism and leaf color variation[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, Zhao et al. found significant differences in secondary metabolite accumulation among \u003cem\u003eCamphora longepaniculata\u003c/em\u003e varieties, primarily involving terpenoids and flavonoids; 23 differentially expressed genes were related to secondary metabolism, with 12 participating in terpenoid and flavonoid biosynthetic pathways[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, compared with herbaceous plants, multi-omics studies on perennial woody plants remain relatively limited, primarily constrained by their complex genomes, long life cycles, and pronounced seasonal variations[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eCamphora glanduliferum\u003c/em\u003e (syn. \u003cem\u003eCinnamomum glanduliferum\u003c/em\u003e), native to southwestern China at altitudes of 1500\u0026ndash;2500 m, is an economically important tree species valued for both timber and aromatic oil production. \u003cem\u003eCamphora glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; is an elite variety selected from natural variants of \u003cem\u003eC. glanduliferum\u003c/em\u003e, characterized by high essential oil yield and abundant eucalyptol content. Its essential oil contains 46.6% eucalyptol and is also rich in terpenoid compounds such as α-terpineol, sabinene, and β-pinene, and exhibits notable antibacterial activity[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Compared with other \u003cem\u003eCamphora\u003c/em\u003e species, studies on \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; have mainly focused on essential oil composition and antibacterial properties, whereas the biosynthesis and molecular regulation of its secondary metabolites remain largely unexplored. Moreover, there is a growing increasing demand for eucalyptol-rich essential oils in pharmaceuticals, aromatherapy, and eco-friendly pesticides, yet the seasonal regulatory mechanisms underlying eucalyptol biosynthesis in Lauraceae species remain unclear[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The yield and composition of secondary metabolites in woody aromatic plants are highly dynamic, collectively influenced by developmental stage, genotype, environmental conditions, and harvest time[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Among these factors, the harvest period plays a decisive role in essential oil quality; inappropriate timing can lead to reduced yield, decreased proportions of key constituents, and diminished bioactivity[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, elucidating the effects of harvest timing on metabolite accumulation is crucial for optimizing product quality and improving economic returns.\u003c/p\u003e \u003cp\u003eThis study represents the first comprehensive investigation to characterize the dynamic reprogramming of terpenoid and flavonoid metabolic fluxes across seasonal harvest times in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; using an integrated transcriptomic and metabolomic framework. Leaf samples collected at three distinct harvest periods were analyzed to elucidate temporal changes in major secondary metabolites, identify key genes and transcription factors involved in metabolic regulation, and construct gene\u0026ndash;metabolite\u0026ndash;TF association networks. Our findings fill a critical knowledge gap in the metabolic regulatory mechanisms of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;, advance the understanding of temporal regulation of secondary metabolism in Lauraceae species, and provide a theoretical foundation for improving essential oil quality and supporting molecular breeding of elite varieties.\u003c/p\u003e \u003cp\u003eIn this study, leaves of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; collected at three different harvest periods were analyzed using integrated transcriptomic and metabolomic approaches. The aim was to reveal dynamic changes in major secondary metabolites across harvest times, identify key genes and transcription factors involved in metabolic regulation, and construct gene\u0026ndash;metabolite association networks. This work not only fills the research gap in metabolic regulation of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; and enriches the understanding of temporal regulation of secondary metabolism in Lauraceae species, but also providing a theoretical basis for essential oil quality improvement and molecular breeding of elite varieties.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material\u003c/h2\u003e \u003cp\u003eFresh leaves of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; were collected from 4\u0026ndash;5-year-old trees cultivated under standard field management conditions in the Hunan Botanical Garden, Yuhua District, Changsha, Hunan Province, China (N 28\u0026deg;6\u0026prime;26\u0026prime;\u0026prime;, E 113\u0026deg;1\u0026prime;51\u0026prime;\u0026prime;). Sampling was performed at 9:00 a.m. on clear days on June 30 (H1), August 30 (H2), and October 30 (H3), 2024. Functionally mature, healthy leaves without any signs of pest infestation or disease were selected from the middle canopy, while newly emerged leaves were avoided. For each sampling event, ten leaves were randomly collected and pooled to constitute one biological replicate, resulting in a total of nine biological replicates. Among them, six replicates were used for metabolomic analysis and three for transcriptomic analysis. Immediately after collection, the leaves were flash-frozen in liquid nitrogen for 10 minutes and subsequently stored at \u0026minus;\u0026thinsp;80\u0026deg;C until further use.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample preparation and extraction\u003c/h3\u003e\n\u003cp\u003e100 mg solid sample was added to a 2 mL centrifuge tube and a 6 mm diameter grinding bead was added. 800 \u0026micro;L of extraction solution (methanol: water\u0026thinsp;=\u0026thinsp;4:1, v/v) containing four internal standards (0.02 mg/mL L-2-chlorophenylalanine, etc.) were used for metabolite extraction. Samples were ground by the Wonbio-96c (Shanghai wanbo biotechnology co., LTD) frozen tissue grinder for 6 min (-10\u0026deg;C, 50 Hz), followed by low-temperature ultrasonic extraction for 30 min (5\u0026deg;C, 40 kHz). The samples were left at-20\u0026deg;C for 30 min, centrifuged for 15 min (4\u0026deg;C, 13000 g), and the supernatant was transferred to the injection vial for LC-MS/MS analysis. As a part of the system conditioning and quality control process, a pooled quality control sample (QC) was prepared by mixing equal volumes of all samples. The QC samples were disposed and tested in the same manner as the analytic samples. It helped to represent the whole sample set, which would be injected at regular intervals (every 6 samples) in order to monitor the stability of the analysis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eUPLC-MS/MS analysis\u003c/h3\u003e\n\u003cp\u003eThe ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis of sample was conducted on a Thermo UHPLC-Q Exactive system equipped with an ACQUITY HSS T3 column (100 mm \u0026times; 2.1 mm i.d., 1.8 \u0026micro;m; Waters, USA). The mobile phases consisted of 0.1% formic acid in water: acetonitrile (2:98, v/v) (solvent A) and 0.1% formic acid in acetonitrile (solvent B). The gradient conditions are as follows: 0-0.5 min, mobile phase B was maintained at 2%; 0.5\u0026ndash;7.5 min, mobile phase B was increased from 2% to 35%; 7.5\u0026ndash;13 min, mobile phase B was increased from 35% to 95%; 13-14.4 min, mobile phase B was maintained at 95%; 14.4\u0026ndash;14.5 min, mobile phase B was decreased from 95% to 2%; 14.5\u0026ndash;16 min, mobile phase B was maintained at 2%. The flow rate was 0.40 mL/min and the column temperature was 40\u0026deg;C. The UPLC system was coupled to a Thermo UHPLC-Q Exactive Mass Spectrometer equipped with an electrospray ionization (ESI) source operating in positive mode and negative mode. The optimal conditions were set as followed: source temperature at 400 \u003csup\u003eo\u003c/sup\u003eC; sheath gas flow rate at 40 arb; Aux gas flow rate at 10 arb; ion-spray voltage floating (ISVF) at-2800 V in negative mode and 3500 V in positive mode, respectively; Normalized collision energy, 20-40-60 V rolling for MS/MS. Full MS resolution was 70000, and MS/MS resolution was 17500. Data acquisition was performed with the Data Dependent Acquisition (DDA) mode. The detection was carried out over a mass range of 70-1050 m/z. Raw LC/MS data was performed by Progenesis QI (Waters Corporation, Milford, USA) software, and a three-dimensional data matrix in CSV format was exported. The information in this three-dimensional matrix included: sample information, metabolite name and mass spectral response intensity. Internal standard peaks, as well as any known false positive peaks (including noise, column bleed, and derivatized reagent peaks), were removed from the data matrix, deredundant and peak pooled. At the same time, the metabolites were identified by searching Self-built plant-specific metabolite database (MJDBPM)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eMetabolome data analysis\u003c/h3\u003e\n\u003cp\u003eThe data were analyzed using the free online platform of Majorbio Cloud Platform (cloud.majorbio.com). After obtaining the analyzable data matrix, the R package \"ropls\" (Version 1.6.2) was used to perform principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Based on the variable importance in projection (VIP) values derived from the PLS-DA model and the p-values from Student\u0026rsquo;s t-test, metabolites with VIP\u0026thinsp;\u0026gt;\u0026thinsp;1, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1 were identified as significantly differential metabolites. These differential metabolites were annotated for metabolic pathways using the KEGG database.\u003c/p\u003e\n\u003ch3\u003eRNA extraction, library construction and sequencing\u003c/h3\u003e\n\u003cp\u003eTotal RNA was extracted from the leaves of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; using the QIAzol Lysis Reagent Kit (Qiagen, Beijing, China). The integrity and purity of the extracted RNA were determined using a NanoDrop2000 spectrophotometer (Thermo Scientific, Waltham, USA) and an Agilent5300 Bioanalyzer (Agilent Technologies, California, USA), respectively. cDNA libraries were constructed from the mRNA of each sample using the Illumina\u0026reg; Stranded mRNA Prep, Ligation (San Diego, CA) for subsequent sequencing. Raw data were generated via bridge PCR amplification and sequencing using the NovaSeq X Plus platform. Subsequently, quality control (QC) filtering was performed on the raw data: adaptors and insert-free sequences were removed; low-quality bases at the ends were trimmed, and reads with excessively low overall quality, high N content, or insufficient length were discarded. After quality assessment, clean data were obtained. Finally, de novo assembly of the clean data was conducted using Trinity software, followed by optimization and filtering with TransRate and CD-HIT. The quality of the final assembly was evaluated via BUSCO.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene function annotation and expression analysis\u003c/h2\u003e \u003cp\u003eAll transcripts generated from transcriptome sequencing were aligned against six major databases (NR, Swiss-Prot, Pfam, COG, GO, and KEGG), and annotation information in each database was obtained with the annotation status statistically summarized. The RSEM software was used to quantitatively analyze the expression levels of genes and transcripts separately, facilitating subsequent analysis of differential expression of genes/transcripts among different samples and enabling the revelation of gene regulatory mechanisms by integrating sequence functional information. Following the acquisition of gene ReadCounts, differential expression analysis of genes across samples was performed to identify differentially expressed genes (DEGs), which further allowed the investigation of DEG functions. The DESeq2 software was employed for differential expression analysis, with the default screening criteria for significantly differentially expressed genes set as follows: FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1. Annotation analysis of DEGs was conducted using the GO and KEGG databases. GO enrichment analysis and KEGG enrichment analysis were performed via Goatools and Python scipy software, respectively[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eSignificance was tested by ANOVA and multiple comparisons were performed using IBM SPSS Statistics 24.0 software. GraphPad prism 5.0 software was used to create figures. Values marked with different lowercase letters are significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eMetabolomic Analysis of\u003c/b\u003e \u003cb\u003eC. glanduliferum\u003c/b\u003e \u003cb\u003e\u0026lsquo;Honganzhang\u0026rsquo; leaves at different harvesting times\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the metabolomic differences among \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; leaves collected at different harvest periods, UPLC-MS/MS was employed. Both positive and negative electrospray ionization (ESI) modes were used to ensure a comprehensive detection of metabolites across harvest times. The total ion chromatograms (TIC) exhibited stable signal responses and distinct separation among the different samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u0026ndash;C), indicating the reliability of the analytical system and suitability of the data for subsequent analyses. After signal-to-noise ratio filtering and database matching, a total of 2,704 metabolites were identified, including 1,453 secondary metabolites, 669 primary metabolites, and 582 compounds classified as other metabolite types.\u003c/p\u003e \u003cp\u003ePrincipal component analysis (PCA) was employed to classify correlated variables, reduce data dimensionality, and visualize metabolic variations both among and within groups. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, samples within the same group clustered closely together, indicating high reproducibility among biological replicates, whereas samples from different groups were clearly separated, suggesting substantial metabolic differences among the groups. The first principal component (PC1) accounted for 35.60% of the total variance, while the second principal component (PC2) explained 28.90%, demonstrating pronounced metabolite differences between groups. These results collectively confirm the stability and reliability of the metabolomic dataset.\u003c/p\u003e \u003cp\u003eThe identified secondary metabolites were annotated to facilitate their classification and functional characterization. These metabolites were categorized into twelve major classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Among them, terpenoids represented the most abundant group, comprising 482 compounds (33.17%), followed by flavonoids with 458 compounds (31.52%), and steroids and steroid derivatives with 157 compounds (10.81%). Other categories included phenolic acids and derivatives (96, 6.61%), lignans and derivatives (75, 5.16%), coumarins and derivatives (55, 3.79%), organic acids and derivatives (45, 3.10%), alkaloids and derivatives (32, 2.20%), stilbenes (18, 1.24%), tannins (13, 0.89%), quinones (12, 0.83%), and indoles and derivatives (10, 0.69%).These results substantially advance the understanding of the metabolomic landscape of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; and provide a scientific foundation for the rational utilization of this aromatic tree resource.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the differential metabolites among groups, partial least squares discriminant analysis (PLS-DA) was applied to the experimental data. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, samples from the same harvest time exhibited tighter clustering and higher within-group homogeneity, while the separation between different harvest times became more distinct. The heatmap visualization of metabolite expression patterns and hierarchical clustering analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) revealed that secondary metabolites displayed stage-specific expression profiles across different groups.\u003c/p\u003e \u003cp\u003eAmong the identified secondary metabolites, differential metabolites (DAMs) were determined based on the criteria |log₂FC| \u0026ge; 1 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A substantial number of DAMs were identified across the comparison groups, with notable variations in the number and direction (up- or down-regulation) of changes. Specifically, in the comparison between H1 and H2, a total of 527 DAMs were identified, including 190 upregulated and 337 downregulated metabolites. Between H1 and H3, the number of DAMs increased to 544, with 185 upregulated and 359 downregulated metabolites, indicating a distinct trend in metabolic regulation. In contrast, the comparison between H2 and H3 revealed 448 DAMs, of which 217 were upregulated and 231 were downregulated. Furthermore, Venn diagram analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) showed that 71 differential metabolites (DAMs) were shared among the three comparison groups, suggesting the presence of a stable core metabolic network that is likely governed by conserved regulatory mechanisms.\u003c/p\u003e \u003cp\u003eKEGG enrichment analysis was performed to investigate the classification and functional pathways of the differential metabolites. The results revealed that these metabolites were significantly enriched in pathways related to flavonoid biosynthesis, terpenoid biosynthesis, and alkaloid biosynthesis. Specifically, in the comparison between H1 and H2, the most significantly enriched pathways were Flavone and flavonol biosynthesis (map00944) and Biosynthesis of various plant secondary metabolites (map00999), while Isoquinoline alkaloid biosynthesis (map00950) and Diterpenoid biosynthesis (map00904) also showed varying degrees of enrichment (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). In the H1 vs H3 comparison, Flavone and flavonol biosynthesis (map00944) and Biosynthesis of various plant secondary metabolites (map00999) were again prominently enriched, accompanied by additional enrichment in Isoquinoline alkaloid biosynthesis (map00950) and Sesquiterpenoid and triterpenoid biosynthesis (map00909) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). For the H2 vs H3 comparison, the most significantly enriched pathway was Isoflavonoid biosynthesis (map00943), while Flavonoid biosynthesis (map00941), Isoquinoline alkaloid biosynthesis (map00950), and Diterpenoid biosynthesis (map00904) were also notably enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003eTo further characterize the dynamic changes in secondary metabolism across different harvesting times, time-series analyses of differential metabolites were performed within the major enriched pathways, including flavonoid, terpenoid, and alkaloid biosynthesis. The results revealed that most flavonoid compounds (such as quercetin and kaempferol derivatives) showed an increasing trend from H1 to H2 and remained stable from H2 to H3, suggesting enhanced antioxidant activity in the later harvest times. The relative content of total terpenoids was highest in H1, remained relatively stable in H2, and declined markedly in H3. Several monoterpenoid compounds (such as valdiate and cinnzeylanol) showed an increasing trend from H1 to H2 and remained stable from H2 to H3. In contrast, alkaloid-related metabolites displayed inconsistent trends across harvest times, indicating stage-specific regulatory mechanisms. Notably, the content of eucalyptol\u0026mdash;the principal component of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;\u0026mdash;remained relatively stable across all harvesting periods, suggesting that its biosynthesis is maintained at a consistent level during seasonal progression.\u003c/p\u003e \u003cp\u003eOverall, these findings indicate that secondary metabolism in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; leaves is highly dynamic across harvesting periods, with pronounced changes in flavonoid and terpenoid pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptome analysis\u003c/h2\u003e \u003cp\u003eA total of 486,115,312 raw sequencing reads were generated from the transcriptome sequencing. After removing low-quality reads, 486,113,300 high-quality clean reads were retained, yielding a total data volume of 72.52 Gb. The average GC content was 45.61%, and the mean Q20 and Q30 values exceeded 99% and 97%, respectively, with an overall sequencing error rate below 0.1%. These metrics indicate that the sequencing data were of high quality and suitable for downstream assembly (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After read assembly, 112,791 unigenes were obtained, exhibiting good assembly quality. Most unigenes ranged from 200\u0026ndash;500 bp (36%) and 501\u0026ndash;1000 bp (29%), with an additional 13,567 unigenes between 1001\u0026ndash;2000 bp and 7,918 unigenes longer than 2000 bp (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), supporting their use in subsequent analyses. Principal component analysis (PCA) showed clear clustering of transcriptome samples within the same harvesting period and distinct separation among different periods, indicating good reproducibility and suitability of the dataset for further transcriptomic comparisons (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eTo elucidate transcriptomic differences across harvest times in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;, differentially expressed genes (DEGs) were identified for each pairwise comparison using the criteria \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026ge; 1. The number of DEGs varied substantially among comparisons: the H1 vs. H2 comparison yielded the fewest DEGs (9,824), including 4,850 upregulated and 4,974 downregulated genes; the H1 vs. H3 comparison identified 11,935 DEGs, of which 7,791 were upregulated and 4,144 were downregulated; and the H2 vs. H3 comparison produced the largest number of DEGs (13,513), with 7,991 upregulated and 5,522 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). A total of 1,058 DEGs were shared across all three comparisons (H1 vs. H2 vs. H3; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). These results indicate pronounced transcriptional divergence among the different harvest times.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQuality control analysis of transcriptome sequencing data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRaw reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw bases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClean reads\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eClean bases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eError rate (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ20(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ30(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGC content (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59780710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8967106500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59780442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8938260469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51219664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7682949600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51219474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7658144542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e98.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48723322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7308498300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48723100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7289126109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e98.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61368276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9205241400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e61368012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9181334857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53405572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8010835800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53405394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7988253507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48089916\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7213487400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48089756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7196489833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59468042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8920206300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59467816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8888776348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52663376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7899506400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52663112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7876328195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51396434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7709465100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51396194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7686534712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e99.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e97.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e486115312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72917296800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e486113300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72703248572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes (DEGs) analysis\u003c/h2\u003e \u003cp\u003eTo elucidate the molecular and metabolic regulatory differences among the three harvest periods, KEGG pathway enrichment analysis and hierarchical clustering were performed on the DEGs. As shown in the KEGG bubble plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), the H1 vs. H2 comparison group exhibited significant enrichment in pathways related to secondary metabolism and carbohydrate metabolism, such as phenylpropanoid biosynthesis, starch and sucrose metabolism, and flavonoid biosynthesis. These pathways showed low p-values, high gene counts, and relatively high rich factors, indicating that secondary metabolite biosynthesis and carbon metabolic processes were particularly active during this harvest time (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In the H1 vs. H3 comparison, significantly enriched pathways included protein processing in the endoplasmic reticulum, monoterpenoid biosynthesis, and phenylpropanoid biosynthesis, suggesting that protein homeostasis and secondary metabolic processes contribute to the molecular distinctions between these two harvesting periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). For the H2 vs. H3 comparison, phenylpropanoid biosynthesis, plant hormone signal transduction, and the biosynthesis of various plant secondary metabolites were significantly enriched, underscoring the involvement of secondary metabolic pathways and hormone-mediated regulation in shaping the metabolic differences between H2 and H3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In addition, hierarchical clustering of DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) revealed distinct expression patterns across the harvest periods, reflecting stage-specific transcriptional regulation. Collectively, these results demonstrate that the enriched DEGs across different harvest periods are predominantly associated with pathways involved in the biosynthesis of diverse classes of secondary metabolites. A total of 863 transcription factors (TFs) were identified from the assembled transcripts, which were assigned to multiple TF families. These included the MYB superfamily (154 members), AP2/ERF (93), C2C2 (70), NAC (69), bHLH (67), WRKY (49), GRAS (33), and bZIP (30), along with several additional, less abundant families (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eIntegrative analysis of metabolome and transcriptome of terpenoid biosynthesis\u003c/h2\u003e \u003cp\u003eIn the terpenoid biosynthesis pathway (Fig.\u0026nbsp;5), a total of 42 DEGs were identified, including five 1-deoxy-D-xylulose-5-phosphate synthase (\u003cem\u003eDXS\u003c/em\u003e) genes, one 1-hydroxy-2-methyl-2-(E)-butenyl-4-diphosphate reductase (\u003cem\u003eIspH\u003c/em\u003e) gene, four isoprene synthase (\u003cem\u003eIspS\u003c/em\u003e) genes, one acetoacetyl-CoA thiolase (\u003cem\u003eACAT\u003c/em\u003e) gene, one 3-hydroxy-3-methylglutaryl-CoA reductase (\u003cem\u003eHMGCR\u003c/em\u003e) gene, two geranylgeranyl diphosphate synthase (\u003cem\u003eGPPS\u003c/em\u003e) genes, one geranyl diphosphate synthase (\u003cem\u003eGPS\u003c/em\u003e) gene, two farnesyl diphosphate synthase (\u003cem\u003eFDPS\u003c/em\u003e) genes, one farnesol dehydrogenase (\u003cem\u003eFLDH\u003c/em\u003e) gene, one α-farnesene synthase 1 (\u003cem\u003eAFS1\u003c/em\u003e) gene, three terpene synthase 04 (\u003cem\u003eTPS04\u003c/em\u003e) genes, one ent-copalyl diphosphate synthase (\u003cem\u003eCPS\u003c/em\u003e) gene, one sandaracopimaradiene synthase (\u003cem\u003eSPS\u003c/em\u003e) gene, one ent-kaurenoic acid oxidase (\u003cem\u003eKAO\u003c/em\u003e) gene, one GA20-oxidase (\u003cem\u003eGA20ox\u003c/em\u003e) gene, five GA2-oxidase (\u003cem\u003eGA2ox\u003c/em\u003e) genes, two GA3-oxidase (\u003cem\u003eGA3ox\u003c/em\u003e) genes, seven 10-hydroxygeraniol oxidoreductase (\u003cem\u003e10HGO\u003c/em\u003e) genes, and two α-terpene synthase (\u003cem\u003eαTPS\u003c/em\u003e) genes.\u003c/p\u003e \u003cp\u003eIsopentenyl pyrophosphate (IPP), the universal C5 precursor for the biosynthesis of all terpenoids, is produced via both the MVA and MEP pathways. The key gene \u003cem\u003eHMGCR\u003c/em\u003e in the MVA pathway, together with the core genes \u003cem\u003eDXS\u003c/em\u003e (2 unigenes) and \u003cem\u003eIspH\u003c/em\u003e in the MEP pathway, exhibited significantly elevated transcript levels in harvest times H2 and H3 relative to H1. This suggests that, along with seasonal progression, the metabolic capacity to supply IPP and dimethylallyl diphosphate (DMAPP) for downstream terpenoid biosynthesis is markedly enhanced. Most genes encoding enzymes responsible for catalyzing the formation of GPP, FPP, and GGPP (e.g., \u003cem\u003eGPPS\u003c/em\u003e, \u003cem\u003eFDPS\u003c/em\u003e) as well as multiple terpene synthase genes (\u003cem\u003eFLDH\u003c/em\u003e, \u003cem\u003eTPS04\u003c/em\u003e, \u003cem\u003eαTPS\u003c/em\u003e, \u003cem\u003eIspS\u003c/em\u003e, \u003cem\u003e10HGO\u003c/em\u003e) showed either a continuously increasing transcription pattern from H1 to H3 or maintained high expression in H2 and H3. Similarly, genes involved in gibberellin (GA) biosynthesis, such as \u003cem\u003eCPS\u003c/em\u003e, \u003cem\u003eKAO\u003c/em\u003e, \u003cem\u003eGA20ox\u003c/em\u003e, and \u003cem\u003eGA3ox\u003c/em\u003e, were substantially upregulated in H2 and H3 compared with H1, whereas the GA-degrading gene \u003cem\u003eGA2ox\u003c/em\u003e was highly expressed in H1 but suppressed in H2 and H3. This coordinated antagonistic regulation implies a rapid and pronounced accumulation of bioactive GA during the transition from H1 to H2 and H3.\u003c/p\u003e \u003cp\u003eOverall, the majority of terpenoid-related genes displayed low transcript abundance in H1 but were significantly upregulated during H2 and H3, indicating that terpenoid biosynthesis exhibits temporal regulation and shows increased transcriptional \u003c/p\u003e \u003cp\u003eactivity at later harvest dates.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;5\u003c/b\u003e Terpenoid biosynthetic pathway constructed based on differentially expressed genes (DEGs). The color scale from blue to red represents the expression levels from low to high (normalized FPKM).\u003c/p\u003e \u003cp\u003eTo elucidate the complex interplay between transcriptional regulation and metabolite accumulation within the terpenoid biosynthetic pathway, a co-expression regulatory network was constructed based on Pearson correlation analysis (|r| \u0026gt; 0.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), incorporating 22 DEGs, 50 TFs, and 56 DAMs (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). This network provides an intuitive visualization of the interaction architecture linking transcription factors, structural genes, and metabolites. Notably, the vast majority of TFs exhibited strong positive correlations with the core biosynthetic genes, including members of the MYB, WRKY, NAC, and bHLH families, which were tightly associated with several key genes such as \u003cem\u003eCgTPS04\u003c/em\u003e, \u003cem\u003eCgGA20ox3\u003c/em\u003e, and \u003cem\u003eCg10HGO5\u003c/em\u003e. These associations suggest that these transcription factors are likely candidates to regulate terpenoid biosynthesis by influencing the transcriptional activity of essential structural genes. Moreover, the expression levels of these core genes were strongly positively correlated with the accumulation of downstream metabolites. For example, the gibberellin biosynthesis gene \u003cem\u003eCgGA20ox3\u003c/em\u003e showed a high correlation with the accumulation of Gibberellin A37 and Gibberellin A44, whereas the terpene synthase gene \u003cem\u003eCgTPS04\u003c/em\u003e was significantly associated with multiple terpenoid compounds, including cannabidiol and ganoderic acid W. Collectively, these findings indicate that multiple transcription factors act as upstream regulatory switches that positively activate a cascade of downstream crucial genes, ultimately driving the substantial accumulation of structurally diverse terpenoid metabolites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIntegrative analysis of metabolome and transcriptome of flavonoid biosynthesis\u003c/h2\u003e \u003cp\u003eThe expression profiles of key genes involved in flavonoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e) revealed a total of 43 differentially expressed genes (DEGs), including five phenylalanine ammonia lyase (\u003cem\u003ePAL\u003c/em\u003e) genes, three 4-coumarate-CoA ligase (\u003cem\u003e4CL\u003c/em\u003e) genes, three chalcone synthase (\u003cem\u003eCHS\u003c/em\u003e) genes, two chalcone isomerase (\u003cem\u003eCHI\u003c/em\u003e) genes, two cinnamate 4-hydroxylase (\u003cem\u003eC4H\u003c/em\u003e) genes, two flavanone 3-hydroxylase (\u003cem\u003eF3H\u003c/em\u003e) genes, twelve hydroxycinnamoyl-CoA shikimate/quinate hydroxycinnamoyl transferase (\u003cem\u003eHCT\u003c/em\u003e) genes, two cytochrome P450 monooxygenase 98A subfamily (\u003cem\u003eCYP98A\u003c/em\u003e) genes, seven caffeic acid O-methyltransferase (\u003cem\u003eCOMT\u003c/em\u003e) genes, one cytochrome P450 monooxygenase 75B1 subfamily (\u003cem\u003eCYP75B1\u003c/em\u003e) gene, two flavonol synthase (\u003cem\u003eFLS\u003c/em\u003e) genes, and two leucoanthocyanidin reductase (\u003cem\u003eLAR\u003c/em\u003e) genes.\u003c/p\u003e \u003cp\u003eThe majority of transcripts encoding the initial enzymes of the phenylpropanoid pathway (\u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, and \u003cem\u003e4CL\u003c/em\u003e) exhibited a fluctuating expression pattern characterized by high abundance in H1 and H3 but markedly reduced levels in H2, suggesting a sufficient supply of upstream flavonoid precursors during H1 and H3, whereas this supply is suppressed in H2. Conversely, genes associated with the synthesis of the core flavonoid skeleton (\u003cem\u003eCHS\u003c/em\u003e and \u003cem\u003eCHI\u003c/em\u003e) displayed the opposite pattern, with significantly elevated expression in H2 and reduced levels in H1 and H3; meanwhile, the expression of the key flavanone hydroxylase gene F3H followed the same trend as the initial enzymes (higher expression in H1/H3 and lower in H2). These results indicate that biosynthetic activity for the core skeleton is strongest in H2, whereas the conversion of flavanones to dihydroflavonols is more active during H1 and H3. Regarding branch-pathway enzymes, the flavonol synthase gene FLS exhibited high expression in H1 and H3, promoting flavonol biosynthesis, while the \u003cem\u003eLAR\u003c/em\u003e gene encoding leucoanthocyanidin reductase was upregulated specifically in H2, facilitating proanthocyanidin accumulation. Additionally, modification-associated enzyme genes (\u003cem\u003eHCT\u003c/em\u003e, \u003cem\u003eCYP98A\u003c/em\u003e, \u003cem\u003eCYP75B1\u003c/em\u003e, and \u003cem\u003eCOMT\u003c/em\u003e) were co-expressed with the initial enzymes (high in H1/H3 and low in H2), enhancing structural diversity of flavonols during H1 and H3. Collectively, these expression patterns reveal a stage-specific metabolic flux of flavonoid biosynthesis: the metabolic direction in H1 and H3 favors flavonol production (driven by active precursor supply, modification steps, and \u003cem\u003eFLS\u003c/em\u003e), whereas H2 preferentially promotes proanthocyanidin accumulation (supported by enhanced core skeleton formation and \u003cem\u003eLAR\u003c/em\u003e activity). This provides the molecular basis for compositional differences in flavonoids across harvest times.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough the integrated co-expression network analysis of DEGs, TFs, and DAMs, a highly coordinated transcriptional regulatory system underlying flavonoid biosynthesis in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; was revealed. A total of 26 structural genes, 72 flavonoid metabolites and 50 transcription factors formed a dense association network based on Pearson correlations (|r| \u0026gt; 0.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Several key structural genes, including \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eCHI\u003c/em\u003e, \u003cem\u003eF3H\u003c/em\u003e, \u003cem\u003eFLS\u003c/em\u003e, \u003cem\u003eLAR\u003c/em\u003e, and \u003cem\u003eCYP75B1\u003c/em\u003e, were identified as central hub genes with the highest connectivity. \u003cem\u003eCHS\u003c/em\u003e showed strong positive correlations with major flavonol compounds such as quercetin-3-O-glucoside, quercetin-7-O-glucoside, kaempferol-3-O-glucoside and pinobanksin-5-O-glucoside, confirming its dominant role in directing metabolic flux toward flavonoid biosynthesis. Similarly, \u003cem\u003eFLS\u003c/em\u003e and \u003cem\u003eCYP75B1\u003c/em\u003e exhibited significant correlations with kaempferol-3-O-rhamnoside, myricetin-3-O-rutinoside and quercetin derivatives, indicating their involvement in downstream flavonol diversification. In addition, \u003cem\u003eLAR\u003c/em\u003e showed strong correlations with (+)-catechin and epicatechin, highlighting its role in proanthocyanidin biosynthesis. Moreover, transcription factors MYB, bHLH, WRKY and NAC displayed extensive co-expression relationships with these structural genes, forming regulatory modules such as \u003cem\u003eCgMYB1\u003c/em\u003e/\u003cem\u003eCgMYB13\u003c/em\u003e\u0026ndash;\u003cem\u003eCHS\u003c/em\u003e/\u003cem\u003eFLS\u003c/em\u003e\u0026ndash;quercetin/kaempferol and CgWRKY33\u0026ndash;CYP75B1/LAR\u0026ndash;catechin, suggesting that flavonoid accumulation is predominantly regulated at the transcriptional level. These results demonstrate that the enhanced expression of key biosynthetic genes and transcription factors contributes to the high accumulation of flavonoids observed at the H2 harvest times.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003ePlants synthesize and accumulate a wide array of metabolites throughout their growth and development, including primary metabolites essential for basic physiological processes and secondary metabolites involved in defense, adaptation, and signal regulation. Systematic metabolomic profiling has estimated that terrestrial plants may produce more than 200,000 natural small-molecule compounds, a level of chemical diversity that far exceeds most other biological groups [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These metabolites not only sustain physiological homeostasis and ecological interactions in plants but also constitute critical resources for human foods, pharmaceuticals, and bioactive natural products, thereby holding substantial significance for agricultural improvement and human health [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. From a biosynthetic and evolutionary perspective, plants have developed diverse secondary metabolic pathways through gene duplication, functional divergence, and recruitment of enzymatic systems. Convergent evolution frequently occurs across distinct lineages, enabling different species to generate structurally similar compounds in response to shared selective pressures such as herbivory and pathogen attack. The remodeling of gene\u0026ndash;enzyme\u0026ndash;metabolite networks is regarded as a major driving force underlying the diversification of secondary metabolism [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Lauraceae species are characterized by abundant secondary metabolites\u0026mdash;including terpenoids, phenylpropanoids, and flavonoids\u0026mdash;and within the genus \u003cem\u003eCamphora\u003c/em\u003e, terpenoids (including monoterpenes, sesquiterpenes, and diterpenes) represent the predominant class. The major constituents of essential oils commonly include camphor, eucalyptol, citral, and linalool [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Numerous studies have demonstrated that \u003cem\u003eCamphora\u003c/em\u003e plants can be classified into distinct chemotypes, such as camphor-type, cineole-type, and linalool-type. These chemotypes differ markedly not only in chemical composition but also in biological activities such as antimicrobial, anti-inflammatory, and antioxidant properties, resulting in substantial variation in the structure and activity of essential oils derived from different species or varieties within the genus [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMulti-omics approaches have played a pivotal role in elucidating the mechanisms underlying the accumulation of plant secondary metabolites and have become essential tools for investigating secondary metabolic regulation in \u003cem\u003eCamphora\u003c/em\u003e species. In \u003cem\u003ePaeonia lactiflora\u003c/em\u003e, Xu \u003cem\u003eet al.\u003c/em\u003e integrated transcriptomic and metabolomic datasets and uncovered distinct tissue-specific accumulation patterns of monoterpene glycosides, gallaglycosides, and flavonoids across roots, leaves, and flowers. Through differential gene expression (DEGs) analysis, they further identified transcriptional regulatory networks associated with the biosynthesis of these metabolites[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, Liu \u003cem\u003eet al.\u003c/em\u003e employed an integrated omics strategy in \u003cem\u003eJinsi Huangju\u003c/em\u003e (Chrysanthemum), systematically characterizing the molecular mechanisms driving flavone biosynthesis and identifying several key genes involved in flavonoid formation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Multi-omics integration has also been extensively used to explore the dynamic changes in volatile metabolites and carotenoids during developmental processes. For example, in different floral developmental stages of \u003cem\u003eCamellia huana\u003c/em\u003e, Chen \u003cem\u003eet al.\u003c/em\u003e combined transcriptomic and metabolomic analyses to investigate biosynthetic pathways of volatiles and carotenoids, revealing temporally and spatially coordinated patterns between gene expression and metabolite accumulation[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, we applied an integrated transcriptomic and metabolomic framework to systematically elucidate the dynamic accumulation patterns of secondary metabolites and their molecular regulatory mechanisms in the leaves of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; harvested at three harvest times (H1: June 30, H2: August 30, H3: October 30). Our results demonstrate pronounced temporal dynamics in secondary metabolism across harvest periods, particularly within terpenoid and flavonoid biosynthetic pathways. Such metabolic plasticity likely represents an adaptive response of woody aromatic plants to seasonal climatic changes and environmental fluctuations, and further highlights the decisive role of harvest timing in determining essential oil quality.\u003c/p\u003e \u003cp\u003eTerpenoids constitute the principal components of the essential oil in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; and represent the most abundant class of secondary metabolites in its leaves. Metabolomic profiling in this study revealed a distinct seasonal variation pattern: the relative content of total terpenoids was highest in the early harvest (H1, June), remained relatively stable in the mid-season (H2, August), and declined markedly in the late season (H3, October). Interestingly, despite the general decline in total accumulation towards the late season, specific monoterpenoid compounds\u0026mdash;such as valdiate and cinnzeylanol\u0026mdash;showed an increasing trend from H1 to H2 and remained stable from H2 to H3. These temporal patterns likely reflect metabolic adjustments aligned with seasonal physiological demands and environmental fluctuations across the sampling dates. Notably, an intriguing discrepancy was observed between transcriptional activity and metabolic accumulation. Although the transcript levels of key biosynthetic genes (e.g., \u003cem\u003eDXS\u003c/em\u003e, \u003cem\u003eGPPS\u003c/em\u003e, \u003cem\u003eTPS04\u003c/em\u003e) were significantly upregulated in H2 and H3 compared to H1, the total terpenoid content in leaves did not increase proportionally; instead, it remained stable or declined. This non-linear correlation, often referred to as \"transcript-metabolite discordance,\" implies that gene expression levels reflect the biosynthetic potential or flux rate rather than the final accumulated pool size [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In the context of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;, this phenomenon can be explained by three seasonal adaptation mechanisms. First, the dynamic equilibrium between volatilization and biosynthesis plays a critical role. The H2 sampling period (August) coincides with peak summer temperatures. High temperatures are known to exponentially increase the emission rates of volatile monoterpenes and sesquiterpenes from leaf glands. Therefore, the upregulated expression of terpenoid biosynthetic genes in H2 and H3 likely reflects a high metabolic flux\u0026mdash;a compensatory mechanism to replenish the volatile pool depleted by heat-induced emission, rather than to increase the net storage [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This \"high flux, low storage\" state suggests the plant is actively maintaining metabolic homeostasis under environmental stress. Second, metabolic flux diversion may limit the accumulation of essential oil constituents. Terpenoid precursors, such as GGPP, serve as common substrates for both secondary metabolites and primary growth regulators like gibberellins (GAs). Our transcriptome data confirmed that GA biosynthetic genes (\u003cem\u003eCPS\u003c/em\u003e, \u003cem\u003eKAO\u003c/em\u003e, \u003cem\u003eGA20ox\u003c/em\u003e) were significantly upregulated in H2 and H3. This suggests that metabolic flux may be competitively diverted towards hormone biosynthesis to regulate seasonal growth or stress adaptation, thereby reducing the carbon flow available for the accumulation of essential oil-specific terpenoids. Additionally, the coupling between hormone signaling and terpenoid metabolism provides further insight into this seasonal reprogramming. GA signaling has been shown to regulate cell elongation and differentiation and to modulate TPS expression or protein activity through interactions with secondary metabolism[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. We propose that dynamic GA fluctuations from June to October may directly or indirectly influence terpenoid biosynthetic flux, mediating the shift from broad-spectrum terpenoid production in H1 to the selective retention of specific metabolites (e.g., valdiate and cinnzeylanol) in H2 and H3. Finally, feedback regulation loops and transcriptional regulatory networks orchestrate this dynamic. The peak accumulation of terpenoids observed in H1 (June) could trigger a negative feedback mechanism that suppresses the expression of upstream biosynthetic genes. Conversely, as the internal metabolite pool is depleted by volatilization or catabolism in H3 (October), this suppression is relieved, resulting in the observed upregulation of transcripts. Network-based transcriptional regulatory analysis further revealed that multiple TFs\u0026mdash;including MYB, WRKY, NAC, and bHLH\u0026mdash;showed strong positive correlations with key terpenoid structural genes. These TFs likely act as integrators of environmental signals (e.g., photoperiod and temperature), serving as upstream regulatory switches that fine-tune pathway-specific metabolic output [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFlavonoids from \u003cem\u003eCamphora\u003c/em\u003e species exhibit substantial bioactivities, including antioxidant, anti-inflammatory, antimicrobial, and potential antidiabetic or metabolic regulatory effects. In vitro functional assays demonstrated that flavonoid mixtures extracted from \u003cem\u003eC. camphora\u003c/em\u003e leaves exhibit notable activity in free-radical scavenging assays (DPPH, ABTS) and antibacterial tests. Moreover, several studies have reported inhibitory effects of these compounds on metabolic enzymes such as α-glucosidase, suggesting their therapeutic potential for metabolic disorders[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Flavonoids represent the second most abundant class of secondary metabolites in the leaves of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;. Temporal metabolic profiling revealed that the majority of flavonoids increased markedly from H1 to H2, followed by stabilization from H2 to H3. However, transcriptional regulation of the flavonoid biosynthetic pathway exhibited a temporally alternating pattern. During H1 and H3, metabolic flux was preferentially directed toward flavonol production. The upstream phenylpropanoid pathway genes (\u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e) were highly expressed at H1 and H3. Similarly, the flavonol-specific branching gene \u003cem\u003eFLS\u003c/em\u003e, together with modification-related enzyme genes (\u003cem\u003eHCT\u003c/em\u003e, \u003cem\u003eCYP98A\u003c/em\u003e, \u003cem\u003eCYP75B1\u003c/em\u003e, \u003cem\u003eCOMT\u003c/em\u003e), exhibited elevated transcript abundance in these harvest times, suggesting enhanced biosynthesis and structural diversification of flavonols such as quercetin- and kaempferol-derived compounds. In contrast, H2 favored the preferential accumulation of proanthocyanidins. Core flavonoid scaffold genes (\u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eCHI\u003c/em\u003e) displayed peak expression at H2, and the \u003cem\u003eLAR\u003c/em\u003e gene (leucoanthocyanidin reductase) was specifically upregulated, promoting accumulation of proanthocyanidins such as (+)-catechin and epicatechin. This alternation is consistent with prior observations in \u003cem\u003eCinnamomum cassia\u003c/em\u003e, where phenylpropanoid pathway genes are upregulated during late harvest times (120 MAP), promoting volatile formation such as cinnamaldehyde[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. \u003cem\u003eLAR\u003c/em\u003e, which catalyzes the conversion of leucocyanidin to (+)-catechin, has been widely recognized as a key enzyme controlling proanthocyanidin biosynthesis, showing strong correlation with catechin accumulation in \u003cem\u003eCamellia sinensis\u003c/em\u003e, grapevine, and \u003cem\u003eLithocarpus polystachyus\u003c/em\u003e, among others[\u003cspan additionalcitationids=\"CR49 CR50\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Studies on flavonoid metabolism in \u003cem\u003eC. camphora\u003c/em\u003e have revealed that major flavonols such as rutin accumulate predominantly in leaves, while the biosynthetic gene \u003cem\u003eCcPAL_1\u003c/em\u003e is predominantly expressed in roots, implying the involvement of metabolite translocation between synthesis and storage tissues [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Such stage-dependent metabolic switching reflects the dynamic balance between ultraviolet protection (flavonols) and structural defense (proanthocyanidins) required for adaptation to different seasonal stressors (e.g., high temperature in summer, lower temperature in autumn), resembling the mechanism observed in \u003cem\u003eCryptomeria fortunei\u003c/em\u003e, wherein flavonoids accumulate during winter to enhance cold resistance [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similar to terpene biosynthesis, regulation of flavonoid pathways is controlled by transcriptional networks dominated by MYB, bHLH, WRKY, and NAC transcription factors. These TFs form tightly coordinated regulatory modules with key structural genes such as \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eFLS\u003c/em\u003e, \u003cem\u003eLAR\u003c/em\u003e, and \u003cem\u003eCYP75B1\u003c/em\u003e. The associations identified in this study for \u003cem\u003eCgMYB1\u003c/em\u003e, \u003cem\u003eCgMYB13\u003c/em\u003e, and related TFs with flavonoid biosynthetic genes are consistent with previous findings describing MYB- and bHLH-mediated regulation of flavonoid accumulation in \u003cem\u003eC. camphora\u003c/em\u003e leaves.\u003c/p\u003e \u003cp\u003eThis study elucidated the dynamic accumulation patterns of secondary metabolites in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; across different harvest periods through integrated multi-omics analysis and uncovered the associated gene regulatory networks. Nevertheless, several limitations remain. First, the analyses were performed at the whole-leaf level and therefore did not resolve the spatiotemporal metabolic profiles of specific tissues such as secretory glands; future investigations incorporating single-cell and spatial omics technologies are required to dissect tissue-specific regulatory mechanisms. Second, the functional roles of key structural genes and transcription factors were inferred primarily from correlative evidence, and their causal relationships must be validated through gene editing, enzymatic assays, and metabolic flux analyses. In addition, seasonal environmental variability may contribute to harvest-dependent differences; controlled-environment experiments with environmental-factor gradients are needed to decouple developmental effects from stress responses. Integrating glandular anatomical characterization with large-scale germplasm screening, GWAS/QTL mapping, and related approaches may ultimately provide precise molecular targets and a theoretical foundation for molecular breeding and industrial development of essential oils in Lauraceae species.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study employed integrated transcriptomic and metabolomic analyses to systematically characterize the accumulation patterns of secondary metabolites and their underlying regulatory mechanisms across three harvest periods (H1, H2, H3) in leaves of \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;. A total of 2,704 metabolites were identified, including 1,453 secondary metabolites predominantly belonging to terpenoids (33.17%) and flavonoids (31.52%). Secondary metabolism exhibited pronounced temporal dynamics, with major differentially accumulated metabolites significantly enriched in flavonoid, terpenoid, and alkaloid biosynthetic pathways. Total terpenoid abundance was highest in H1, remained stable in H2, and declined sharply in H3; however, several monoterpenoids retained high abundance from H2 to H3, and the principal essential-oil constituent, eucalyptol, showed no significant variation across harvest periods. Flavonoid levels increased from H1 to H2 and subsequently stabilized, indicating stage-specific metabolic prioritization. Transcriptome sequencing revealed extensive, harvest-dependent transcriptional reprogramming, with 9,824\u0026ndash;13,513 differentially expressed genes (DEGs) identified across comparisons. KEGG enrichment analysis indicated that pathways related to phenylpropanoid biosynthesis, flavonoid biosynthesis, monoterpenoid biosynthesis, and hormone signaling were major contributors to the stage-specific metabolic differences. Integrated network analysis further demonstrated coordinated regulation among key structural genes (e.g., \u003cem\u003eACAT\u003c/em\u003e, \u003cem\u003eDXS\u003c/em\u003e, \u003cem\u003eGPS\u003c/em\u003e, \u003cem\u003eFLS\u003c/em\u003e, \u003cem\u003eLAR\u003c/em\u003e, \u003cem\u003eGA20ox\u003c/em\u003e), secondary metabolites, and transcription factors from the MYB, bHLH, NAC, and WRKY families, forming tightly connected co-expression modules that orchestrate terpenoid and flavonoid metabolic flux.\u003c/p\u003e \u003cp\u003eCollectively, this study elucidates the molecular regulatory framework governing secondary-metabolite dynamics across harvest periods in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;. The findings provide practical guidance for optimizing harvest timing to maximize target compounds\u0026mdash;particularly monoterpenoid-rich essential oils and bioactive flavonoids\u0026mdash;and offer a molecular foundation for future metabolic engineering and breeding efforts aimed at improving essential-oil composition and product quality.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e The collection of plant materials and all experimental procedures complied with relevant institutional, national, and international guidelines and legislation. Plant materials of \u003cem\u003eCamphora glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; were collected in Changsha, Hunan Province, China, in accordance with local regulations. Permission to collect the plant materials was obtained from the \u003cb\u003eAdministration of the Hunan Botanical Garden\u003c/b\u003e. Voucher specimens have been deposited in the Herbarium of the Institute of Resource Plants, Hunan Provincial Botanical Garden, Changsha, Hunan, China \u003cb\u003e(Voucher number: HBG-CGH-20240530)\u003c/b\u003e. The specimens were identified by Professor Xinhai Peng, Hunan Botanical Garden.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor details\u003c/h2\u003e \u003cp\u003e \u003csup\u003e1\u003c/sup\u003eCollege of Forestry, Central South University of Forestry \u0026amp; Technology, Changsha, 410004, China. \u003csup\u003e2\u003c/sup\u003eHunan Botanical Garden, Changsha, 410116, China. \u003csup\u003e3\u003c/sup\u003eGolden Land Ecological Agriculture Co., Ltd., Hongjiang, 418200, China.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Hunan Innovation Platform and Talent Project (Number: 2022NK4194).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.Y. and X.Q. conceived and designed the study. Y.Y. and X.Q. performed the experiments and conducted the transcriptomic and metabolomic analyses. Z.Y. and Y.Y.N. assisted with sample collection and metabolite profiling. L.J. and P.J. supervised the project, acquired funding, and critically revised the manuscript. Y.Y. and X.Q. drafted the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe RNA-seq data have been deposited in the Genome Sequence Archive in the National Genomics Data Center under the accession number CRA058443, which is publicly accessible at https://ngdc.cncb.ac.cn/gsa. Other supporting data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCrozier A, Clifford MN, Ashihara H. Plant secondary metabolites: occurrence, structure and role in the human diet. 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Front Plant Sci. 2022;13:862746. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fpls.2022.862746\u003c/span\u003e\u003cspan address=\"10.3389/fpls.2022.862746\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Camphora glanduliferum ‘Honganzhang’, Secondary metabolites, Harvest time, Transcriptome, Metabolome","lastPublishedDoi":"10.21203/rs.3.rs-8200539/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8200539/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003e \u003cem\u003eCamphora glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo; is an economically important woody plant valued for its eucalyptol-rich essential oils. The yield and composition of its secondary metabolites are highly dynamic and sensitive to harvest timing. However, the molecular mechanisms governing the seasonal reprogramming of terpenoid and flavonoid biosynthesis in this species remain largely unexplored. To elucidate these regulatory networks, we performed an integrated metabolomic and transcriptomic analysis of leaves collected at three distinct harvest times: June (H1), August (H2), and October (H3).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMetabolomic profiling identified 2,704 metabolites, with terpenoids (33.17%) and flavonoids (31.52%) being the predominant classes. A clear temporal divergence was observed: total terpenoid accumulation peaked in H1 and declined in H3, whereas flavonoid content increased significantly from H1 to H2. Transcriptomic analysis identified 9,824\u0026thinsp;\u0026minus;\u0026thinsp;13,513 differentially expressed genes (DEGs) across comparisons. Notably, we observed a transcript-metabolite discordance in terpenoid biosynthesis; key genes (e.g., \u003cem\u003eDXS\u003c/em\u003e, \u003cem\u003eGPPS\u003c/em\u003e, \u003cem\u003eTPS04\u003c/em\u003e) were upregulated in H2 and H3 despite lower total terpenoid accumulation, suggesting a compensatory mechanism driven by high volatilization or metabolic flux diversion towards gibberellin biosynthesis. Conversely, flavonoid biosynthesis exhibited a strictly coordinated stage-specific flux redirection: H1 and H3 favored flavonol accumulation via the upregulation of \u003cem\u003eFLS\u003c/em\u003e and \u003cem\u003eCYP75B1\u003c/em\u003e, while H2 prioritized proanthocyanidin synthesis through the specific upregulation of \u003cem\u003eLAR\u003c/em\u003e. Furthermore, a co-expression regulatory network was constructed to link key metabolites and structural genes to transcription factors from the MYB, bHLH, NAC, and WRKY families, highlighting their roles as central hubs in orchestrating these metabolic shifts.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reveals the dynamic transcriptional reprogramming that drives the seasonal accumulation of secondary metabolites in \u003cem\u003eC. glanduliferum\u003c/em\u003e \u0026lsquo;Honganzhang\u0026rsquo;. It investigates the specific regulatory modules controlling the trade-off between flavonol and proanthocyanidin pathways and proposes a mechanism for the seasonal fluctuation of terpenoids. These findings provide a theoretical foundation for determining optimal harvest windows to maximize specific bioactive compounds and facilitate the molecular breeding of elite Lauraceae varieties.\u003c/p\u003e","manuscriptTitle":"Integrated transcriptome and metabolome analysis reveals the accumulation of secondary metabolites in Camphora glanduliferum ‘Honganzhang’ at different harvest times","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-29 13:00:59","doi":"10.21203/rs.3.rs-8200539/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-28T11:39:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-27T07:33:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10407394501540990172791707586109495200","date":"2026-01-08T07:25:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317728743427824902193327396184848291088","date":"2025-12-30T04:44:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-26T12:24:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-26T04:57:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-25T08:46:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-12-25T08:38:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"867e4e8a-6c6e-4b30-a281-0214b65c536a","owner":[],"postedDate":"December 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T13:00:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-29 13:00:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8200539","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8200539","identity":"rs-8200539","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}