Abstract Background Eucommia ulmoides Oliver is a dioecious plant with great economic value, but the underlying metabolite mechanism difference between male and female E. ulmoides remain poorly understood. Results In this study, we integrated a dual-platform metabolomics approach with transcriptomics to systematically analyze differences in metabolite and gene expression in the cortex and leaves of male and female E. ulmoides. As a result, 1,452 metabolites (603 volatile, 849 non-volatile) were characterized, and 156 and 129 differentially accumulated metabolites were identified in FEC vs. MEC and FEF vs. MEF, respectively. The expression of key genes (PLA2G4, DOXs, LOX2S, and CEQORH) involved in alpha-linolenic acid metabolism was significantly altered in MEC. Similarly, the expression of key genes (CHS, FLS, DFR, and ANR) involved in flavonoid biosynthesis was significantly altered in MEF. Conclusions Our findings shed light on the metabolite mechanism behind the metabolic differences between male and female E. ulmoides and contribute to the exploration of value utilization. Supplementary Information The online version contains supplementary material available at 10.1186/s12870-025-06575-x. Keywords: Eucommia ulmoides Oliver, Dioecious plant, Metabolomics, Transcriptomics Introduction Eucommia ulmoides Oliver (E. ulmoides), also known as Du Zhong, is a dioecious tree species of the family Eucommiaceae, which plays an important economic value role in traditional Chinese medicine (TCM) and edible plants. Meanwhile, according to the Chinese Pharmacopoeia, its medicinal parts are E. ulmoides Cortex (EC) and E. ulmoides Folium (EF), among which the cortex as medicine is the most important. Famous ancient medical books, including “Compendium of Materia Medica” and “Essential of Materia Medica”, have recorded that E. ulmoides cortex exhibited the effects of treating lumbago, knee pain, osteoporosis, liver protection, anti-aging, etc [[46]1]. Previous research has indicated that E. ulmoides contains a variety of nutritional and bioactive compounds, including lignans, flavonoids, phenols, lipids, terpenes and others. These diverse chemical compositions have endowed E. ulmoides with a series of remarkable pharmacological effects, which are manifested in effects such as lowering blood pressure, anti-tumor, and regulating the immune system [[47]2]. Moreover, as a typical medicinal and food resource, the flowers, leaves, cortex, and seeds of E. ulmoides have been developed into various herbal medicinal and food functional health products [[48]3]. E. ulmoides is a typical dioecious plant, exhibiting distinct morphological and physiological characteristics as a consequence of the presence of different sexes and sex chromosomes [[49]4]. It has been demonstrated that the total phenolic acid content and volatile components of male E. ulmoides exhibit notable differences from those of female E. ulmoides [[50]5]. Our previous research demonstrated that the application of the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) model to metabolite differences between males and females of E. ulmoides could effectively identify 13 and 12 major compounds as chemical marker constituents for the identification of male and female EC and EF [[51]6]. As a complex TCM, dioecious E. ulmoides presents diverse beneficial effects depending on its metabolites. Nevertheless, few studies have explored the differences in comprehensive metabolic profiling and the molecular mechanisms underpinning the metabolic diversity of male and female E. ulmoides. The functional role of metabolites is largely contingent upon their structural characteristics, making developing methods for the comprehensive characterization of metabolites a critical research priority. Metabolomics is a powerful tool for characterizing the metabolism of organisms and is used to compare metabolites and metabolic networks in plants from different geographical regions and species. The dual-platform full-spectrum metabolomics approach of Gas Chromatography-Tandem Mass Spectrometry (GC-MS/MS) and Liquid Chromatography/Tandem Mass Spectrometry (LC-MS/MS) has been widely used, and Yide Meng et al. comprehensively analyzed 193 metabolites from the leaf of E. ulmoides by using dual-platform non-targeted metabolomics [[52]7]. Similarly, the metabolites in the bark of A. salviifolium were comprehensively characterized, and 81 secondary metabolites were identified [[53]8]. It is noteworthy that the full-spectrum metabolome employs a dual extraction method to comprehensively cover both volatile and nonvolatile metabolites. This effectively addresses the limitations of the metabolome’s low effective flux and limited coverage, enabling a more comprehensive screening of effective differential metabolites and elucidating their mechanisms of investigation [[54]9]. Next-generation sequencing (NGS), also known as high-throughput sequencing, provides a means of identifying functional genes involved in metabolite biosynthesis [[55]10]. The advent of NGS has greatly facilitated research on sex determination in plants, and it has been widely employed to identify the differentially expressed genes (DEGs) involved in sex differentiation in garden asparagus [[56]11], Mulberry [[57]12], and Taxus trees [[58]13]. The integrated analysis of metabolomics and transcriptomics is an effective method for identifying metabolism-related functional genes and has been widely used to study the association between DEGs and differentially accumulated metabolites (DAMs) in plant metabolite biosynthetic pathways [[59]14, [60]15]. Accordingly, we employ an integrated transcriptomics and metabolomics analytical approach to comprehensively investigate the metabolic alterations and gene expression in male and female E. ulmoides. Overall, the present work not only contributes to a better unraveling of the underlying metabolite regulatory mechanism difference between male and female E. ulmoides, but also further explores the economic value of E. ulmoides. Materials and methods Chemicals and reagents A total of seventeen reference substances of chlorogenic acid (batch number Y20A11K111541), cryptochlorogenic acid (batch number P11M10L88123), neochlorogenic acid (batch number M07GB140938), geniposidic acid (batch number Z24A10 × 95926), Harpagide (batch number J22IB220115), Syringin (batch number Y14N11H131195), geniposide (batch number J21J9T66173), caffeic acid (batch number W16O10B100366), isoquercitrin (batch number X29O11Y128970), genipin (batch number P27N10L104331), ferulic acid (batch number JB255905), trans-coniferin (batch number P27N10L104331), pinoresinol diglucoside (batch number C19M11S109808), Asperuloside (batch number P05D10L105146), protocatechuic acid (batch number W10D10B104178), and aucubin (batch number S13F11H108635) were purchased from Shanghai Yuanye Bio-Technology Co. Ltd. (Shanghai, China). The purity of each reference standard was above 98%. The appropriate quantity of each standard substance was dissolved in methanol and prepared into a solution with a concentration of 1 mg/mL. The 17 standard solutions were then combined, filtered through a 0.22 μm filter, and analyzed by UPLC-MS/MS.Methanol, acetonitrile, and hexane of HPLC grade were achieved from Merck (Darmstadt, Germany), and Formic acid (LC-MS grade) was purchased from Aladdin (Shanghai, China). Sodium chloride was produced by Sinopharm Group Chemical Reagent Co., LTD (Shanghai, China). Ultrapure water was prepared using a Milli-Q water purification system (Millipore, Massachusetts, USA). A filter membrane of 0.22 μm pore size was purchased from ANPEL (Shanghai, China). Samples The E. ulmoides samples, the fresh natural 15-year-old male and female plants, were collected from production areas in Hu-nan Province, and were randomly selected from Zhangjiajie E. ulmoides Base in Hu-nan Province (110°49′30.28"E, 29°10′6. 24"N, 313 m), with three samples of male E. ulmoides cortex (MEC), three samples of female E. ulmoides cortex (FEC), three samples of female E. ulmoides folium (FEF), and three samples of male E. ulmoides folium (MEF) in the production area. Meanwhile, three biological replicates of E. ulmoides cortex and leaves were taken from each male and female. In total, twelve batches of E. ulmoides samples were collected. In addition, the male cortex and female cortex of E. ulmoides were abbreviated as MEC and FEC, respectively, and the male and female leaves of E. ulmoides only were abbreviated as MEF and FEF, respectively. All the herbal samples were authenticated by Professor Lu-ping Qin (School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou City, Zhejiang Province, P.R. China). Finally, the samples were deposited in the School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, with the code 330702190908179LY. Metabolomics analysis Headspace-solid phase Microextraction-gas Chromatography-mass spectrometry (HS-SPME-GC-MS) analysis For the HS-SPME-GC-MS analysis,500 mg of the powder was transferred immediately to a 20 mL head-space vial (Agilent, Palo Alto, CA, USA), containing NaCl saturated solution, to inhibit any enzyme reaction.20 µL of 3-hexanone was added as the internal standard (I.S.). The vials were sealed using crimp-top caps with TFE-silicone headspace septa (Agilent). At the time of SPME analysis, each vial was placed in 60 ℃ for 5 min, then a 120 μm Divinylbenzene/Carboxen/Polydimethylsiloxane (DVB/CWR/PDMS) fibre (Agilent) was exposed to the headspace of the sample for 15 min at 60 ℃. The identification and quantification of VOCs was carried out using an Agilent Model 7890B GC and a 7000D mass spectrometer (Agilent). After sampling, desorption of the VOCs from the fibre coating was carried out in the injection port of the GC apparatus at 250 ℃ for 5 min in the splitless mode. GC experimental conditions: 5% phenyl-polymethylsiloxane (DB-5MS) capillary column (30 m × 0.25 mm × 0.25 μm); He (purity: 99.99%) carrier gas; 1.2 mL/min carrier gas flow. The injector temperature was maintained at 250 ℃. The oven temperature was programmed from 40 ℃ (3.5 min), increasing at 10 ℃/min to 100 ℃, at 7 ℃/min to 180 ℃, at 25 ℃/min to 280 ℃, held for 5 min. MS experimental conditions: Mass spectra were recorded in electron impact (EI) ionisation mode at 70 eV. The quadrupole mass detector, ion source, and transfer line temperatures were set, respectively, at 150, 230, and 280 ℃. The MS selected ion monitoring (SIM) mode was used for the identification and quantification of analytes. Based on multiple databases, literature, some standard substance, and retention indices, the database was established independently, containing identified retention time (RT) as well as qualitative and quantitative ions for selective ion detection mode for accurate scanning, and identification of substances based on RT and qualitative ions [[61]16], the quantitative ions were selected for the integration of the chromatographic peaks and the calibration work. Ultra Performance Liquid Chromatography/Tandem Mass Spectrometry (UPLC-MS/MS) analysis For the UPLC-Q/TRAP-MS analysis, the E. ulmoides sample extraction procedures were mainly according to the modified protocols described by the optimal extraction conditions previously published [[62]6]. A solution of 50 mg of lyophilized powder in 1.2 mL of 70% methanol was prepared by vortexing for 30 s and subjected to ultrasonic extraction for 40 min at room temperature. Centrifuged at 12,000 rpm for 8 min at 4 ℃. The 1,000 µL supernatant was collected and lyophilized. The freeze-dried E. ulmoides sample was redissolved with methanol, filtered, and stored at -20 ℃ prior to analysis by UPLC-MS/MS. Three biological replicates were conducted for each group, and quality control (QC) samples were prepared by combining 10 µL of each E. ulmoides sample. The sample extracts were analyzed using an UPLC-ESI-MS/MS system (UPLC, ExionLC™ AD, [63]https://sciex.com.cn/; MS, Applied Biosystems 4500 Q TRAP, [64]https://sciex.com.cn/). UPLC experimental conditions: column, Agilent SB-C18 (1.8 μm, 2.1 mm * 100 mm); The mobile phase included solvent A (aqueous solution) and solvent B (acetonitrile, ACN), both with 0.1% formic acid, using the following gradient elution: 0–9 min, 5–95% B, 9–10 min, 95% B, 10–11.1 min 95%~5% B, and 11.1–14 min, 5% B. Flow rate 0.35 mL/min; column temperature 40 ℃; injection volume 2 µL. MS experimental conditions: source temperature 550 ℃; ion spray voltage (IS) 5500 V (positive ion mode)/-4500 V (negative ion mode); ion source gas I (GSI), gas II(GSII), curtain gas (CUR) were set at 50, 60, and 25 psi, respectively; the collision-activated dissociation(CAD) was high. Triple quadrupole (QQQ) scans were acquired as Multiple Reaction Monitoring (MRM) experiments with collision gas (nitrogen) set to medium. Declustering potential (DP) and collision energy (CE) for MRM transitions were done with further DP and CE optimization. A specific set of MRM transitions was monitored for each period according to the metabolites eluted within this period. Based on the above self-constructed database, substance characterization was performed based on RT and secondary spectral information, and the identified metabolites were quantified using MRM mode. After obtaining the metabolite profiling data of different samples, peak area integration was performed for all the substance chromatographic peaks, and the integration was corrected for the mass spectrometry (MS) peaks of the same metabolite in different samples among them. Data analysis of metabolomics The MS data were processed with Analyst 1.6.1 software. Based on the local metabolic database, the mass spectrometry qualitative and quantitative analysis of the metabolites of the samples was performed according to the information of the RT and peak type of the metabolites. The characteristic ions of each substance were screened out by QQQ, and the signal intensity (CPS) of the characteristic ions was obtained in the detector. The integration and correction of the chromatographic peaks were performed, and the peak area (Area) of each chromatographic peak represented the relative content of the corresponding substance. The specific method for identifying the chemical components of E. ulmoides, including lignans, phenylpropanoids, iridoids, and flavonoid metabolites, is as follows: Under MS collision energy and negative ion mode, lignans have favored the formation of two kinds of precursor ions including [M-H] − and [M + HCOO]−; subsequently, it was found that the neutral loss of one or two glucoside residues (glucoside bond cleavage) was a typical fragmentation mode for lignans, and resulted in highly abundant aglycone fragment ion [M-H-Glc] − or [M-H-2*Glc]−; Next, the neutral loss of a CH[3] group and CO molecules was successively further lost due to the presence of methylation and cracking of furan rings; following obtained corresponding feature fragment ion [M-H-Glc-CH3]−, [M-H-2*Glc-CO[2]]−, [M-H-Glc-CO]−, [M-H-2*Glc-CO]−, [M-H-2*Glc-CH[2]O]−, and [M-H-2*Glc-2*CH[2]O]−. Phenylpropanoid in E. ulmoides was a class of constituents composed of benzene rings and three straight carbon chains joined together as units (C6-C3) compounds, which could exist alone or in two or more C6-C3 groups, mainly including two categories: chlorogenic acid subtype and caffeic acid subtype. In addition, the main mass spectral cleavage path of the chlorogenic acid subtypes was the cleavage of the ester group between quinic acid and caffeic acid to form high-abundance fragment ions. The phenylpropyl iridoid was a cyclic monoterpene derivative containing three subtypes (including cyclopentane, cyclopentene, and epoxy cyclopentane), among which the cyclopentane subtype was dominant. In the MS negative ion mode, the iridoid predominantly formed precursor ions [M-H]⁻. Subsequently, it was observed that the neutral loss of the glucoside group was a typical fragmentation mode for iridoid, resulting in the formation of the aglycone fragment ion [M-H-Glc]⁻. Meanwhile, the neutral loss of an H[2]O group and CO[2] molecules was successively further lost due to the presence of hydroxyl group and cracking of esterification; following achieved corresponding feature ion [M-H-Glc-H[2]O]-, [M-H-Glc-CO[2]]⁻, and [M-H-Glc-H[2]O-CO[2]]-. Generally, flavonoids consist of two moieties: one aglycone and one or more glycosyl groups. At the MS/MS collision energy, the glucoside bond in the flavonoid or flavonoid glycoside frequently undergoes break cleavage, resulting in the neutral loss of glucose or rhamnose molecules. This generates high-abundance skeleton fragment ions, namely aglycone fragment ions [M-H-Glc]⁻ or [M-H-Rha]⁻. Subsequently, cleavage of the C-ring of the aglycone fragment ions was observed, accompanied by the continuous loss of CH[3], CO, H[2]O, and CHO groups, which resulted in the formation of a series of high-abundance characteristic ions. Subsequent analysis was conducted using R software, which enabled the generation of principal component analysis (PCA), OPLS-DA analysis, cluster heat map, and key metabolite Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway maps. Annotation and enrichment analysis of differential metabolites was conducted using the KEGG database. Metabolites with the absolute values of log[2]FoldChange (|log[2]FC|) ≥ 1 and the variable importance in projection (VIP) > 1 exceeding the threshold were identified as DAMs. Transcriptomic analysis (RNA extraction, cDNA library construction, and sequencing) Total RNA was extracted from frozen E. ulmoides samples of each group using the RNAprep PureLink Plant Total RNA Extraction Kit (Invitrogen, USA) according to the manufacturer’s instructions. Subsequently, the concentration and quality of the collected E. ulmoides RNAs were determined and established using the NanoDrop NC2000 ultraviolet-visible spectrophotometer (Thermo Fisher Scientific, Massachusetts, USA) and Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), respectively, and their integrity was detected by gel electrophoresis. Meanwhile, their integrity was verified by agarose electrophoresis. The mRNA with polyA tail was further extracted and enriched from total RNA by using poly-T oligo-attached magnetic beads. Meanwhile, poly(A) mRNA was broken into short fragments using an RNA fragmentation kit (Ambion, USA). Next, cDNA libraries were prepared using the Ultra II RNA Library Prep Kit for Illumina according to the manufacturer’s recommendations. Eventually, about paired-end 200 bp reads were sequenced by adopting an Illumina NovaSeq 6000 platform (Illumina Inc., San Diego, California, USA); then, PCR amplification and purification were further performed to obtain the library. Meanwhile, when the library was constructed, reverse-transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used for QC to ensure that the effective concentration of the library was higher than 2nM. Three biological replicates of each group of E. ulmoides samples were employed for transcriptome analysis. After the sequencing, initial data were obtained, and the fastq software was used to control the quality of the raw reads for the transcriptome analysis. The process involved removing connector sequences, low-quality bases (Q ≤ 20), reads containing polyN, and duplicates to obtain clean reads. The Trinity.2.11.0 software was used to assemble clean-read transcripts and the Corset system was employed for transcript clustering. Subsequent analyses were conducted on high-quality data. Data analysis of transcriptomics Gene expression was calculated using fragments per kilo bases per million fragments (FPKM) approach. Then, DESeq2 was used to perform differential expression analysis between sample groups to obtain differentially expressed gene sets between two biological conditions. After differential analysis, the Benjamini-Hochberg method was used to correct the hypothesis test probability (P-value) for multiple hypothesis testing to obtain the false discovery rate (FDR). The screening conditions for DEGs were |log[2]Fold Change| >= 1 and FDR < 0.05. Next, DIAMOND software [[65]17] was used to align the de-redundant transcript sequences with the Kyoto Encyclopedia of Genes and Genomes (KEGG) Ortholog database (KO, www, genome.jp/kegg), Non-Redundant Protein Sequence Database (NR, [66]www.ncbi.nlm.nih.gov), Swiss-Prot (a manually annotated and reviewed protein sequence database, [67]www.expasy.ch/sprot), Gene Ontology (GO, [68]http://www.geneontology.org/), Cluster of Orthologous Groups of proteins (COG/KOG), and Trembl databases. HMMER software was used to align the amino acid sequences with the protein family database (Pfams, [69]http://xfam.org/) to obtain seven database annotation information of the transcripts. Functional annotation of GO enrichment pathways was based on GO term analysis P < 0.05, and the number of enriched genes was used as a criterion to screen KEGG pathways and GO entries that were significantly enriched for DEGs. Correlation analysis of dams and DEGs KEGG pathway enrichment analysis was performed on DAMs and DEGs to identify KEGG pathways co-enriched by metabolomics and transcriptomics. Pearson correlation coefficients (PCC) were calculated using the Cor function in R using the constant values of genes and metabolites in all samples, and DAMs and DEGs with a |PCCs| > 0.80 and a P < 0.05 for each pathway were selected and visualized for DAM and DEG correlations using Cytoscape 3.10.0 software. Validation of RNA-Seq (RNA Sequencing) data by RT-qPCR To validate the E. ulmoides RNA-Seq data, a total of eight DEGs, including four DEGs (Cluster-189403.61035, Cluster-189403.44155, Cluster-253241.0, and Cluster-224308.0) involved in the alpha-Linolenic acid metabolism pathway, and other four DEGs (Cluster-189403.11461, Cluster-189403.46350, Cluster-189403.44480, Cluster-189403.43752) involved in the flavonoid biosynthesis pathway were selected to identify their expression patterns using RT-qPCR. The obtained cDNA was designed as a template for determining gene expression levels using specific primers for genes related to alpha-linolenic acid metabolism and flavonoid biosynthesis; thus, the actin RNA gene (unigene0020006) was used as an internal control gene (the relative quantitative reference gene). The primers employed in the RT-qPCR analysis were shown in Table [70]1. The SYBR Premix Ex TaqTM kit (TaKaRa, Japan) was used, then RT-qPCR expression assays were performed adopting an Applied Biosystems (ABI) 7500 Fast Real-Time PCR System platform (Thermo Fisher, USA). The thermal cycling conditions were as follows: held at 95℃ for 10 min, and 40 cycles of 95℃ for 10 s, followed by holding at 60℃ for 60 s. Three biological replicates were analyzed for each treatment. The 2-ΔΔCt method was used to calculate and analyze gene expression levels [[71]18]. Moreover, for more sensible statistical analyses, each clone presented three biological replicates, and three technical replicates were performed for each sample, presenting as means ± standard error (SE). The above data were collected to GraphPad Prism version 6.0; meanwhile, the DEGs between the two groups were compared and evaluated based on the student t-test. P-value < 0.05 as statistically significant. Table 1. The primers of genes validated by RT-qPCR analysis Gene ID Gene name Forward primer (5′–3′) Reverse primer (5′–3′) Cluster-189403.61035 LOX2S CGCCGCCTGAGCAACAAC ACCCTTTACCGTCACCGTATCC Cluster-189403.44155 OPR CGATAGGGAAGACGGAAACAAAGC CGGATTAGCCAAGAACCAACGC Cluster-253241.0 ACAA1 CCTTGACTGTTAAGTGGACGGATC AGAGACTCGGCTGTGATACCTTC Cluster-224308.0 PLA2G4 ATTGACAGCGTGGTTGAACTAGC CTTTCTCTCGTAAACTGCCCTGAC Cluster-189403.11461 FLS CAATCATTGGTCCTCTGCCTGTG CTTGTTGAATTTGTGGTGCCTGTAC Cluster-189403.46350 ANR CGAGTAATTGAGGGTTGTGATGGG TCAAGTAATTCTGCCTGCGGATC Cluster-189403.44480 CYP98A CTGAATGTGGTGGTGACGAGTTC AGTCCTATACCTATCCGCCAAGTG Cluster-189403.43752 DFR AAGCGAAGACGGTGAAGAGATTC CACTCCACATCGGTCCAACAAC Actin GGCAGTTTTTCCAAGTATTGTCGG CTTCATCCCCCACATAGGCATCC [72]Open in a new tab Results Structural Elucidation of chemical constituents of E. ulmoides by metabolomics combined with cleavage pathways The chemical constituents of E. ulmoides were systematically elucidated based on the dual platform of GC-MS/MS and UPLC-MS/MS to obtain as much volatile and non-volatile metabolite information as possible. The QC samples, prepared by pooling equal aliquots of each E. ulmoides group sample, were detected to confirm the repeatability, stability, and reliability of the dual-platform analytical method. The total ion chromatograms (TIC) of the QC samples overlapped well, and the relative standard deviation (RSD, %) was < 20%, indicating that these data of RT and MS intensity of the chromatographic peaks were almost consistent and reliable, as shown in Fig. [73]1A-C. After filtering the chromatographic peaks with unqualified repeatability, these were divided into 25 groups based on their structural types and diagnostic fragment ions. According to the high-resolution MS and MS/MS characteristic fragment ions, several new features of the fragmentation pattern of E. ulmoides chemical constituents, including lignans, phenylpropanoid, iridoid, and flavonoid, were characterized and summarized as follows. The EIC graphs of the target compound and its MS/MS graph compared with the reference compound are shown in Fig. [74]S1-[75]S2. Fig. 1. [76]Fig. 1 [77]Open in a new tab TIC of six QC sample injections. TIC of six QC sample injections of male and female E. ulmoides extracts in GC-MS/MS (A) and LC-MS/MS in positive (B) and negative (C) modes. Structural Elucidation and fragmentation patterns of furanoid lignans Lignans are a class of phenylpropyl derivatives. Single epoxy lignans and double epoxy lignans both contain four tetrahydrofuran rings. The cracking of the tetrahydrofuran rings would produce the characteristic fragment ion of m/z 151, distinguishing the new lignans from the single epoxy lignans and the double epoxy lignans. Taking the structural elucidation of Cpd 260 (pinoresinol-4,4’-O-di-O-glucoside) as an example, the molecular formula of Cpd 260 was deduced as C32H42O16 according to its [M-H] ion at m/z 681.2435. MS/MS data analysis of the [M-H] ion at m/z 681.2435 provided characteristic fragments. First, diagnostic ions at m/z 151.0446 were significantly observed, indicating that Cpd 260 is a lignan subtype. Second, two consecutive 162 gaps were observed from ion m/z 681.2435 to m/z 519 [M-H-Glc]^− and m/z 357 [M-H-2*Glc]^− due to the sequential neutral loss of two glucoside groups, indicating the presence of a hex-hex sugar chain on Cpd 260. Thirdly, in the high energy CID, the ions at m/z 327 [M-H-2*Glc-CH[2]O]^− and m/z 297 [M-H-2*Glc-2*CH[2]O]^− were obtained derived from the continuous cleavage of two tetrahydrofuran rings in the characteristic fragment ion m/z 357, which successively lost CH[2]O; then this information supported that Cpd 260 was diepoxide lignan. Whereafter, the diagnostic ion m/z 151 was further formed when one of the two tetrahydrofuran rings of fragment ion m/z 357 was completely broken, and finally, based on the above-detailed evidence, this Cpd 260 was assigned as pinoresinol diglucoside by corresponding reference standards through comparison of their high-resolution MS and MS/MS spectra and literature [[78]19], and its mass spectral cleavage pathway was shown in Fig. [79]2A. Fig. 2. [80]Fig. 2 [81]Open in a new tab MS/MS spectra and cleavage pathways of each representative constituent: Furanoid lignans representative: pinoresinol-4,4’-O-di-O-glucoside (A) and olivil (B); Phenylpropanoid representative: chlorogenic acid (C), neochlorogenic acid (D)) and cryptochlorogenic acid (E). Similarly, Cpd 245 (olivil) was tentatively assigned as a furanoid lignan with the formula C[20]H[24]O[7] based on its [M-H] precursor ion at m/z 375. On MS/MS analysis, the diagnostic ion m/z 151 was observed as evidence for the existence of the single epoxy lignan or double epoxy lignan subtype group. Secondly, the tetrahydrofuran ring in the structure was cleaved, forming the characteristic ion of m/z 151. Meanwhile, the fragment ion of m/z 195 lost one molecule of CH[2]O to obtain the characteristic ion of m/z 165, indicating that there was only one tetrahydrofuran ring. Thus, according to the MS/MS cleavage pathway analysis, Cpd 245 was identified as olivil by comparison with the corresponding literature [[82]19] (Fig. [83]2B). According to the above fragmentation pattern, the structure of furanoid lignans, including single epoxy lignan and double epoxy lignan subtypes, was systematically elucidated. These observations were also consistent with previous reports. Detailed data was shown in Table [84]S1. Structure Elucidation and fragmentation pattern of phenylpropanoid Structure elucidation of phenylpropanoids was similar to that of furanoid lignans. Taking the structural elucidation of Cpd 704 (chlorogenic acid) as an example, precursor ions of chlorogenic acid at m/z 353.0875 [M-H] easily generated a high abundance of characteristic ions m/z 191 and m/z 161 in the MS/MS spectra derived from the chlorogenic acid structure formed by the cleavage of the ester group between quinic acid and caffeic acid [[85]20]. Similarly, the MS/MS spectrum cleavage pathway of caffeic acid subtypes was a lost substituent group in the structure, such as the neutral loss CO[2] or H[2]O molecule. For example, precursor ions of caffeic acid (Cpd 802) at m/z 179 [M-H]^− in the MS/MS spectra produced a high abundance of characteristic ions m/z 135 derived from the neutral loss of carboxyl groups in caffeic acid [[86]21]. Hence, the diagnostic ions were m/z 191, m/z 179, m/z 161, and m/z 135, where ions m/z 191 and m/z 179 were the precursor ions of quinic acid and caffeic acid, respectively; subsequently, further loss of CO[2] or H[2]O molecule. It is noteworthy that the high-resolution MS/MS data were insufficient for differentiating between isomeric species. Therefore, it was also of great importance to achieve sufficient chromatographic separation of the isomeric species in our study. Our research revealed that the precursor ions of Cpd 704 (chlorogenic acid), Cpd 705 (neochlorogenic acid), and Cpd 706 (cryptochlorogenic acid) were all at m/z 353, suggesting the possibility of isomerism. However, Cpd 705 and Cpd 706 were observed less frequently or in low quantities in TCMs. Meanwhile, they consist of quinic acid combined with one to four residues of certain cinnamic acid, most commonly caffeic acid, p-coumaric acid, and ferulic acid. The structural differences between Cpd 704, Cpd 705, and Cpd 706 lie in the substitution sites of the hydroxyl group replaced on quinic acid [[87]22]. As an illustration, the characteristic ions m/z 191 and m/z 179 were initially observed in MS/MS data. Subsequently, the neutral loss of an H[2]O group was further lost due to the presence of a hydroxyl group in these compounds. This resulted in the generation of the characteristic ions m/z 191 of quinic acid, which were derived from the neutral loss of one H[2]O molecule (18 Da). Meanwhile, the characteristic ions m/z 179 of caffeic acid were readily generated, resulting in the formation of ion m/z 135 derived from the neutral loss of one decarboxylated molecule, which contributed an additional 44 Da. In light of the aforementioned high-resolution MS/MS data, it can be posited that Cpd 704, Cpd 705, and Cpd 706 are chlorogenic acid and its isomers. Secondly, the MS/MS spectra of Cpd 705 and Cpd 706 exhibited a high degree of consistency with those of the chlorogenic acid reference standard. The presence of ions m/z 191, m/z 179, and m/z 161 indicated that the structures of Cpd 705 and Cpd 706 were highly similar to that of chlorogenic acid. Concurrently, chlorogenic acid was synthesized through the esterification and condensation of the carboxyl group in caffeic acid and a hydroxyl group at position five in quinic acid. Similarly, at positions three and four, hydroxyl groups in the quinic acid structure underwent esterification. Consequently, neochlorogenic acid (the result of a condensation reaction between caffeic acid and a hydroxyl group at position three) and cryptochlorogenic acid (the result of a condensation reaction between caffeic acid and a hydroxyl group at position four) were identified in the MS/MS spectra. To further confirm the authenticity of these isomer compounds, we performed the aforementioned isomer confirmation by comparing them with the MS/MS characteristic ion information of the reference standard, the RT order of chromatographic peaks, and the corresponding literature [[88]22]. Meanwhile, the MRM transitions were also selected at m/z 353.1→179.0, 353.1→191.0, for neochlorogenic acid, 353.1→191.1, for chlorogenic acid, and 353.2→173.0, 353.2→179.0 for cryptochlorogenic acid [[89]23]. In light of the aforementioned evidence, Cpd 704, Cpd 705 and Cpd 706 were respectively assigned as Cpd 706 chlorogenic acid (i.e., 3-caffeoyl quinic acid), Cpd 705 neochlorogenic acid (i.e., 5-caffeoyl quinic acid), and Cpd 706 cryptochlorogenic acid (i.e., 4-caffeoyl quinic acid), and its mass spectrum cleavage pathway was illustrated in (Fig. [90]2C-E). Structural Elucidation and fragmentation pattern of iridoid The structural elucidation of Cpd 810 (geniposidic acid) serves as an illustrative example. Based on the [M-H]- ion at m/z 373, Cpd 810 was tentatively assigned as an iridoid subtype with the formula of C[16]H[22]O[10]. Firstly, MS/MS analysis of the [M-H]⁻ ion at m/z 373 provided abundant structural fragment ion information, and a 162-Da gap from m/z 373 to m/z 211 [M-H-Glc]⁻ was observed, providing evidence of the existence of the glucose structure. Secondly, the cracking of Cpd 810 in the cyclohexane moiety resulted in the formation of a characteristic ion at m/z 123, which indicated that Cpd 810 belonged to the iridoid skeleton structure. Thirdly, the ion at m/z 211 continued the neutral loss of H₂O and CO₂ to form fragment ions at m/z 193 [M-H-Glc-H₂O]⁻, m/z 167 [M-H-Glc-CO₂]⁻, and m/z 149 [M-H-Glc-H₂O-CO₂]⁻, indicating the presence of hydroxyl (OH) and carboxyl (-COOH) groups in the structure of Cpd 810. Ultimately, a combined analysis of the MS/MS data from the reference standard and existing literature led to the identification of Cpd 810 as geniposidic acid, with the mass spectrum cleavage pathway illustrated in Fig. [91]3A. Fig. 3. [92]Fig. 3 [93]Open in a new tab MS/MS spectra and cleavage pathways of each representative constituent: Iridoid representative: geniposidic acid (A), geniposide (B), and aucubin (C); Flavonoids representative: rutin (D) and hyperinand (E); Other classification metabolitess representative: quinic acid (F) Similarly, Cpd 811 (geniposide) was identified as an iridoid subtype with the formula C[17]H[24]O[10] based on the observation of a [M + H]^+ ion at m/z 389. The characteristic ion m/z 123 was achieved, indicating that Cpd 811 belonged to an iridoid skeleton structure. Subsequently, the high-resolution MS/MS spectra revealed the presence of ions at m/z 227 [M-H-Glc]⁻ and m/z 209 [M-H-GLC-H[2]O]⁻, which were obtained by the precursor ion successively losing a group of 162 gaps and an H[2]O molecule. This indicates the presence of a glucose group and a hydroxyl (OH) group in the structure of Cpd 811. In light of the aforementioned evidence, Cpd 811 was identified as geniposide through a comparison with an authentic standard, and its mass spectrum cleavage pathway is illustrated in Fig. [94]3B. Another typical iridoid is exemplified by the identification of Cpd 808 (aucubin). The precursor ion at m/z 345 of Cpd 808 generated the fragment ions m/z 183 [M-H-Glc]⁻ and m/z 165 [M-H-Glc-H₂O]⁻, indicating the presence of a neutral loss of 162 Da and 18 Da derived from a molecule of hexose and a hydroxy group, respectively. Moreover, the cracking of Cpd 808 cyclohexane resulted in the formation of a characteristic ion at m/z 123, which is indicative of an iridoid. Consequently, Cpd 808 was identified as aucubin, and its mass spectrum cleavage pathway is illustrated in Fig. [95]3C. Structural Elucidation and fragmentation pattern of flavonoids Cpd 205 (rutin) served as a prototypical example to elucidate the structural identification of flavonoids in E. ulmoides. Based on its [M + H]^+ ion at m/z 611, the molecule formula of Cpd 205 was deduced to be C[27]H[29]O[16]. In the MS/MS analysis, two consecutive 154 gaps losses from m/z 611 to m/z 465 [M + H-Glc]⁻ and m/z 303 [M + H-2*Glc]⁻ were achieved as a result of the sequential loss of two glucose groups. Subsequently, the aglycon fragment ion was observed at m/z 303, and further cleavage of H₂O and CO was observed at m/z 285 [M + H-Glc-H₂O]⁻ and m/z 257 [M + H-Glc-H₂O-CO]⁻. Moreover, the distinctive ion at m/z 151 was identified as the RDA cleavage of the aglycone fragment. In light of the aforementioned evidence, Cpd 205 was identified as rutin through a comparison with relevant literature, and its mass spectrum cleavage pathway is illustrated in Fig. [96]3D. Similarly, the precursor ion at m/z 463 of Cpd 152 (hyperin) C[21]H[20]O[12] yielded the fragment ions m/z 301 [M-H-Glc]⁻, m/z 283 [M-H-Glc-H[2]O]- and m/z 255 [M-H-Glc-H[2]O- CO]⁻, indicating the presence of neutral loss of 162 Da, 18 Da, and 28 Da, respectively, derived from a molecule of hexose, a hydroxy, and a carbanyl group. Furthermore, the diagnostic ion for iridoids at m/z 123 was also observed. Therefore, Cpd 152 was identified as hyperin, and its mass spectrum cleavage pathway is illustrated in Fig. [97]3E. Structural Elucidation and cragmentation pattern of other classification metabolites In addition to the aforementioned classification metabolites, other metabolites were identified in E. ulmoides, including phenolic acids (quinic acid, ferulic acid, caffeic acid, and protocatechuic acid), nucleotides and derivatives (adenosine), and organic acids (citric acid). To illustrate, the MS/MS fragmentation pathways of typical Cpd 539 (quinic acid) were identified. This can be exemplified by a comparison of quinic acid (Cpd 539), which presents a [M-H]⁻ ion at m/z 191 under high-resolution MS/MS analysis. Subsequently, the neutral loss of CO₂ and H₂O was observed to occur successively at m/z 173, m/z 127, and m/z 109 in the precursor ions, indicating the presence of three or more hydroxyl groups (-OH) and a carboxyl group (-COOH). Comparing the literature reported, Cpd 539 was identified as quinic acid [[98]24] (Fig. [99]3F). In addition to the initial classification of metabolites, further comprehensive profiling was conducted based on the high-resolution MS/MS data. As an illustration, the MS/MS spectrum revealed the presence of ferulic acid, caffeic acid, and protocatechuic acid of the phenolic acids residue ion at m/z 193 [M-H]⁻, m/z 179 [M + H]^+, and m/z 153 [M-H]⁻, respectively. Additionally, adenosine and citric acid residue ions at m/z 268 [M + H]^+ and m/z 191 [M-H]⁻ were successfully characterized in the MS/MS data. It is noteworthy that 30 representative metabolites are presented in Table [100]2, while the detailed data for the remaining metabolites can be found in Table [101]S1. Table 2. 30 Representative metabolites No. RT (min) Measured mass(m/z) Theoretical mass(m/z) Formula Ionization model Compounds Class DP CE MS/MS 1 1.94 166.0863 165.0705 C9H11NO2 [M + H]+ L-Phenylalanine Amino acids and derivatives 20 20 120.0803、103.0540 2 2.47 203.0826 204.0899 C11H12N2O2 [M-H]- L-Tryptophan Amino acids and derivatives -40 -20 186.0595、159.0963、116.0513 3 5.03 287.0561 288.0634 C15H12O6 [M-H]- Eriodictyol (5,7,3’,4’-Tetrahydroxyflavanone) Flavonoids -40 -40 151.0060、135.0470 4 5.68 285.0405 286.0477 C15H10O6 [M-H]- Kaempferol (3,5,7,4’-Tetrahydroxyflavone) Flavonoids -40 -30 257.0480、211.0422、151.0049 5 4.19 449.1078 448.1006 C21H20O11 [M + H]+ Kaempferol-3-O-glucoside (Astragalin) Flavonoids 50 30 287.0635、127.0389、85.0286 6 5.54 271.0612 272.0685 C15H12O5 [M-H]- Naringenin (5,7,4’-Trihydroxyflavanone) Flavonoids -40 -30 243.1867、151.0137、107.0158 7 5.08 303.0499 302.0427 C15H10O7 [M + H]+ Quercetin Flavonoids 40 40 303.0503、153.0176、137.0215 8 3.65 463.0882 464.0955 C21H20O12 [M-H]- Quercetin-3-O-galactoside (Hyperin) Flavonoids -60 -40 301.0391、255.0325、151.0065 9 3.98 463.0882 464.0955 C21H20O12 [M-H]- Quercetin-3-O-glucoside (Isoquercitrin) Flavonoids -50 -30 300.0293、271.0255、151.0372 10 3.78 611.1579 610.1534 C27H30O16 [M + H]+ Quercetin-3-O-rutinoside (Rutin) Flavonoids 50 30 303.0578、129.055、153.2392 11 3.19 177.0193 178.0266 C9H6O4 [M-H]- Esculetin (6,7-Dihydroxycoumarin) Lignans and Coumarins -40 -20 149.0311、133.0363、105.0406 12 4.04 375.1449 376.1522 C20H24O7 [M-H]- Olivil Lignans and Coumarins -80 -20 360.1261、179.0732、151.0384 13 3.34 681.2400 682.2473 C32H42O16 [M-H]- Pinoresinol-4,4’-O-di-O-glucoside Lignans and Coumarins -40 -20 519.1884、357.1356、151.0399 14 11.24 279.2330 280.2402 C18H32O2 [M-H]- Linoleic acid Lipids -50 -10 279.2377、261.2262 15 10.66 277.2173 278.2246 C18H30O2 [M-H]- α-Linolenic Acid Lipids -20 -20 253.0519、151.0052 16 2.55 153.0193 154.0266 C7H6O4 [M-H]- 3,4-Dihydroxybenzoic acid (Protocatechuic acid) Phenolic acids -50 -18 109.0295 17 3.38 179.0350 180.0423 C9H8O4 [M-H]- Caffeic acid Phenolic acids -20 -20 135.0459、117.0367、107.0523 18 2.74 353.0878 354.0951 C16H18O9 [M-H]- Chlorogenic acid (3-O-Caffeoylquinic acid) Phenolic acids -20 -20 191.0570、179.0359、135.0458 19 2.72 341.1242 342.1315 C16H22O8 [M-H]- Coniferin Phenolic acids -60 -20 179.0352、135.0455 20 2.92 353.0878 354.0951 C16H18O9 [M-H]- Cryptochlorogenic acid (4-O-Caffeoylquinic acid) Phenolic acids -40 -40 191.0619、179.0348、173.0450、135.0450 21 4.03 193.0506 194.0579 C10H10O4 [M-H]- Ferulic acid Phenolic acids -20 -20 178.0282、149.0611、134.0390 22 3.92 515.1195 516.1268 C25H24O12 [M-H]- Isochlorogenic acid A Phenolic acids -40 -30 353.0881、191.0583、179.0371、135.0466 23 2.58 353.0878 354.0951 C16H18O9 [M-H]- Neochlorogenic acid (5-O-Caffeoylquinic acid) Phenolic acids -60 -31 191.0637、179.0395、161.0264、135.0495 24 3.79 165.0546 164.0474 C9H8O3 [M + H]+ p-Coumaric acid Phenolic acids 20 30 147.0412、119.5040 25 2.76 371.1348 372.1420 C17H24O9 [M-H]- Syringin Phenolic acids -60 -20 209.0803、194.0567 26 3.78 577.1352 578.1424 C30H26O12 [M-H]- Procyanidin B4 Tannins -60 -30 407.0802、289.0737 27 1.85 345.1191 346.1264 C15H22O9 [M-H]- Aucubin Terpenoids -80 -13 183.0662、121.0292 28 3.40 227.0914 226.0841 C11H14O5 [M + H]+ Genipin Terpenoids 50 30 149.0595、103.0543 29 3.31 389.1442 388.1369 C17H24O10 [M + H]+ Geniposide Terpenoids 60 20 227.0769、209.0808 30 2.15 373.1140 374.1213 C16H22O10 [M-H]- Geniposidic acid Terpenoids -20 -30 211.0644、167.0732、149.0642 [102]Open in a new tab Chemical charaterization profiling of male and female E. ulmoides Following the comprehensive structural characterization, a total of 1,452 metabolites were identified in the E. ulmoides sample, comprising 603 volatile and 849 non-volatile metabolites. A total of 25 classes of homogeneous metabolites were identified in both the male and female cortex and leaves of E. ulmoides. The top eight classes of significant metabolites accounted for 66.32% of the total, consisting of terpenoids (10.67%), phenolic acids (10.40%), lipids (10.33%), flavonoids (8.82%), others (7.02%), esters (6.54%), heterocyclic compounds (6.47%), and organic acids (6.06%). In addition, the nine classes of metabolite that constituted less than 5% of each species were listed as follows: alkaloids, lignans and coumarins, hydrocarbons, amino acids and derivatives, alcohols, aldehydes, ketones, nucleotides and derivatives, and aromatics. Meanwhile, the remaining eight species of metabolites, including amines, phenols, acids, nitrogen compounds, tannins, halogenated hydrocarbons, ethers, and sulfur compounds, collectively account for less than 1% of the total identified metabolites. The comprehensive metabolite composition data is presented in Fig. [103]4A. Subsequently, the aforementioned metabolite was utilized to generate a cluster of the heatmap through the Metware Cloud ([104]https://cloud.metware.cn). The results of the heat map distribution demonstrate a pronounced disparity in metabolite composition between the bark and the leaves of E. ulmoides. Furthermore, the metabolites of the two E. ulmoides tissue types exhibit a notable divergence with regard to sex differentiation. (Fig. [105]4B) Fig. 4. [106]Fig. 4 [107]Open in a new tab Chemical characteristics profiling of male and female E. ulmoides. (A) Primary substance classification loop diagram of all metabolites. (B) Cluster heatmap analysis revealed the differential metabolites present in the male and female E. ulmoides. Primary substance classification pie chart and histogram of volatile metabolites detected by the GC-MS (C), non-volatile metabolites detected by the UPLC-MS/MS (D). (E) The PCA score plots of the male and female E. ulmoides For GC-MS analysis, a total of 603 volatile metabolites of E. ulmoides were classified into 16 categories including 112 terpenes, 95 esters, 94 heterocyclic compounds,70 hydrocarbons, 55 alcohols, 49 aldehydes, 48 ketones, 31aromatic hydrocarbons, 14amines, 9 phenols, 7 acids, 5 nitrogen compounds, 4 halogenated hydrocarbons, 4 others, 3 ethers, and 3 sulfur compounds. Of these, 18.57% were identified as terpene species, representing the most abundant class of volatile metabolites, followed by 15.75% heterocyclic species (Fig. [108]4C and Table [109]S1). For LC-MS results, a total of 849 non-volatile metabolites were divided into 13 categories including 151 phenolic acids, 150 lipids, 128 flavonoids, 98 others, 88 organic acids, 64 amino acids and derivatives, 47 nucleotides and derivatives, 43 terpenoids, 40 alkaloids, 36 lignans and coumarins, and 4 tannins. Among these, the number of phenolic acid species was proportioned to 17.79%, representing the most abundant class of non-volatile metabolites, followed by 17.67% lipids species (Fig. [110]4D and Table [111]S1). To compare the different compositions of the MEC and FEC compounds, our analysis revealed that 570 volatile metabolites and 775 non-volatile metabolites were detected in both MEC and FEC samples. Moreover, for the unique compound, a total of 10 volatile metabolites and 23 non-volatile metabolites were particularly found in MEC sample; while a total of 5 volatile metabolites and 16 non-volatile metabolites were exclusive to the FEC sample (Table [112]S2). Similarly, a total of 570 volatile metabolites and 775 non-volatile metabolites were detected in both MEF and FEF samples. Of note, for the unique compound in the MEF samples, 1 volatile metabolite and 28 non-volatile metabolites were exclusively identified; while a total of 5 volatile metabolites and 9 non-volatile metabolites were demonstrated to uniquely present in the FEF sample (Table [113]S3). The resulting metabolite composition data sets were subjected to PCA, an unsupervised model recognition approach, was employed to visualize the overall clustering trend of different sample groups and the degree of variation of samples within the same group. The PCA results indicated that all E. ulmoides samples could be distinctly classified into four groups based on the UPLC-MS/MS and GC-MS dual-platform data, indicating that the metabolic profiles exhibited notable differences in both male and female E. ulmoides, irrespective of the leaves or cortex parts. The clustering of three biological replicates within each E. ulmoides group confirmed the reproducibility and reliability of the dual-platform analytical method. It is noteworthy that the male and female E. ulmoides cortex samples were distinctly separated according to the second principal component (PC2). Similarly, the male and female E. ulmoides leaf samples were also distinguished according to the PC2. The PCA results thus elucidated a clear discrimination in metabolic profiles between male and female E. ulmoides (Fig. [114]4E). Analysis of significant differentially accumulated metabolites analysis between male and female E. ulmoides To further explore the significant differential metabolite results, a supervised OPLS-DA was carried out to identify the DAMs that contribute to distinguishing the cortex and leaves between male and female E. ulmoides. The OPLS-DA model scoring plots of pairwise comparisons demonstrated that the male and female E. ulmoides samples were significantly separated, irrespective of whether the samples were taken from the cortex or the leaves of E. ulmoides (Fig. [115]5A and B). Notably, these results were consistent with those of PCA, in line with expectations. Meanwhile, the permutation test of 200 iterations demonstrated that both R^2 Y = 1 and Q^2 > 0.95, indicating that the OPLS-DA model presents a high predictive accuracy (Fig. [116]S3 and Fig. [117]S4). Furthermore, the variable importance in the projection (VIP) value, with a VIP > 1.0 and a log[2]FC ≥ 1, was employed to identify the significant DAMs within each E. ulmoides group pairwise comparison (Table [118]S4). The detailed results are as follows: Fig. 5. [119]Fig. 5 [120]Open in a new tab Significant differential accumulated metabolites (DAMs) analysis in the male and female E. ulmoides. The OPLS-DA score plots of FEC vs. MEC (A), and FEF vs. MEF (B) comparisons. Volcano plot of metabolites in FEC vs. MEC (C) and FEF vs. MEF (D) comparisons. Cluster heatmap analysis of DAMs in FEC vs. MEC (E) and FEF vs. MEF (F) comparisons. KEGG enrichment analysis of DAMs in FEC vs. MEC (G) and FEF vs. MEF (H) comparisons The MEC sample presented 156 DAMs relative to the FEC sample (36 volatile metabolites, 120 non-volatile metabolites), among which 117 DAMs were up-regulated (19 volatile metabolites, 98 Non-volatile metabolites), and 39 DAMs were down-regulated (17 volatile metabolites, 22 Non-volatile metabolites) (Fig. [121]5C). As illustrated in Fig. [122]5E the MEC sample exhibited a general up-regulation trend of DAMs relative to the FEC sample, particularly in the DAMs of lipids and most terpenes, which constituted a significant proportion in all comparisons. However, only a small number of flavonoids, esters, phenolic acids, terpenes, and organic acids exhibited a tendency towards down-regulation. Similarly, the MEF sample displayed 129 DAMs in comparison to the FEF sample (11 volatile metabolites, 118 non-volatile metabolites), with 106 DAMs were up-regulated (5 volatile metabolites, 101 non-volatile metabolites), and 23 DAMs were down-regulated (6 volatile metabolites, 17 non-volatile metabolites) (Fig. [123]5D). As illustrated in Fig. [124]5F, the MEF sample exhibited a general up-regulation trend of DAMs relative to the FEF sample, particularly up-regulation of DAMs of the majority of flavonoids and phenolic acids, which accounted for a significant proportion in all comparisons. However, only a few amino acids and their derivatives, lipids, organic acids, phenolic acids, and terpenoids demonstrated a trend of down-regulation. All DAMs were classified into different known metabolic pathways to get a comprehensive overview of the complex biological behavior variations. A KEGG enrichment analysis was conducted to elucidate the functional roles of DAMs in the two comparisons of FEC vs. MEC and FEF vs. MEF. Based on the screening criterion of P-value ≤ 0.05, a total of three significantly enriched metabolic pathways were identified as the most abundant in both comparisons from FEC vs. MEC (Fig. [125]5G). Of these, the pathways with P-value ≤ 0.01 were alpha-linolenic acid metabolism, linoleic acid metabolism, flavone, and flavonol biosynthesis. The alpha-linolenic acid metabolism pathway was identified as the most significant metabolic pathway for the DAMs. Similarly, a total of 11 metabolic pathways were identified as the most abundant in both comparisons from FEF vs. MEF according to the screening criterion of P-value ≤ 0.05 (Fig. [126]5H). The metabolic pathways with P-value ≤ 0.01 were found within the metabolic terms “flavonoid biosynthesis,” “glutathione metabolism,” “D-amino acid metabolism,” “flavone and flavonol biosynthesis,” and “biosynthesis of amino acids.” These pathways were the most significantly enriched in both comparisons. Among these, the flavonoid biosynthesis pathway was identified as the most significant pathway for differential metabolites. Transcriptomic profiling of male and female E. ulmoides Sequencing was performed on Illumina’s high-throughput sequencing platform. Sequence data that support the findings of this study have been deposited in the NCBI repository, with the primary accession code PRJNA1206689. FastQC analysis showed that the Q30 scores of all samples ranged from 90.71% to 93.99%, and the guanine-cytosine (GC) content was ≥ 45.88% (Table [127]S5), indicating the high quality of the sequencing data. Whereafter trimming and filtering low-quality sequencing reads, clean reads were acquired as follows: an average of 47,862,670 and 45,567,580 clean reads respectively corresponding to the MEC and FEC samples, and an average of 50,075,090 and 43,681,440 clean reads respectively corresponding to the MEF and FEF samples. Thus, a total of 603,893 transcripts and 429,028 unigenes were successfully mapped by the assembly, and the average lengths of the assembled genes were 473 bp and 567 bp, with N50 of 835 bp and 990 bp, and N90 of 238 bp and 265 bp, respectively (Fig. [128]S5). To investigate the functional annotation of all the unigenes in E. ulmoides, we conducted a comparative analysis with various databases, including KEGG, NR, Swiss-Prot, TrEMBL, KOG, GO, and Pfam database using HMMER V3.2 software ([129]http://hmmer.org/). Based on the reference transcriptome, the numbers of unigenes were 157,791 (36.78%), 215,329 (50.19%), 110,061 (25.65%), 166,521 (38.81%), 137,853 (32.13%), 148,932 (34.71%), and 126,148 (29.40%), respectively, corresponding to KEGG, NR, Swiss-Prot, TrEMBL, KOG, GO, and Pfam database. Among them, a comparison of the data in NR gene database revealed that the highest match with E. ulmoides was Quercus suber (63.64%), followed by Vitis vinifera (2.49%) (Fig. [130]6A). Fig. 6. [131]Fig. 6 [132]Open in a new tab Transcriptomic profiling of male and female E. ulmoides. (A) Taxonomic statistical map of species compared with NR database. Volcano plot of annotated genes in FEC vs. MEC (B) and FEF vs. MEF (C) comparisons. GO enrichment analysis of DEGs in FEC vs. MEC (D) and FEF vs. MEF (E) comparisons. Bubble chart (a) and circle diagrams (b) of KEGG enrichment analysis of DEGs in FEC vs. the MEC sample (F) and FEF vs. MEF (G) comparisons DEGs in male and female E. ulmoides To illustrate the sex-specific gene expression, the clean reads of the male and female E. ulmoides samples were each grouped into one dataset for comparative digital expression analysis. Adopting the criteria of an adjusted P-value (padj) < 0.05 and a minimal twofold difference in expression i.e., log2(fold change value) > 1 to screen and obtain DEGs, we identified 21,472 DEGs between the MEC and FEC sample, of which 14,766 DEGs were up-regulated and 6706 DEGs were down-regulated in the MEC sample compared with the FEC sample, respectively (Fig. [133]6B). Similarly, there were 3,554 DEGs between the MEF and FEF samples, of which 1,395 DEGs were up-regulated and 2,159 DEGs were down-regulated in the MEF sample compared to the FEF sample (Fig. [134]6C). All of these genes were up- (log2(FC) > 2) or down-regulated (log2(FC) < -2). To better understand the regulation patterns of these significant DEGs, the DEGs in male and female E. ulmoides were further compared and classified by GO enrichment and KEGG pathway analysis databases to analyze their functions and metabolic processes. All DEGs of FEC vs. MEC and FEF vs. MEF were separately subjected to GO functional annotation, and the differential genes were mainly involved in three categories: “biological processes”, “cellular components”, and “molecular functions”. A total of 59 descriptions of enriched GO terms were identified in the MEC vs. FEC sample, including biological processes (27 pathways), cellular components (18 pathways), and molecular functions (14 pathways) (Fig. [135]S6). Similarly, a total of 52 descriptions of GO terms were identified in the MEF vs. FEF sample, including biological processes (27 pathways), cellular components (14 pathways), and molecular functions (11 pathways) (Fig. [136]S7). Meanwhile, for DEGs concerning biological processes, a large number of DEGs in FEC vs. MEC sample and FEF vs. MEF samples were mainly focused on the cellular process, metabolic process, and response to a stimulus. In terms of cellular components, DEGs were predominantly focused on cells, cell parts, and organelles. In terms of molecular functions, DEGs were mainly concentrated in binding and catalytic function and their activity. Among these genes, GO enrichment analysis was further performed to elucidate the functional differences of the sample genes. The enriched GO terms in FEC vs MEC sample was shown in Fig. [137]6D, “response to high light intensity”, “RNA-directed 5’-3’ RNA polymerase activity” and “hydrogen peroxide catabolic process” were the main terms. The majority of DEGs in FEF vs MEF sample were mapped to “response to chitin”, “anchored component of membrane”, “glutathione transferase activity”, “glutathione transferase activity” and “flavonoid metabolic process” (Fig. [138]6E). The KEGG annotation results demonstrated that all the DEGs were distributed into five categories: “cellular processes, environmental information processing, genetic information processing, metabolism, and organic systems”. There were 118 pathways in the MEC and FEC sample, among which 89 pathways were included in metabolism and 20 were related to genetic information processing. The majority of DEGs were thus assigned to metabolic pathways (Fig. [139]S8). MEF and FEF had a total of 60 pathways, of which 41 were metabolic pathways and 10 were genetic information processing-related pathways. And their DEGs were mainly concentrated in metabolic pathways (Fig. [140]S9). Then, KEGG significance enrichment analysis was performed, which revealed that protein processing in the endoplasmic reticulum and glycolysis/gluconeogenesis pathways were the top two significantly enriched pathways with the smallest P-value in MEC and FEC samples (Fig. [141]6F(a)). Notably, the number of DEGs involved in metabolic pathways was the highest, including “alpha-linolenic acid metabolism”, “fructose and mannose metabolism”, “pyruvate metabolism”, “riboflavin metabolism”, and so on (Fig. [142]6F(b)). Similarly, the MEF and the FEF samples exhibited biosynthesis enrichment in “Indole alkaloid biosynthesis” and “flavonoid biosynthesis” (Fig. [143]6G(a)), as well as a notable abundance of DEGs involved in “secondary metabolite biosynthesis” and “plant-pathogen interaction pathways”, all of which were significantly enriched (Fig. [144]6G(b)). Integrated analysis of dams and DEGs To investigate the association between DEGs and DAMs in two types of E. ulmoides, an integrated analysis was performed. First, the DAMs and DEGs were subjected to KEGG pathway enrichment analysis to identify the KEGG pathways that were co-enriched by metabolomics and transcriptomics. A total of 26 pathways (Fig. [145]7A) and 49 enrichment pathways (Fig. [146]7B) were identified as co-enriched by DAMs and DEGs in FEC vs. MEC and FEF vs. MEF, respectively. Among them, the most significant pathways enriched by DAMs in the cortex and leaves of E. ulmoides were alpha-linolenic acid metabolism and flavonoid biosynthesis, respectively. Next, the discussion of correlation analysis was focused on the metabolites and genes involved in the two pathways of “alpha-linolenic acid metabolism” and “flavonoid biosynthesis”. Fig. 7. [147]Fig. 7 [148]Open in a new tab Integrated analysis of DAMs and DEGs. KEGG enrichment analysis of co-enrichment DAMs and DEGs in contrast to FEC vs MEC. (A) and FEF vs MEF (B). The correlation network of the DEGs and DAMs involved in alpha-linolenic acid metabolism (C) and flavonoid biosynthesis (D). (The DEG-DAM pairs with |PCCs| ≥ 0.80 and P-value < 0.05 were adopted to produce the network diagram. Red and green edges represent positive and negative correlations, respectively. Yellow and green circles represent up-regulated and down-regulated metabolites, and pink and blue squares represent up-regulated and down-regulated genes, respectively). The RT-qPCR validation of significant DEGs involved in alpha-Linolenic acid metabolism pathway (E) and flavonoid biosynthesis pathway (F). (Data are shown as mean ± standard deviation of three biological replicates.) The correlations were represented by network diagrams, DAMs and DEGs with |PCCs| ≥ 0.80 and P-value < 0.05 in each pathway were selected for plotting (Table [149]S6). The results revealed 26 and 27 correlation pairs in male and female E. ulmoides, respectively. This requires the investigation and understanding of the regulatory mechanisms underlying these processes. As shown in Fig. [150]7C, 5 DAMs and 13 DEGs in alpha-linolenic acid metabolism (ko00592) showed significant strong correlations, in which the up-regulated genes “ACAA1 (Cluster-253241.0)”, “PLA2G4 (Cluster-224308.0)” and DAMs “17-hydroxylinolenic acid”, “2R-hydroxy-9Z,12Z,15Z-octadecatrienoic acid”, “9-hydroxy-12-oxo-15(Z)-octadecatrienoic acid” were positively correlated; and with down-regulated genes “LOX2S (Cluster-189403.61035)”, was negatively correlated with “α-linolenic acid”. The “Cluster-189403.63555”, “Cluster-213161.1” in mixed-regulated genes “CEQORH” and “Cluster-190728.0” in “OPR” were negatively correlated with α-linolenic acid, and “Cluster-190728.0” was negatively correlated with “9-hydroxy-10,12,15-octadecatrienoic acid”, and the rest of the genes were positively correlated with DAMs. Similarly, as shown in Fig. [151]7D, the correlation analysis between 9 DAMs and 18 DEGs involved in flavonoid biosynthesis (ko00941) showed significantly strong correlations, and in which the up-regulated genes “CHS (Cluster-189403.2317)”, “C12RT1 (Cluster-189403.11393)” and “DFR (Cluster-189403.107889)” were involved with the DAMs “epicatechin”, “gallocatechin”, “catechin phloretin”, “quercetin eriodictyol”, “aromadendrin” and “prunin” were positively correlated. Meanwhile, the down-regulated gene “FLS (Cluster-189403.11461)” showed a positive correlation with the following DAMs: “vitexin”, “eriodictyol”, “aromadendrin”, “prunin”, “epicatechin”, “catechin”, “phloretin”, and “gallocatechin”. In addition, the up-regulated gene “CYP73A (Cluster-189403.35596)” and the mixed-regulated gene “HCT (Cluster-189403.57650)” were negatively correlated with quercetin, eriodictyol, aromadendrin, and prunin. The mixed regulatory genes “DFR” and “HCT” were mostly positively associated with metabolites. Expression analysis of genes through RT-qPCR method To validate the accuracy of the RNA-seq results, we used RT-qPCR to evaluate the expression of four DEGs associated with the alpha-linolenic acid metabolism and flavonoid biosynthesis pathway. Four DEGs associated with the alpha-linolenic acid metabolism pathway (LOX2S, Cluster-189403.61035; OPR, Cluster-189403.44155; ACAA1, Cluster- 253241.0; and PLA2G4, Cluster-224308.0) was validated by RT-qPCR (Fig. [152]7E). Our observations revealed that the DEGs of LOX2S, OPR, ACAA1, and PLA2G4 compared with FEC sample were significantly up-regulated in the MEC sample. Similarly, four DEGs associated with flavonoid biosynthesis (FLS, Cluster-189403.11461; ANR, Cluster-189403.46350; CYP98A, Cluster-189403.44480; DFR, Cluster-189403.43752) were also validated by RT-qPCR (Fig. [153]7F). Our observations revealed that DEGs of FLS compared with FEF sample were significantly up-regulated in the MEF sample. On the contrary, DEGs of ANR, CYP98A, and DFR compared with FEF sample showed lower expression levels in the MEF sample. Similar expression patterns were observed between RT-qPCR and RNA-Seq results. These results indicated that transcription results accurately reflected gene expression in the DEGs (alpha-linolenic acid pathway and flavonoid biosynthesis) of male and female E. ulmoides, indicating reliable transcriptomics results. Comparison of E. ulmoides cortex and leaves It is noteworthy that a total of 1123 common metabolites were characterized in the cortex and leaves of E. ulmoides (Fig. [154]8A), of which 58 metabolites, including organic acids, phenolic acids, terpenoids, etc., were found exclusively in the cortex sample, with organic acids being the most abundant class (17.24%). Meanwhile, a total of 176 metabolites, including flavonoids, phenolic acids, lipids, etc., were found exclusively in the leaf sample. Among them, the flavonoid class was the most abundant, accounting for 52.84% of the total metabolites (Fig. [155]8B). The above detailed data are shown in Table [156]S7. Fig. 8. [157]Fig. 8 [158]Open in a new tab The comparison metabolites of E. ulmoides cortex and leaves. (A) Venn diagram of metabolites in the EC and EF. (B) The unique metabolites in in the EC and EF. Classes of up-regulated DAMs (C) and down-regulated DAMs (D) in the EC vs. EF. (E) Classes of non-significantly different metabolites in the EC vs. EF. (F) Bar graphs of DAMs and non-significantly different metabolites in the cortex and leaves of E. ulmoides Based on the above summaries of the identification results, a total of 738 metabolites showed significant differences between the cortex and leaves of the E. ulmoides sample in terms of metabolite content, of which the content of 475 DAMs was up-regulated in the leaf sample relative to the cortex sample, except for LysoPG 16:0 and ethyl hydrogen succinate. The specific distribution of metabolites was as follows: (1) several major metabolites dominated with 78.95%, including flavonoids (24.00%), phenolic acids (11.58%), lipids (11.58%), terpenoids (6.32%), and others (saccharides) (9.26%), organic acids (5.68%), amino acids and derivatives (5.47%), and nucleotides and derivatives (5.05%); (2) several metabolites accounted for less than 5%, including phenylpropanoids, alkaloids, alcohols, esters, hydrocarbons, heterocyclic compounds, aromatics, aldehydes, and ketones; (3) the remaining compounds accounted for less than 1%, including amines, phenols, tannins, others, nitrogen compounds, and sulfur compounds (Fig. [159]8C). Similarly, the content of 261 DAMs was down-regulated in the leaf sample compared to the cortex sample, and the distribution of metabolites was as follows: (1) major metabolites accounted for a total of 67.43%, including esters (11.11%), phenolic acids (11.11%), terpenes (10.73%), lipids (10.73%), organic acids (10.73%), heterocyclic compounds (6.90%), and phenylpropanoids (6.13%); (2) several metabolites accounted for less than 5%, including aldehydes, others, ketones, amino acids and derivatives, aromatic hydrocarbons, amines, alkaloids, alcohols, hydrocarbons, flavonoids, acids, nucleotides, and derivatives; (3) the remaining compounds accounted for less than 1%, including ethers, sulfur compounds, and phenols, each accounting for less than 1.00% (Fig. [160]8D).Interestingly, there were 15 co-occurring DAMs in the FEC vs. MEC and FEF vs. MEF comparisons (Fig. [161]S10), suggesting that these DAMs may be able to discriminate between the sexes of E. ulmoides, whether in the cortex or in the leaves of E. ulmoides. (The detailed data are shown in Table [162]S8). Moreover, a total of 448 non-significantly different accumulated metabolites between the cortex and leaves of E. ulmoides, among which were terpenoids, heterocyclic compounds, lipids, phenolic acids, esters, hydrocarbons, others, and organic acids together accounted for 68.97% of the metabolites. These metabolites accounted for less than 5%, including the alcohols, aldehydes, amino acids and derivatives, ketones, aromatic hydrocarbons, nucleotides and derivatives, alkaloids, lignans and coumarins, phenols; and the remaining metabolites accounted for 1% or fewer nitrogen compounds, halogenated hydrocarbons, acids, sulfur compounds, flavonoids, ethers, and amines (Fig. [163]8E). Furthermore, Fig. [164]8F allows a clear comparison of the amounts of DAMs and non-significantly different metabolites in the cortex and leaves of E. ulmoides. The above detailed data are presented in Table [165]S7. Discussion E. ulmoides is an economically important medicinal and food plant, and its metabolites have a variety of biological effects that contribute to human health [[166]25]. As a dioecious plant, its sex differentiation significantly affects its gene expression and metabolic characteristics [[167]6]. Previous studies have confirmed that several genes and endogenous metabolites were implicated in sex determination in E. ulmoides, and these metabolites can serve as reliable biomarkers for sex determination [[168]6, [169]26]. Notably, previous research on the metabolite profiles of E. ulmoides has concentrated on the content variations in different plant parts. For example, approximately 23 chemical compositions of different parts of E. ulmoides have been identified using HPLC-Q-Exactive MS. The results demonstrated that the dominant chemical compositions of E. ulmoides cortex were similar to those of E. ulmoides leaves, with both identifying cycloeletherpene, flavonoids, and lignans [[170]27]. Meanwhile, research suggests that volatile metabolites are also affected by sex differentiation. The results of one study suggest that loral volatiles in female and male Salix purpurea willow catkins measured by GC-MS; male flowers emitted more total terpenoids than females, but females generated more benzenoids [[171]28]. However, the volatile metabolites of male and female E. ulmoides are rarely reported to adopt GC-MS at present. Therefore, the objective of this study was to comprehensively elucidate the differences in metabolic profiles between male and female E. ulmoides using dual platforms of LC-MS/MS and GC-MS/MS, which can provide more comprehensive coverage of metabolites [[172]29]. In this study, 1,452 metabolites were identified in E. ulmoides, including 603 volatile and 849 non-volatile metabolites. These metabolites were classified into 26 categories, including terpenoids, phenolic acids, lipids, flavonoids, esters, heterocyclic compounds, and organic acids, which was consistent with the findings of previous studies [[173]1]. The results of PCA, OPLS-DA, and heatmap showed that the metabolic profiles of male and female E. ulmoides showed relative differences, indicating that the content of a large number of metabolites differed significantly between males and females. A total of 156 DAMs were identified in the comparison of the FEC and MEC, and 129 DAMs were identified in the comparison of the FEF and MEF. The majority of these were phenolic acids, lipids, terpenoids, flavonoid compounds, amino acids, and derivatives. It is noteworthy that the aforementioned DAMs exhibited a significantly elevated expression in the male relative to the female E. ulmoides, both in the cortex and the leaves. A substantial body of research has demonstrated that these compounds possess a multitude of biological functions in plants, which are beneficial to human health, with anti-inflammatory, antioxidant, immunomodulatory, anti-tumor, anti-cancer, and other functions [[174]30]. KEGG co-enrichment analysis was performed to explore the function of DAMs and DEGs in the two comparisons of FEC vs. MEC and FEF vs. MEF to provide a more comprehensive understanding of the transcriptional regulation mechanisms involved in metabolic pathways. The results demonstrated that the most significantly enriched pathways were the alpha-linolenic acid metabolic pathway and the flavonoid biosynthesis pathway in the two comparisons, respectively. The alterations in the levels of diverse DAMs and DEGs identified in this study suggest the following proposed pathways: alpha-linolenic acid metabolism in FEC vs. MEC and flavonoid biosynthesis in FEF vs. MEF (Fig. [175]9A, B). Fig. 9. [176]Fig. 9 [177]Open in a new tab Potential biosynthetic pathway of alpha-linolenic acid metabolism and flavonoid biosynthesis. DEGs and DAMs of alpha-linolenic acid metabolism in contrast FEC vs. MEC (A) and flavonoid biosynthesis in contrast FEF vs. MEF (B) based on the KEGG pathway database. (The red colors represent up-regulation in the MEC (A) and the MEF sample (B), green colors represent down-regulation, and blue colors represent mix-regulation) With regard to the alpha-linolenic acid metabolic pathway illustrated in Fig. [178]9A, cytosolic phospholipase A2 (PLA2G4) represents a pivotal enzyme within the alpha-linolenic acid pathway, facilitating the release of fatty acids from membrane phospholipids through its catalytic activity [[179]31], which is a key step in the metabolism of the polyunsaturated fatty acid alpha-linolenic acid (ALA). The observed increase in ALA may be attributed to the up-regulation of the PLA2G4 gene. Moreover, 17-hydroxylinolenic acid (17-HLA) is formed from ALA through hydroxylation, which in turn leads to increased 17-HLA accumulation. Lipoxygenases (LOX) play a pivotal role in oxylipin biosynthesis in a multitude of plant species, where they facilitate the conversion of fatty acids or their esters to hydroperoxides [[180]32]. LOX converts ALA to 9(S)-HpOTrE and 13 S-HpOTrE, which are reactive hydroperoxides that are easily reduced to the hydroxylated derivatives 9 S-hydroxy-octadecatrienoic acid (9 S-HOTrE) and 13 S-HOTrE, respectively [[181]33]. The action of ALA through LOX, alloxanase (AOS), and cyclooxygenase (CEQORH), followed by a non-enzymatic reaction, produces 9-hydroxy-12-oxo-15(Z)-octadecenoic acid. Fatty acid alpha-dioxygenase (DOX) catalyzes the stereospecific conversion of fatty acids, resulting in the generation of the corresponding (R)-2-hydroperoxy [[182]34]. An increase in DOX expression may result in a corresponding increase in 2(R)-HpOTrE, produced by DOX-catalyzed linolenic acid oxidation. It is worth noting that the downregulation of LOX expression did not lead to a decrease in the level of 9(S)-HpOTrE, but rather an increase. Related studies have shown that LOX can affect male gametophytes [[183]35], and that downregulation of LOX2 expression in female Jatropha curcas plants may lead to male flower abortion in female plants. However, in addition to sex, LOX expression may also be affected by factors such as light, stress, and plant hormones [[184]36]. At the same time, epoxy derivatives of linoleic acid, low temperature, and retinoic acid can further reduce LOX activity [[185]37]. There are sex differences in Pistacia lentiscus of LOX activity in Pistacia chinensis [[186]38], while LOX in tomato is up-regulated or down-regulated under high temperature, salt, or drought stress [[187]39]. Therefore, we speculate that the reason for this phenomenon may be that the downregulation of LOX expression leads to the accumulation of precursor ALA, while LOX activity may still promote the synthesis and accumulation of 9(S)-HpOTrE. In addition, under conditions such as oxidative stress, even if LOX expression is reduced, the increased level of ROS may promote the synthesis of 9(S)-HpOTrE through spontaneous lipid peroxidation [[188]40]. As illustrated in Fig. [189]9B, chalcone synthase (CHS) represents a pivotal enzyme within the flavonoid biosynthesis pathway, facilitating the phosphorylation of dihydroflavonols to flavonols [[190]41]. It was postulated that the up-regulation of CHS expression may be the primary factor contributing to the elevated synthesis of phloretin, eriodictyol, dihydrokaempferol, and prunin. Flavonol synthase (FLS) facilitates the conversion of dihydroflavonols to flavonols through the introduction of a double bond between C-2 and C-3, representing a pivotal step in the biosynthetic pathway [[191]42]. The up-regulation of FLS expression may contribute to an increased accumulation of quercetin. Intriguingly, flavanone 4-reductase (DFR) is a pivotal enzyme in the flavonoid synthesis pathway, which is responsible for the conversion of dihydroflavonols to leucoanthocyanidins. This step serves as a precursor for the synthesis of anthocyanins, flavan-3-ols, and proanthocyanidins. These compounds serve as precursors to the synthesis of other flavonoids, including proanthocyanidins and flavan-3-ols. Although DFR is most active on dihydroflavonols, it also displays significant activity on flavanones and anthocyanins. The enzyme is inhibited by the flavonol quercetin and by high concentrations of di-hydroflavonols or flavanones [[192]43]. This may be the reason why the DFR enzyme is regulated by a mixture of genes. Meanwhile, anthocyanidin reductase (ANR) is also a pivotal enzyme in the proanthocyanidin metabolic pathway, facilitating the conversion of cyanidin to epicatechin [[193]44]. This intermediate is subsequently transported to vesicles and polymerized into proanthocyanidins [[194]45]. Consequently, the down-regulation of ANR expression may result in an increased accumulation of epicatechin. The developmental trends of flavonoid synthesis in male and female plants are different. When nutrients are limited, female plants prioritize obtaining nutrients, while male plants allocate more resources to above-ground growth [[195]46]. Flavonoids can protect plant DNA from UV damage and promote the formation of functional pollen tubes and pollen germination [[196]47, [197]48]. The study found that the FLS gene of the flavonoid biosynthesis pathway was significantly expressed in the male flowers of Fraxinus mandshurica Rupr., indicating that male plants have higher levels of flavonoid biosynthesis, which may contribute to their rapid growth and pollen formation [[198]49]. The expression of CHS, CHI, F3’H, F3H, DFR, LAR, ANS, and ANR genes related to flavonoid biosynthesis was significantly different in male and female Ginkgo leaves, and there was also a significant difference in flavonoid metabolism [[199]50]. The expression of pivotal genes involved in alpha-linolenic acid metabolism, including PLA2G4, DOXs, LOX2S, and CEQORH, was markedly altered in MEC compared to FEC, which may increase ALA and its metabolites. Similarly, the expression of pivotal genes involved in flavonoid biosynthesis, including CHS, FLS, DFR, and ANR, was markedly altered in MEF compared to FEF, which may result in an augmented synthesis of flavonoids. ALA is a significant polyunsaturated fatty acid with extensive industrial applications and considerable potential for development as a nutritious vegetable oil or dietary supplement [[200]51]. The physiological activity of ALA has been demonstrated in modern medical research, establishing it as a vital component of human health. The benefits of ALA include the prevention and management of obesity, diabetes, and cancer, as well as the reduction of inflammation, oxidative stress, and cardiovascular risk. Furthermore, ALA has been demonstrated to enhance cognitive function, including memory and learning [[201]52]. Meanwhile, ALA, a principal component of plant cell membrane lipids and seed storage lipids, demonstrates robust resilience to environmental stresses that plants confront during their growth and development, such as cold, drought, and salinity [[202]53]. Flavonoids are not only pervasively present in the human diet but are also esteemed for their salutary effects, which typically manifest as antioxidant, anti-inflammatory, and antithrombotic properties [[203]54]. Furthermore, flavonoids play a role in plant defense mechanisms, whereby they can activate defense-related pathways and regulate defenses, thus increasing plant resistance and achieving the purpose of stress resistance [[204]55]. In general, the expression of genes involved in significant regulatory pathways exhibiting differential regulation, including LOX2S, OPR, ACAA1, and PLA2G4 genes in alpha-linolenic acid metabolism, and FLS, ANR, CYP98A, and DFR in flavonoid biosynthesis, in cortex and leaves of male and female E. ulmoides, respectively. These findings were consistent with the results of metabolomics that ALA and its metabolites accumulated to a greater extent in the male cortex, while flavonoids accumulated to a greater extent in the male leaves. It can be postulated that both of these may confer greater resilience to environmental stresses. These findings provide a theoretical basis for understanding the molecular mechanisms underlying metabolic differences between male and female E. ulmoides. E. ulmoides is a dioecious plant, and its metabolites differ significantly between sexes, especially in α-linolenic acid metabolism and flavonoid biosynthesis. The gender difference in α-linolenic acid metabolism in E. ulmoides cortex reveals the physiological function of fatty acid metabolism related to sex. Compared with plants such as Fraxinus mandshurica Rupr. and Ginkgo biloba [[205]49, [206]50], the gender differences in the flavonoid biosynthesis pathway of E. ulmoides leaves may reflect its unique strategies in reproduction, physiology, disease resistance and environmental adaptation. This discovery provides new insights into how dioecious plants use specific metabolites to adapt to the environment. Moreover, the chemical compound similarities and differences between the cortex and leaves of E. ulmoides were also elucidated by our study, which revealed that the chemical composition exhibits a considerable number of components (identical 1123 compounds) shared by both parts. Additionally, the distribution of specific components was found to be markedly disparate. For example, 58 metabolites (organic acids, phenolic acids, terpenoids, etc.) and 176 metabolites (flavonoids, phenolic acids, lipids, etc.) were identified as exclusive to the cortex and leaves of E. ulmoides. Nevertheless, while E. ulmoides cortex is the predominant medicinal component and possesses significant medicinal value in clinical practice, its shortcomings are also evident. These include the need for a lengthy growing period, low yield, and costly harvesting costs, which collectively present a significant challenge to meeting market demand. Consequently, finding reliable alternative sources of E. ulmoides cortex also is a crucial matter of urgency. And the E. ulmoides leaves are more readily available and possess analogous active components and pharmacological effects, including antioxidant, anti-inflammatory, hypotensive, neuroprotective, osteoprotective, hypotensive, and hypolipidemic effects, which are comparable to those observed in E. ulmoides cortex [[207]56]. The results of this study also demonstrated that the chemical composition of the cortex and leaves of E. ulmoides exhibits a relatively high degree of similarity. In certain applications in medicine and food, E. ulmoides leaves can be regarded as a complement to E. ulmoides cortex [[208]57]. Through comprehensive metabolomics and transcriptomics analysis, this study comprehensively revealed the mechanistic differences in metabolites between male and female E. ulmoides, which not only deepened the understanding of the metabolic differences between male and female E. ulmoides individuals, but also provided a new perspective and framework for the study of sex-specific metabolism. But there are still several limitations. First, only adult plants were analyzed, while male and female plants at different developmental stages may have significant metabolic differences [[209]58]. Secondly, this study did not consider the effects of environmental stress (such as drought, salinity, high temperature, etc.) on sex-specific metabolism. Previous studies have shown that environmental stress significantly changes plant metabolites, and male and female plants may have different stress response mechanisms [[210]59, [211]60]. Therefore, future research should cover different developmental stages and environmental stresses, and systematically explore the regulatory mechanism of genotype-environment interaction on sex metabolic differences in E. ulmoides, so as to fully understand the ecological adaptation strategy of E. ulmoides. Conclusion In the present study, we employed an integrating dual-platform metabolomic with transcriptomic, to uncover the underlying metabolite mechanisms that differ between male and female E. ulmoides cortex and leaves. Our findings revealed significant differences in metabolic profiles and gene expression patterns between male and female E. ulmoides. In total, 1,452 metabolites were identified in E. ulmoides, comprising 603 volatile and 849 non-volatile metabolites. The MEC and FEC samples found 33 unique metabolites, while the MEF and FEF samples found 29 and 14, respectively. And 156 and 129 DAMs were identified in the cortex and folium of E. ulmoides from the two tissues, respectively. Moreover, transcriptomic analysis characterized 21,472 and 3,554 DEGs in two contrasts. The KEGG co-enrichment analysis of DAMs and DEGs revealed that the alpha-linolenic acid metabolism and flavonoid biosynthesis metabolism pathways were the significantly in the two contrasts, respectively. ALA and its metabolites were the primary discriminating DAMs between the FEC and MEC, while flavonoids were the primary discriminating DAMs between the FEF and MEF. Differences in the metabolic profiles of male and female E. ulmoides cortex and leaves may be associated with significant changes in several key alpha-Linolenic acid metabolism-related genes (including LOX2S, OPR, ACAA1, and PLA2G4) and flavonoid biosynthesis-related genes (including FLS, ANR, CYP98A, and DFR), respectively. This novel finding provides a theoretical basis for the underlying molecular mechanisms of metabolic differences between male and female E. ulmoides and facilitates further comprehensive research on more dioecious species in the future. Electronic supplementary material Below is the link to the electronic supplementary material. [212]Supplementary Material 1^ (2MB, docx) [213]Supplementary Material 2^ (1.4MB, xls) Acknowledgements