Integrated metabolomic and transcriptomic analyses of flavonoid accumulation in different cultivars of Platostoma palustre

Integrated metabolomic and transcriptomic analyses of flavonoid accumulation in different cultivars of Platostoma palustre | 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 metabolomic and transcriptomic analyses of flavonoid accumulation in different cultivars of Platostoma palustre Jiankai You, Lishan Zeng, Zhongdong Wang, Yimeng Xia, Ying Lin, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4689992/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2024 Read the published version in BMC Plant Biology → Version 1 posted 12 You are reading this latest preprint version Abstract Background Platostoma palustre is a kind of plant resource with medicinal and food value, which has been differentiated into many different varieties after a long period of breeding. The cultivars of Taiwan(TW) and Pingyuan(PY) are widely grown in Guangdong, but a clear basis for species differentiation has not yet been established, resulting in the mixing of different species which limits their production and application. Results Regarding leaf surface morphology, the TW exhibited greater leaf area, non-glandular hairs, and the number of stomata than the PY. Regarding chemical activities, the TW exhibited higher total flavonoid content and antioxidant activity than the PY. In metabolomics, a total of 85 DAMs were detected, among which four flavonoid DAMs were identified, all of which were up-regulated in TW expression. Transcriptome analysis identified 2503 DEGs, which were classified according to their functional roles. The results demonstrated that the DEGs were primarily involved in amino acid metabolism, carbohydrate metabolism, sorting and degradation. Combined analysis of metabolome and transcriptome indicated that the phenylpropanoid pathway plays a significant role in flavonoid synthesis. Furthermore, real-time fluorescence qrt-PCR validation demonstrated that the expression trend of 10 DEGs was consistent with the transcriptomics data. Conclusion The phenylpropanoid pathway affects the synthesis of secondary metabolites, resulting in functional differences. In this study, metabolomic and transcriptomic analyses were performed to elucidate the regulatory mechanisms of flavonoid synthesis in P. palustre and to provide a theoretical basis for the identification, differentiation and breeding cultivation of different cultivars of P. palustre . Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Background Platostoma palustre (or Mesona chinensis Benth.), also known as “Xiancao”, is a herbaceous plant in the family Lamiaceae , which is distributed in the provinces of Guangdong, Guangxi, Zhejiang, Jiangxi and Taiwan, in China [ 1 ], and is one of the important medicinal and edible cash crops [ 2 ]. P. palustre has a lengthy history of serving as a resource for medicinal and food purposes, and it has been utilized in ancient folk medicine to treat hypertension, diabete and liver diseases [ 3 ]. P. palustre is rich in many active ingredients, including polysaccharides, flavonoids, triterpenoids, phenolic acid and other compounds [ 4 ]. It is widely used as an antioxidant drug [ 5 – 8 ] and has the effects of anti-oxidative stress damage [ 9 – 11 ], anti-inflammation [ 12 , 13 ] and lowering blood lipids [ 8 ]. P. palustre is recognized for its refreshing flavor and high nutritional value as a food ingredient. It is widely used in the production of grass jelly, which has a significant consumer following and is gradually becoming a hot research topic. Due to the increasing demand both domestically and overseas, the cultivation area of P. palustre has expanded annually. After a prolonged period of natural selection and deliberate breeding, plant morphology diverges resulting in the formation of distinct cultivars. Among them, the two cultivars Taiwan(TW) and Pingyuan(PY) are more widely planted in Guangdong. Xiaoying Lu et al. utilized headspace gas-chromatography-mass spectrum(HS-GC-MS) to analyze the volatile constituents of TW and PY, which were distinctly categorized into two groups [ 14 ]. In addition to variations in chemical composition, the two cultivars also exhibited differences in their agronomic traits and herb quality. TW plants were observed to be taller, with thicker leaves, denser trichome growth, higher yields, and fewer diseases. In contrast, PY plants were found to be shorter, possessed thinner leaves, shorter trichomes, lower yields, and more diseases, yet displayed an enhanced aroma after the herbs were stored. A clear basis for species differentiation has not been established, which has resulted in the two cultivars being often mixed in commercial production [ 15 ]. This has led to variability in quality, which has seriously affected the stability of the quality of the herb and restricted its use in clinical applications and food production. Flavonoids represent the principal active ingredients in P. palustre, which can be employed as the primary components of anti-inflammatory, antibacterial, antiviral and stomachic and elimination drugs [ 16 , 17 ]. They are closely related to the quality of P. palustre herbs. However, the majority of current studies focus on the isolation, extraction and in vitro functional application of the active components of P. palustre , with fewer detailed studies on the mechanism of flavonoid synthesis and regulation in vivo. The correlation analysis of metabolomics and transcriptomics can analyze the co-expression of metabolites and genes related to metabolic pathways, to explore the relationship between functional characteristics of plant varieties and metabolites or genes [ 18 – 20 ]. This study will use non-targeted metabolomics and transcriptomics to analyze the TW and PY cultivars. The aim is to explore the differences in metabolites and genetic characteristics between these two cultivars, determine their respective total flavonoid content, and assess antioxidant activity to provide a strong foundation for the differentiation, breeding, and cultivation of P. palustre across various cultivars. Materials and methods Plant materials The Pingyuan grass (PY) and Taiwan grass (TW) seedlings used in this experiment were sourced from the planting base of P. palustre in Shizheng Town, Pingyuan County, Meizhou City, Guangdong Province, China. They were planted in Shi Zhen Mountain, Guangzhou University of Traditional Chinese Medicine (GZTCM) in early May 2022 and were identified as belonging to the family Labiatae, specifically Platostoma palustre , by Associate Professor Zhang Guifang of GZTCM. The leaves of the plant were harvested on 10th December 2022 and all tissues were promptly frozen in liquid nitrogen and transferred to -80°C for future use. Screening micromorphological traits using scanning electron microscopy The leaf abaxial (AB) and adaxial (AD) surfaces of the species under this study were mounted onto stubs with double-sided adhesive tape, coated for 2 min with gold in a polaron JFC-1100E coating unit, and then examined and photographed with a JEOL JSM-IT200 in Guangzhou University of Chinese Medicine. The quantitative and qualitative features of the epidermal cells of both leaf surfaces (AB and AD) were recorded, including data on leaf surface structures, both closed and opened stomata, i.e., length and width, and measured by ImageJ analysis software. Metabolite extraction and profiling analysis Refer to other research methods [ 21 ], accurate to weigh an appropriate amount of sample into a 2 mL centrifuge tube, add 600 µL MeOH (stored at -20℃) (Containing 2-Amino-3-(2-chloro-phenyl)-propionic acid(4 ppm), vortex for 30 s. Add 100 mg glass bead, place in a tissue grinder for 90 s at 60 Hz, and room temperature ultrasound for 15 min. Centrifuge for 10 min at 12,000 rpm and 4℃(Refrigerated centrifuge H1850-R, Hunan Xiangyi Laboratory Instrument Development Co., Ltd., Hunan City, China), filter the supernatant by 0.22 µm membrane and transfer into the detection bottle for LC-MS detection. LC-MS conditions The extracts were analyzed using an Ultra Performance Liquid Chromatography system(Thermo Vanquish, Thermo Fisher Scientific, USA)coupled with a tandem mass spectrometer(Thermo Orbitrap Exploris 120, Thermo Fisher Scientific, USA ). The experiment conditions were as follows: The UPLC system was equipped with ACQUITY UPLC HSS T3 C18 column (Waters, 1.8 µm×2.1 mm×100 mm); solvent system, water. The column maintained at 40 ℃. The flow rate and injection volumewere set at 0.25 mL/min and 2 µL, respectively. For LC-ESI (+)-MS analysis, the mobile phasesconsisted of (B2) 0.1% formic acid in acetonitrile (v/v) and (A2) 0.1% formic acid in water (v/v). Separation was conducted under the following gradient: 0 ~ 1 min, 2% B2; 1 ~ 9 min, 2%~50% B2;9 ~ 12 min, 50%~98% B2; 12 ~ 13.5 min, 98% B2; 13.5 ~ 14 min, 98%~2% B2; 14 ~ 20 min, 2% B2. ForLC-ESI (-)-MS analysis, the analytes was carried out with (B3) acetonitrile and (A3) ammoniumformate (5mM). Separation was conducted under the following gradient: 0 ~ 1 min, 2% B3; 1 ~ 9min, 2%~50% B3; 9 ~ 12 min, 50%~98% B3; 12 ~ 13.5 min, 98% B3; 13.5 ~ 14 min, 98%~2% B3;14 ~ 17 min, 2% B3 [ 22 ]. Mass spectrometric detection of metabolites was performed on Orbitrap Exploris 120 (ThermoFisher Scientific, USA) with ESI ion source. Simultaneous MS1 and MS/MS (Full MS-ddMS2 mode, data-dependent MS/MS) acquisition was used. The parameters were as follows: sheath gaspressure, 30 arb; aux gas flow, 10 arb; spray voltage, 3.50 kV and − 2.50 kV for ESI(+) and ESI(-), respectively; capillary temperature, 325 ℃; MS1 range, m/z 100–1000; MS1 resolving power, 60000 FWHM; number of data dependant scans per cycle, 4; MS/MS resolving power, 15000 FWHM; normalized collision energy, 30%; dynamic exclusion time, automatic [ 23 ]. Qualitative and quantitative analysis of metabolites The raw data were firstly converted to mzXML format by MSConvert in the ProteoWizard software package (v3.0.8789) and processed using XCMS for feature detection, retention time correction and alignment. MS/MS data which were matched with HMDB [ 24 ] ( http://www.hmdb.ca ), massbank [ 25 ]( http://www.massbank.jp/ ), LipidMaps [ 26 ] ( http://www.lipidmaps.org ), mzcloud [ 27 ]( https://www.mzcloud.org ) and KEGG [ 28 ] ( http://www.genome.jp/kegg/ ). The robust LOESS signal correction (QC-RLSC) [ 29 ]was applied for data normalization to correct for any systematic bias. Finally, the following bioinformatics analysis of the data was performed using Ropls software [ 30 ]: principal component analysis(PCA), orthogonal partial least squares(OPLS-DA) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway. All samples were examined using six biological replicates. RNA extraction, library construction, and sequencing Total RNA was isolated using an RNAprep Pure Plant kit (Tiangen, Beijing, China) according to the instructions. Using the NanoPhotometer®spectrophotometer (IMPLEN, CA, USA) and the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA. RNA purity and integrity were assessed. cDNA libraries were created using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA), library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, USA) and the quality of the libraries was assessed on the Agilent Bioanalyzer 2100 system. library quality was assessed. All clean reads were mapped to GO, SwissProt, PFAM, KEGG and eggNOG databases for gene function annotation. The DEGs were identified with a threshold of |log2fold change|≥ 1 and p value < 0.05. All samples were examined using three biological replicates. Verification of candidate genes by quantitative real-time PCR (qRT-PCR) The DEGs identified in this study were verified by qRT-PCR (C1000 Touch PCR instrument, Bio-Rad, USA) using primers synthesized by Shanghai Sangon Bioengineering Co., Ltd (Tabel S1). The reaction mixture consisted of 10.0 µL of 2x ChamQ SYBR qPCR Master Mix, 0.4 µL of forward primer (10 µM), 0.4 µL of reverse primer (10 µM), 0.4 µL of 50x ROX Reference Dye 1, 2.0 µL of diluted cDNA, and 6.8 µL of ddH2O. qRT‒PCR was carried out using the following program: 95℃ for 30 s, followed by 40 amplification cycles at 95℃ for 5 s and 60℃ for 30 s. Finally, the relative expression levels of genes were analyzed using the 2 −ΔΔCT [ 31 ]method with the reference gene GAPDH [ 32 ]. Each sample was analyzed using three technical replicates. Results The leaf appearance and shape of TW and PY Fresh leaves from various cultivars were compared for measurement. The leaf blade traits of TW are ovate-lanceolate, while those of PY are long ovate (Fig. 1 A, B). The leaf area and thickness were measured, and the resulting averages were 11.18 cm 2 and 1.24 mm for TW, 9.54 cm 2 and 0.93 mm for PY, respectively. Subsequently, the scanning electron microscope (SEM) of the dried leaves showed that (Fig. 1 C-F) the non-glandular hairs of the leaves of the two cultivars were concentrated near the leaf veins, and the non-glandular hairs of TW were more and longer than PY, and the density of stomata TW was higher than that of PY. Flavonoid contents and antioxidant capacities between cultivars The total flavonoid content and antioxidant activity of TW and PY were determined to better understand the differences between the two cultivars (Fig. 2 ). The results showed that the total flavonoid content of TW was significantly higher than that of PY (120.97 and 99.76 mg/g of dry weight, respectively; p < 0.05). TW showed a statistically significant (p < 0.05) increase in the capacity to scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals and ferric reducing antioxidant power (FRAP) (61.49% and 482.44 µmol Trolox/g in TW, respectively; 40.29% and 345.33 µmol Trolox/g in PY, respectively). In terms of scavenging free radical 2,2-azinobis [3-ethyl-benzothiazoline-6-sulfonic acid] (ABTS), the activities of TW and PY were 163.75 µmol Trolox/g and 131.31 µmol Trolox/g, respectively. However, there was no statistically significant difference between them. The total flavonoid content and antioxidant capacity of TW were higher than those of PY, and there was a positive correlation between them. Qualitative analysis of metabolites LC-MS/MS-based widely non-targeted metabolomics was carried out, and the mass spectrometry data were substituted into the metabolic component information database for detection and analysis. Further using ppm as a screening standard, compounds within ± 10ppm were retained, and a total of 501 compounds were identified. The metabolites were clustered, in the heatmap(Fig. 3 A), and all biological replicates were grouped, indicating a strong correlation among the replicates and high data reliability. The 501 metabolites can be classified in detail according to their properties (Table S2 ): 82 lipids, 78 organic acids, 58 saccharides and alcohols, 53 amino acids, 26 phenols, 24 nucleotides and their derivatives, 22 esters, 21 flavonoids, 17 terpenoids, 16 steroids, 13 amines, 13 lignans and coumarins, 12 aldehydes, 11 alkaloids, 10 vitamins, 9 ketones and 36 others. Principal component analysis (PCA) To understand the differences in the composition of metabolites in P. palustre among cultivars, PCA was used to perform pattern recognition on all P. palustre samples, which can visualize grouping trends and outliers. The PCA scores of the mass spectrometry data for each treatment quality control sample (Fig. 3 B) showed that two principal components (PC1 and PC2) accounted for 20.4% and 16.8%. From the figure (Fig. 3 C), Metabolites contributing significantly to PC1 included saccharopine, mesaconate, geniposidic acid, 3,4-methylenedioxyamphetamine, etc. Metabolites contributing significantly to PC2 included (S)-2, 3-epoxysqualene, cellotetraose, 3-(2-hydroxyphenyl)propanoic acid, naringenin, etc. PCA results showed that the content of compounds identified in TW and PY samples was different to some extent. Discriminant analysis of orthogonal partial least squares(OPLS-DA) However, the detailed differences in each cluster remained unknown. OPLS-DA is a supervised pattern recognition method that can be used to analyze, classify and reduce the dimensionality of complex datasets. The OPLS-DA scores plot(Fig. 3 D) shows the TW and the PY groups were clearly separated, indicating that the metabolites of TW and PY tended to separate. The OPLS-DA model was validated by the permutation test (Fig. S1 ). In the figure, the R2 and Q2 values on the left were lower than those on the right, indicating good classification and predictability of the model. Differentially accumulated metabolites(DAMs) analysis According to the variable importance of the inter-group prediction (VIP) value and the significance test of the OPLS-DA model, the potential metabolites causing resource differences were screened. The variables, only under the condition of VIP > 1.0 and p < 0.05 (t-test), were considered to have a meaningful contribution to the OPLS-DA model. Using this model as a basis, we conducted a screening for distinct metabolites within the TW and PY. The screening results were presented as volcano plots(Fig. 3 E), where 85 DAMs were screened, of which 39 were up-regulated and 46 were down-regulated. The 85 DAMs can be classified in detail according to their properties (Fig. 4 A): 13 organic acids, 12 amino acids, 10 lipids, 7 saccharides and alcohols, 6 esters, 4 alkaloids, 4 phenols, 4 flavonoids, 3 aldehydes, 3 terpenoids, 3 lignans and coumarins, 2 amines, 2 steroids, 1 nucleotides and their derivatives, 1 vitamin and 10 others. The DAMs between TW and PY are mainly organic acid compounds, accounting for 15.29%, most of which are involved in amino acid metabolism, followed by amino acids and lipids, accounting for 14.12% and 11.76%, respectively. It can be seen that the DAMs of flavonoids were all up-regulated in TW varieties, including Naringenin (14.78 times), Cyanidin 3-glucoside (4.91 times), 5, 7-dihydroxyflavone (3.95 times), Formononetin (1.39 times). Significance analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) can elucidate the primary biological processes associated with DAMs. The DAMs were involved in 56 pathways, and the enriched results also presented in a bubble chart (only the results of the top 20) are shown (Fig. 4 B). The figure showed that the top five significantly enriched KEGG pathways were tyrosine metabolism, phenylalanine biosynthesis, Synthesis and degradation of ketone bodies, vitamin B6 metabolism, and phenylalanine metabolism. Naringenin and 5, 7-dihydroxyflavone were enriched in the KEGG pathway of Flavonoid biosynthesis. Transcriptomics Analysis From Illumina sequencing, raw data and raw reads were obtained from the TW and PY cultivars. Q30 scores of all samples ranged from 93.91–94.46%(Table 1 ), indicating high sequencing data quality. After trimming of connector sequences and low-quality reads, 199,574 clean sequences were obtained and 71220 Unigene was concatenated by Trinity (Table 2 ). The maximum and average lengths of the assembled genes were 16568bp and 993.30bp, respectively. The length of the annotated gene is shown in (Fig. S2 ). Table 1 Raw data analysis Sample Reads No. Bases (bp) Q30 (bp) N (%) Q20 (%) Q30 (%) PYL1 39378418 5946141118 5597142022 0.003467 98.02 94.13 PYL2 45207198 6826286898 6419085392 0.003431 97.98 94.03 PYL3 40596726 6130105626 5783013118 0.003458 98.1 94.33 TWL1 44939142 6785810442 6409878654 0.003443 98.13 94.46 TWL2 41389498 6249814198 5882005998 0.003477 98.03 94.11 TWL3 42570664 6428170264 6037231591 0.003429 97.93 93.91 Table 2 Sequence statistics Transcript Unigene Total Length (bp) 258023747 70742520 Sequence Number 199574 71220 Max. Length (bp) 16568 16568 Mean Length (bp) 1292.87 993.3 N50 (bp) 1879 1549 N50 Sequence No. 44375 13270 N90 (bp) 574 415 N90 Sequence No. 137542 51160 GC% 41.79 41.2 Differentially expressed genes(DEGs) analysis According to the conditions that |log2FoldChange| < 1 and P-value < 0.05, 3384 DEGs were screened out, including 1454 up-regulated genes and 1930 down-regulated genes. Through matching with the GO, SwissProt, PFAM, KEGG and eggNOG database information, 2503 DEGs were identified (Fig. 5 A). According to functional classification, 798 DEGs with known functions were further partitioned into 5 initial categories, including Metabolism, Genetic Information Processing, Cellular Processes, Environmental Information Processing, Organismal Systems. Then, we performed KEGG enrichment analysis of the second KEGG pathway category, which revealed the following pathways: Amino acid metabolism (92), Biosynthesis of other secondary metabolites (37), Carbohydrate metabolism (144), Energy metabolism (32), Environmental adaptation(47), Folding, sorting and degradation (61), Glycan biosynthesis and metabolism (14), Lipid metabolism (53), Membrane transport (21), Metabolism of cofactors and vitamins (32), Metabolism of other amino acids (27), Metabolism of terpenoids and polyketides (18), Nucleotide metabolism(13), Replication and repair (17), Signal transduction (64), Transcription(17), Translation (72), Transport and catabolism (37). KEGG enrichment annotation (Fig. 5 B) showed that DEGs were mainly enriched in plant-pathogen interaction, ABC transporters, MAPK signaling pathway - plant, etc. Correlation analysis between the Metabolomic and transcriptomic data We used the O2PLS (Two-way Orthogonal Partial Least Squares) method to assess the intrinsic correlation between transcriptomics and metabolomics by calculating the score for each sample and obtaining the joint score, and by calculating the loading values for each differential gene and differential metabolite to obtain the loading plot and the absolute values of the load values of the first 20 DAMs/DEGs are selected to construct the histogram (Fig. 6 ). From the results it was seen that DAMs such as Chorismate, Naringenin, Cyanidin 3-glucoside are associated with the phenylpropanoid pathway and are involved in the synthesis of flavonoids by the phenylpropanoid pathway. Among the DEGs, TRINITY_DN4104_c0_g3 was related to photosynthesis. Correlation analysis of metabolomics and transcriptomics of TW and PY cultivars of P. palustre was performed in combination with KEGG pathway enrichment analysis, and the same pathway was enriched in the transcriptomics and metabolomics(Fig. 6 C). 5 KEGG pathways are involved in biological processes related to phenylpropanoid metabolism, including Phenylpropanoid biosynthesis (map00940), Phenylalanine metabolism (map00360), and Flavonoid biosynthesis (map00941), Tyrosine metabolism (map00350), Tryptophan metabolism(mapmap00380). In the Phenylpropanoid biosynthesis pathway, it was discovered that 10 DEGs have also been involved in the biological processes of Cyanoamino acid metabolism and Starch and sucrose metabolism. In transcriptomics, 7 DEGs were found to be enriched in KEGG pathways associated with flavonoid biosynthesis, while 22 DEGs were found to be enriched in KEGG pathways associated with phenylpropanoid biosynthesis (Fig. 7 A). To better understand the molecular mechanism of flavonoid accumulation in the two cultivars of P. palustre , we performed a predictive reconstruction of flavonoid biosynthesis based on the results of the screened flavonoid biosynthesis-related differential genes and KEGG pathway analyses (Fig. 7 B). As illustrated in the pathway diagram, while the flavonoid DAMs exhibited a significant upregulation in TW, the stronger expression of functional genes PAL , CHS , and ANS related to flavonoid synthesis was observed in PY. This result can be attributed to the intricate regulation of flavonoid synthesis and metabolism, as well as several feedback and regulatory mechanisms, including post-transcriptional modification, enzyme activity adjustment, and metabolite feedback regulation. A comprehensive investigation into the functional pathway of flavonoid synthesis in P. palustre has yet to be conducted, and further research is required. Confirmation of DEGs via qRT-PCR To validate the reliability and stability of the RNA sequencing (RNA-seq) data of the DEGS, we selected 9 DEGS for qRT-PCR validation (Fig. 8 ). Among them 3 DEGs ( ANS , FLS , CHS ) were associated with flavonoid biosynthesis, 2 DEGs ( HPD , AOC ) were associated with phenylalanine metabolism and tyrosine metabolism, and 4 DEGs ( CAD , UGT72E , PAL , blgx ) were associated with phenylpropanoid biosynthesis. The results are consistent with the transcriptomic gene expression trends, suggesting that the transcriptomic data were reliable. Discussion In this experiment, 85 DAMs (39 up-regulated and 46 down-regulated) and 3384 DEGs (1454 up-regulated and 1930 down-regulated genes) were identified between TW and PY cultivars. In metabolomics, 21 flavonoids and 16 terpenoids, as well as 11 alkaloids, were identified among the secondary metabolites (Table S3). Plants can produce a variety of secondary metabolites with different chemical characteristics in response to the changing environment [ 33 ]. Flavonoids are secondary metabolites of a phenolic nature that exist in nature and possess a broad spectrum of pharmacological actions [ 34 ], such as antibacterial activity [ 35 ], anti-inflammatory activity [ 36 – 38 ], antiviral activity [ 39 ], antioxidant activity [ 40 – 42 ], and play a defense role in plants in response to various biological and abiotic stresses through dynamic changes [ 43 ]. We have detected 21 flavonoids in total, comprising six flavonoids, five anthocyanins, four flavonols, three dihydroflavonols, two isoflavones, and one dihydroisoflavone. All four flavonoid DAMs were up-regulated in TW. Naringenin, the DAMs measured in the metabolome, is a significant intermediate in the flavonoid synthesis pathway [ 44 ], as it is a flavanone produced through enzymatic catalysis of chalcone. Formononetin is the conversion of chalcone to isoflavones after a series of enzyme-catalyzed conversions, and liquintigenin generates Formononetin under the regulation of HIDH [ 45 ]. Flavanones are catalysed by F3H to form dihydroflavonols, which are then regulated by DFR , ANS , etc. to form Cyanidin [ 46 , 47 ], and Cyanidin combines with glycosides to form Cyanidin 3-glucoside; 5,7-Dihydroxyflavone is formed by cinnamoyl-coenzyme A under the constant regulation of CHS , CHI , and FNS II to form salicin, which is formed by dehydrogenation of salicin [ 48 ]. Subsequently, we demonstrated via sodium nitrite-aluminum nitrate spectrophotometry and antioxidant activity experiments that the TW possesses a greater total flavonoid content and antioxidant capacity than PY. Furthermore, we confirmed a positive correlation between antioxidant activity and total flavonoid content in P. palustre . We hypothesized that the differences in the ability of TW and PY cultivars to synthesize flavonoids were related to the phenylpropanoid pathway. It is widely accepted that flavonoid synthesis primarily occurs via the phenylpropanoid pathway [ 49 ]. Coumaroyl coenzyme A, with phenylalanine and tyrosine as precursors, undergoes catalysis by CHS and CHI to form dihydroflavonoids. These are then processed by various enzymes to produce different types of flavonoid compounds [ 50 – 52 ]. As evidenced by the transcriptomics and metabolomics findings of this experiment, there was a significant enrichment of DAMs and DEGs in the phenylpropanoid pathway. In transcriptomics, 5, 6, and 16 DEGs were enriched in KEGG pathways for tyrosine, phenylalanine, and phenylpropanoid biosynthesis, respectively. In metabolomics, 5, 3, and 4 DAMs were enriched in KEGG pathways for tyrosine, phenylalanine, and phenylpropanoid biosynthesis, respectively. Chorismate, Naringenin, and Cyanidin 3-glucoside were also found to be associated with the phenylpropane pathway and involved in flavonoid synthesis in O2PLS analysis. Enrichment analyses aim to identify biological pathways that are significant in various biological processes, phenylpropanoid metabolism is one of the important secondary metabolic pathways in plants. Therefore, it is reasonable to hypothesize that the functional differences in flavonoid synthesis in P. palustre may be linked to the phenylpropanoid pathway. The two cultivars of P. palustre have different resistance traits that can be interpreted and explored from a variety of perspectives. As can be seen from the SEM images, TW has more non-glandular hairs than PY. Non-glandular hairs, as specialised accessory structures embedded in the plant epidermis, separating the external environment from the plant epidermis. They perform important functions, including defense [ 53 ] and resistance [ 54 ]. These functions are particularly important in the context of insect and pathogen resistance. Non-glandular hairs can affect insect development, feeding, movement, and delay or limit the access of phytophagous insects to the plant epidermis [ 55 ]. Additionally, research studies have indicated that non-glandular hairs may possess disease resistance. For instance, the legume red clover has been observed to exhibit resistance to powdery mildew, which facilitates normal plant development [ 56 ]. Non-glandular hairs safeguard the apical shoots of plant stems from external harm during the preliminary stages of growth. It has been observed that plants with longer non-glandular hairs exhibit greater resilience towards cold temperatures [ 57 ]. Concurrently, flavonoids have significant antioxidant effects and reduce oxidative damage caused by the accumulation of reactive oxygen species, which play a role in plant growth, development, and defense [ 58 ]. To a certain extent, the antioxidant activity of plants can reflect the strength of stress tolerance and disease resistance. It has been demonstrated that maize seedlings treated with exogenous abscisic acid (ABA) showed an increase in antioxidant enzyme activities CAT and SOD, resulting in improved resistance to drought [ 59 ]. Additionally, papaya treated with methyl jasmonate (MEJA) displayed increased antioxidant activity, leading to enhanced tolerance to low temperatures [ 60 ]. Transfecting tobacco with maize Cat2 resulted in higher catalase (CAT) activity in comparison to untransfected plants, thus inhibiting pathogen growth [ 61 ]. In this experiment, the flavonoid content and antioxidant activity of the TW cultivar exceeded that of the PY cultivar in this investigation. These may explain why the TW cultivar experiences less disease. In addition to flavonoids, we have identified 11 types of alkaloids, including isoquinoline, pyridine, and indole alkaloids, in the study. 4 of the DAMs, Isocorypalmine, Isoquinoline, 17-O-Acetylnorajmaline, and Nicotine, were found to be up-regulated in TW expression. The composition of the 16 terpenoids includes 7 monoterpenoids, 2 sesquiterpenes, 3 diterpenes, and 4 triterpenoids. DAMs xanthotoxin was significantly up-regulated in TW, while that of Soyasapogenol A was down-regulated. The phenylpropanoid metabolic pathway is a significant pathway for the synthesis of plant secondary metabolites. Numerous studies have uncovered the enzymatic reaction process of phenylpropanoid metabolism in plants, along with the entire metabolic pathway's regulatory mechanism. PAL , C4H , and 4CL , which are the key enzymes in the phenylpropanoid metabolic pathway [ 62 ], successively reacted to produce cinnamic acid, p-hydroxycinnamic acid, and p-coumaroyl coenzyme A. These substrates are eventually converted into a variety of phenylpropanoid compounds, including flavonoids, lignans, terpenoids, alkaloids, and other secondary metabolites. Research has shown that the phenylpropanoid metabolic pathway is linked to plant resistance [ 63 ]. When plants are under stress, enzymes in this pathway, including PAL , 4CL , and C4H , become more active. This increase in activity leads to the metabolic synthesis of lignin, which promotes the degree of cellular lignification. Additionally, the pathway produces a variety of metabolites such as phenols, flavonoids, and terpenes, which further synthesize phytophysical proteins. It has been shown that the phytopropane pathway plays a role in producing salicylic acid, which is essential for activating the plant's immune pathway and enhancing its defense against diseases. As a result, this regulates the plant's disease resistance and defense ability [ 64 ]. We have identified 10 DEGs in the phenylpropanoid pathway that are involved in cyanoamino acid metabolism. Among them, TRINITY_DN7521_c3_g1 , TRINITY_DN10843_c0_g2 , and TRINITY_DN39702_c0_g1 are peroxidases that break down peroxides. Although the exact mechanism has not yet been determined, peroxidases are known to strengthen plant defenses against pathogens [ 65 , 66 ]. We suggest that these three genes are involved in redox processes in P. palustre which are associated with its antioxidative properties. Conclusion Differences in phenotypic characteristics, total flavonoid content, and antioxidant activity of P. palustre samples from TW and PY cultivars were identified. Analyzing the transcriptome and metabolome, we observed the impact of the phenylpropanoid pathway on the synthesis of secondary metabolites in P. palustre , which may account for functional variations, and explored the correlation between antioxidant activity and plant resistance. Our investigation has provided a new understanding of the regulatory mechanisms behind flavonoid biosynthesis in P. palustre . This has led to critical theoretical data that significantly aids the process of identifying P. palustre from various cultivars, as well as facilitating its selection and cultivation. Abbreviations DEGs Differential expression genes DAMs Differentially accumulated metabolites KEGG Kyoto encyclopedia of genes and genomes qRT-PCR Quantitative real-time PCR PAL Phenylalanine ammonia lyase C4H Cinnamate4-hydroxylase 4CL 4—coumarate:CoA ligase CHS Chalcone synthase CHI Chalcone isomerase F3’H Flavonoid 3-hydroxylase FLS Flavonol synthase DFR Dihydroflavonol 4-reductase HIDH 2-hydroxyisoflavanone dehydratase ANS Anthocyanidin Synthase HCT Shikimate O-hydroxycinnamoyltransferase CAD Cinnamyl-alcohol dehydrogenase UGT72E Coniferyl-alcohol glucosyltransferase Declarations Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Acknowledgments Not applicable. Authors’ contributions R.Z. and G.Z. designed the experiments. J.Y. conducted the experiments and analyzed the data. L.Z., Z.W., Y.X., and Y.L. performed the research. J.Y. wrote the manuscript. All authors read and approved the manuscript. Funding This research was financially supported by National Survey of Traditional Chinese Medicine Resources Project of the State Administration of Traditional Chinese Medicine (GZY-KJS-2018-004), the Key Field Project of "Serving Rural Revitalization Plan" of Colleges and Universities in Guangdong Province -- Construction of Scientific and Technological service System of Southern Medicine Industry based on Guangdong Rural Revitalization (2019KZDZX2017) and Guangdong Provincial Rural Revitalization Strategy (Agricultural Science and Technology Innovation and Promotion System Construction) Special Project -- Guangdong Modern Southern Medicine Industry Technology System Innovation Team (2023KJ148). Availability of data and materials The plant materials were grown in our resource nursery. These materials are available from the corresponding author upon reasonable request. 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The phenotypical characteristics of TW(A)and PY(B). Distribution characteristics of non-glandular hair in leaves of different varieties under 50 fold(C, D). Distribution characteristics of glandular hair and stomata in leaves under 200 fold(E, F).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/47811065fc13244964ab837f.jpg"},{"id":61781617,"identity":"70c13ce4-c41f-4635-8585-6f9c387e7098","added_by":"auto","created_at":"2024-08-05 13:49:36","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2180230,"visible":true,"origin":"","legend":"\u003cp\u003eTotal flavonoid contents and antioxidant activities between the TW and PY. \u003cstrong\u003eA \u003c/strong\u003eTotal flavonoid contents. \u003cstrong\u003eB\u003c/strong\u003e DPPH radical scavenging activity. \u003cstrong\u003eC\u003c/strong\u003eABTS scavenging capacity. \u003cstrong\u003eD \u003c/strong\u003eFRAP scavenging activity.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/55106e78f3e22d31e9b81ea1.jpg"},{"id":61782892,"identity":"e889350f-02f5-42ec-8f34-d9cf8b588b8d","added_by":"auto","created_at":"2024-08-05 14:05:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":353634,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Results of cluster analysis of metabolites detected in all samples. \u003cstrong\u003eB, C \u003c/strong\u003eThe score and loading results of PCA. \u003cstrong\u003eD \u003c/strong\u003eThe score result of PCA. \u003cstrong\u003eE\u003c/strong\u003e Volcanic map of differential metabolites.\u003c/p\u003e","description":"","filename":"floatimage314.png","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/02e3ca701d6dbf448a76f5fa.png"},{"id":61782893,"identity":"c3464c52-8959-443d-850d-18d7e49d218d","added_by":"auto","created_at":"2024-08-05 14:05:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2691743,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eClassification of significantly differentially regulated metabolites between TW and PY. \u003cstrong\u003eB \u003c/strong\u003eScatterplot of the KEGG pathway enriched by different metabolites between TW and PY. The vertical axis represents the name of the pathway, and the horizontal axis represents the P value. The size and color of bubbles indicate the number and degree of enrichment of DAMs, respectively.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/a7341ea358d0d8ea01a96f4b.jpg"},{"id":61781612,"identity":"9bcfaee7-7eb8-41ba-b322-4724e0a214a8","added_by":"auto","created_at":"2024-08-05 13:49:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":110490,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eDistribution and classification of DEGs between TW and PY. \u003cstrong\u003eB \u003c/strong\u003eScatterplot of the KEGG pathway enriched by DEGs between TW and PY. The vertical axis represents the name of the pathway, and the horizontal axis represents the P value. The size and color of bubbles indicate the number and degree of enrichment of DEGs, respectively.\u003c/p\u003e","description":"","filename":"OnlineFigure52.png","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/299ae59aef1ca23018bcd8bf.png"},{"id":61781620,"identity":"2f32d7b1-7c7e-4466-910c-420ff867cc95","added_by":"auto","created_at":"2024-08-05 13:49:37","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3480730,"visible":true,"origin":"","legend":"\u003cp\u003eMetabolite and transcript correlation analysis. \u003cstrong\u003eA \u003c/strong\u003eThe top 20 DEGs and DAMs loading histogram. Orange bars represent genes and red bars represent metabolites. The abscissa represents the combined loading value pq1, and the ordinate represents the DEGs/DAMs. \u003cstrong\u003eB \u003c/strong\u003eAssess the intrinsic correlation between the metabolites and transcripts by the two-way orthogonal partial least squares (O2PLS) method. The orange dots represent genes and the red dots represent metabolites. The abscissa and ordinate represent the combined loading value, each gene/metabolite has a relative coordinate point in pq1 and pq2, p represents the loading value of the gene, and q represents the loading value of the metabolite. \u003cstrong\u003eC \u003c/strong\u003eHistogram of the KEGG pathway enriched by the diferentially expressed genes and metabolites. The ordinate represents the pathway name, and the abscissa represents the pathway to impact.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/351e097e4aad2c8922d1ec15.jpg"},{"id":61782358,"identity":"f262839a-66ca-43d8-b8e6-775d74f32f66","added_by":"auto","created_at":"2024-08-05 13:57:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":107575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eHeatmap of gene expression associated with flavonoid biosynthesis and phenylpropanoid biosynthesis. \u003cstrong\u003eB \u003c/strong\u003eThe flavonoids biosynthesis pathway potentially used by the two cultivars based on gene expression levels and pathway annotations in the KEGG database. In the heatmap, the red block represents up-regulation and green, and blue represent downregulation of expression, respectively. The metabolic components and genes were mapped to be generated based on phenylpropanoid biosynthesis (k000940) and flavonoid biosynthesis (ko00941).\u003c/p\u003e","description":"","filename":"OnlineFigure71.png","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/fe27627a5cd497bfad13ff8d.png"},{"id":61781621,"identity":"c428a509-9148-424f-9582-2b1499baeb3f","added_by":"auto","created_at":"2024-08-05 13:49:37","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2407522,"visible":true,"origin":"","legend":"\u003cp\u003eqRT-PCR validation of the relative expression levels of 9 selected genes from different cultivars of \u003cem\u003eP. palustre\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/08fee58cba6bb29eb16be61e.jpg"},{"id":72201824,"identity":"8f16f1f6-475b-4850-8151-84621c7faded","added_by":"auto","created_at":"2024-12-23 16:10:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12633488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/323cb9b7-54e9-43ad-965f-bc83fbbf58f2.pdf"},{"id":61781611,"identity":"f1f0a9d5-4c6a-44f0-83db-21d1e318b3fb","added_by":"auto","created_at":"2024-08-05 13:49:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":162752,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/d4fdfea5876720093446c8dc.docx"},{"id":61781619,"identity":"c82644df-de66-4961-9f47-6287c74deeab","added_by":"auto","created_at":"2024-08-05 13:49:37","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":23871,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4689992/v1/46fc99f0fb27472492dfe060.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated metabolomic and transcriptomic analyses of flavonoid accumulation in different cultivars of Platostoma palustre","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cem\u003ePlatostoma palustre\u003c/em\u003e (or \u003cem\u003eMesona chinensis\u003c/em\u003e Benth.), also known as \u0026ldquo;Xiancao\u0026rdquo;, is a herbaceous plant in the family \u003cem\u003eLamiaceae\u003c/em\u003e, which is distributed in the provinces of Guangdong, Guangxi, Zhejiang, Jiangxi and Taiwan, in China [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], and is one of the important medicinal and edible cash crops [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. \u003cem\u003eP. palustre\u003c/em\u003e has a lengthy history of serving as a resource for medicinal and food purposes, and it has been utilized in ancient folk medicine to treat hypertension, diabete and liver diseases [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. \u003cem\u003eP. palustre\u003c/em\u003e is rich in many active ingredients, including polysaccharides, flavonoids, triterpenoids, phenolic acid and other compounds [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. It is widely used as an antioxidant drug [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and has the effects of anti-oxidative stress damage [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], anti-inflammation [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and lowering blood lipids [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. \u003cem\u003eP. palustre\u003c/em\u003e is recognized for its refreshing flavor and high nutritional value as a food ingredient. It is widely used in the production of grass jelly, which has a significant consumer following and is gradually becoming a hot research topic.\u003c/p\u003e \u003cp\u003eDue to the increasing demand both domestically and overseas, the cultivation area of \u003cem\u003eP. palustre\u003c/em\u003e has expanded annually. After a prolonged period of natural selection and deliberate breeding, plant morphology diverges resulting in the formation of distinct cultivars. Among them, the two cultivars Taiwan(TW) and Pingyuan(PY) are more widely planted in Guangdong. Xiaoying Lu et al. utilized headspace gas-chromatography-mass spectrum(HS-GC-MS) to analyze the volatile constituents of TW and PY, which were distinctly categorized into two groups [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition to variations in chemical composition, the two cultivars also exhibited differences in their agronomic traits and herb quality. TW plants were observed to be taller, with thicker leaves, denser trichome growth, higher yields, and fewer diseases. In contrast, PY plants were found to be shorter, possessed thinner leaves, shorter trichomes, lower yields, and more diseases, yet displayed an enhanced aroma after the herbs were stored. A clear basis for species differentiation has not been established, which has resulted in the two cultivars being often mixed in commercial production [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This has led to variability in quality, which has seriously affected the stability of the quality of the herb and restricted its use in clinical applications and food production.\u003c/p\u003e \u003cp\u003eFlavonoids represent the principal active ingredients in P. palustre, which can be employed as the primary components of anti-inflammatory, antibacterial, antiviral and stomachic and elimination drugs [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. They are closely related to the quality of \u003cem\u003eP. palustre\u003c/em\u003e herbs. However, the majority of current studies focus on the isolation, extraction and in vitro functional application of the active components of \u003cem\u003eP. palustre\u003c/em\u003e, with fewer detailed studies on the mechanism of flavonoid synthesis and regulation in vivo. The correlation analysis of metabolomics and transcriptomics can analyze the co-expression of metabolites and genes related to metabolic pathways, to explore the relationship between functional characteristics of plant varieties and metabolites or genes [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. This study will use non-targeted metabolomics and transcriptomics to analyze the TW and PY cultivars. The aim is to explore the differences in metabolites and genetic characteristics between these two cultivars, determine their respective total flavonoid content, and assess antioxidant activity to provide a strong foundation for the differentiation, breeding, and cultivation of \u003cem\u003eP. palustre\u003c/em\u003e across various cultivars.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eThe Pingyuan grass (PY) and Taiwan grass (TW) seedlings used in this experiment were sourced from the planting base of \u003cem\u003eP. palustre\u003c/em\u003e in Shizheng Town, Pingyuan County, Meizhou City, Guangdong Province, China. They were planted in Shi Zhen Mountain, Guangzhou University of Traditional Chinese Medicine (GZTCM) in early May 2022 and were identified as belonging to the family Labiatae, specifically \u003cem\u003ePlatostoma palustre\u003c/em\u003e, by Associate Professor Zhang Guifang of GZTCM. The leaves of the plant were harvested on 10th December 2022 and all tissues were promptly frozen in liquid nitrogen and transferred to -80\u0026deg;C for future use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eScreening micromorphological traits using scanning electron microscopy\u003c/h2\u003e \u003cp\u003eThe leaf abaxial (AB) and adaxial (AD) surfaces of the species under this study were mounted onto stubs with double-sided adhesive tape, coated for 2 min with gold in a polaron JFC-1100E coating unit, and then examined and photographed with a JEOL JSM-IT200 in Guangzhou University of Chinese Medicine. The quantitative and qualitative features of the epidermal cells of both leaf surfaces (AB and AD) were recorded, including data on leaf surface structures, both closed and opened stomata, i.e., length and width, and measured by ImageJ analysis software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetabolite extraction and profiling analysis\u003c/h2\u003e \u003cp\u003eRefer to other research methods [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], accurate to weigh an appropriate amount of sample into a 2 mL centrifuge tube, add 600 \u0026micro;L MeOH (stored at -20℃) (Containing 2-Amino-3-(2-chloro-phenyl)-propionic acid(4 ppm), vortex for 30 s. Add 100 mg glass bead, place in a tissue grinder for 90 s at 60 Hz, and room temperature ultrasound for 15 min. Centrifuge for 10 min at 12,000 rpm and 4℃(Refrigerated centrifuge H1850-R, Hunan Xiangyi Laboratory Instrument Development Co., Ltd., Hunan City, China), filter the supernatant by 0.22 \u0026micro;m membrane and transfer into the detection bottle for LC-MS detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eLC-MS conditions\u003c/h2\u003e \u003cp\u003eThe extracts were analyzed using an Ultra Performance Liquid Chromatography system(Thermo Vanquish, Thermo Fisher Scientific, USA)coupled with a tandem mass spectrometer(Thermo Orbitrap Exploris 120, Thermo Fisher Scientific, USA ). The experiment conditions were as follows:\u003c/p\u003e \u003cp\u003eThe UPLC system was equipped with ACQUITY UPLC HSS T3 C18 column (Waters, 1.8 \u0026micro;m\u0026times;2.1 mm\u0026times;100 mm); solvent system, water. The column maintained at 40 ℃. The flow rate and injection volumewere set at 0.25 mL/min and 2 \u0026micro;L, respectively. For LC-ESI (+)-MS analysis, the mobile phasesconsisted of (B2) 0.1% formic acid in acetonitrile (v/v) and (A2) 0.1% formic acid in water (v/v). Separation was conducted under the following gradient: 0\u0026thinsp;~\u0026thinsp;1 min, 2% B2; 1\u0026thinsp;~\u0026thinsp;9 min, 2%~50% B2;9\u0026thinsp;~\u0026thinsp;12 min, 50%~98% B2; 12\u0026thinsp;~\u0026thinsp;13.5 min, 98% B2; 13.5\u0026thinsp;~\u0026thinsp;14 min, 98%~2% B2; 14\u0026thinsp;~\u0026thinsp;20 min, 2% B2. ForLC-ESI (-)-MS analysis, the analytes was carried out with (B3) acetonitrile and (A3) ammoniumformate (5mM). Separation was conducted under the following gradient: 0\u0026thinsp;~\u0026thinsp;1 min, 2% B3; 1\u0026thinsp;~\u0026thinsp;9min, 2%~50% B3; 9\u0026thinsp;~\u0026thinsp;12 min, 50%~98% B3; 12\u0026thinsp;~\u0026thinsp;13.5 min, 98% B3; 13.5\u0026thinsp;~\u0026thinsp;14 min, 98%~2% B3;14\u0026thinsp;~\u0026thinsp;17 min, 2% B3 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMass spectrometric detection of metabolites was performed on Orbitrap Exploris 120 (ThermoFisher Scientific, USA) with ESI ion source. Simultaneous MS1 and MS/MS (Full MS-ddMS2 mode, data-dependent MS/MS) acquisition was used. The parameters were as follows: sheath gaspressure, 30 arb; aux gas flow, 10 arb; spray voltage, 3.50 kV and \u0026minus;\u0026thinsp;2.50 kV for ESI(+) and ESI(-), respectively; capillary temperature, 325 ℃; MS1 range, m/z 100\u0026ndash;1000; MS1 resolving power, 60000 FWHM; number of data dependant scans per cycle, 4; MS/MS resolving power, 15000 FWHM; normalized collision energy, 30%; dynamic exclusion time, automatic [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQualitative and quantitative analysis of metabolites\u003c/h2\u003e \u003cp\u003eThe raw data were firstly converted to mzXML format by MSConvert in the ProteoWizard software package (v3.0.8789) and processed using XCMS for feature detection, retention time correction and alignment. MS/MS data which were matched with HMDB [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hmdb.ca\u003c/span\u003e\u003cspan address=\"http://www.hmdb.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), massbank [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.massbank.jp/\u003c/span\u003e\u003cspan address=\"http://www.massbank.jp/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), LipidMaps [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.lipidmaps.org\u003c/span\u003e\u003cspan address=\"http://www.lipidmaps.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), mzcloud [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mzcloud.org\u003c/span\u003e\u003cspan address=\"https://www.mzcloud.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and KEGG [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.genome.jp/kegg/\u003c/span\u003e\u003cspan address=\"http://www.genome.jp/kegg/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The robust LOESS signal correction (QC-RLSC) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]was applied for data normalization to correct for any systematic bias.\u003c/p\u003e \u003cp\u003eFinally, the following bioinformatics analysis of the data was performed using Ropls software [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]: principal component analysis(PCA), orthogonal partial least squares(OPLS-DA) and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway. All samples were examined using six biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eRNA extraction, library construction, and sequencing\u003c/h2\u003e \u003cp\u003e Total RNA was isolated using an RNAprep Pure Plant kit (Tiangen, Beijing, China) according to the instructions. Using the NanoPhotometer\u0026reg;spectrophotometer (IMPLEN, CA, USA) and the RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA. RNA purity and integrity were assessed. cDNA libraries were created using the NEBNext\u0026reg; Ultra\u0026trade; RNA Library Prep Kit for Illumina\u0026reg; (NEB, USA), library fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, USA) and the quality of the libraries was assessed on the Agilent Bioanalyzer 2100 system. library quality was assessed.\u003c/p\u003e \u003cp\u003eAll clean reads were mapped to GO, SwissProt, PFAM, KEGG and eggNOG databases for gene function annotation. The DEGs were identified with a threshold of |log2fold change|\u0026ge; 1 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All samples were examined using three biological replicates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eVerification of candidate genes by quantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eThe DEGs identified in this study were verified by qRT-PCR (C1000 Touch PCR instrument, Bio-Rad, USA) using primers synthesized by Shanghai Sangon Bioengineering Co., Ltd (Tabel S1). The reaction mixture consisted of 10.0 \u0026micro;L of 2x ChamQ SYBR qPCR Master Mix, 0.4 \u0026micro;L of forward primer (10 \u0026micro;M), 0.4 \u0026micro;L of reverse primer (10 \u0026micro;M), 0.4 \u0026micro;L of 50x ROX Reference Dye 1, 2.0 \u0026micro;L of diluted cDNA, and 6.8 \u0026micro;L of ddH2O. qRT‒PCR was carried out using the following program: 95℃ for 30 s, followed by 40 amplification cycles at 95℃ for 5 s and 60℃ for 30 s.\u003c/p\u003e \u003cp\u003eFinally, the relative expression levels of genes were analyzed using the 2\u003csup\u003e\u0026minus;ΔΔCT\u003c/sup\u003e [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]method with the reference gene GAPDH [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Each sample was analyzed using three technical replicates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe leaf appearance and shape of TW and PY\u003c/h2\u003e \u003cp\u003eFresh leaves from various cultivars were compared for measurement. The leaf blade traits of TW are ovate-lanceolate, while those of PY are long ovate (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B). The leaf area and thickness were measured, and the resulting averages were 11.18 cm\u003csup\u003e2\u003c/sup\u003e and 1.24 mm for TW, 9.54 cm\u003csup\u003e2\u003c/sup\u003e and 0.93 mm for PY, respectively. Subsequently, the scanning electron microscope (SEM) of the dried leaves showed that (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC-F) the non-glandular hairs of the leaves of the two cultivars were concentrated near the leaf veins, and the non-glandular hairs of TW were more and longer than PY, and the density of stomata TW was higher than that of PY.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eFlavonoid contents and antioxidant capacities between cultivars\u003c/h2\u003e \u003cp\u003eThe total flavonoid content and antioxidant activity of TW and PY were determined to better understand the differences between the two cultivars (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results showed that the total flavonoid content of TW was significantly higher than that of PY (120.97 and 99.76 mg/g of dry weight, respectively; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). TW showed a statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) increase in the capacity to scavenge 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radicals and ferric reducing antioxidant power (FRAP) (61.49% and 482.44 \u0026micro;mol Trolox/g in TW, respectively; 40.29% and 345.33 \u0026micro;mol Trolox/g in PY, respectively). In terms of scavenging free radical 2,2-azinobis [3-ethyl-benzothiazoline-6-sulfonic acid] (ABTS), the activities of TW and PY were 163.75 \u0026micro;mol Trolox/g and 131.31 \u0026micro;mol Trolox/g, respectively. However, there was no statistically significant difference between them.\u003c/p\u003e \u003cp\u003eThe total flavonoid content and antioxidant capacity of TW were higher than those of PY, and there was a positive correlation between them.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQualitative analysis of metabolites\u003c/h2\u003e \u003cp\u003eLC-MS/MS-based widely non-targeted metabolomics was carried out, and the mass spectrometry data were substituted into the metabolic component information database for detection and analysis. Further using ppm as a screening standard, compounds within \u0026plusmn;\u0026thinsp;10ppm were retained, and a total of 501 compounds were identified. The metabolites were clustered, in the heatmap(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), and all biological replicates were grouped, indicating a strong correlation among the replicates and high data reliability. The 501 metabolites can be classified in detail according to their properties (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e): 82 lipids, 78 organic acids, 58 saccharides and alcohols, 53 amino acids, 26 phenols, 24 nucleotides and their derivatives, 22 esters, 21 flavonoids, 17 terpenoids, 16 steroids, 13 amines, 13 lignans and coumarins, 12 aldehydes, 11 alkaloids, 10 vitamins, 9 ketones and 36 others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal component analysis (PCA)\u003c/h2\u003e \u003cp\u003eTo understand the differences in the composition of metabolites in \u003cem\u003eP. palustre\u003c/em\u003e among cultivars, PCA was used to perform pattern recognition on all \u003cem\u003eP. palustre\u003c/em\u003e samples, which can visualize grouping trends and outliers. The PCA scores of the mass spectrometry data for each treatment quality control sample (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) showed that two principal components (PC1 and PC2) accounted for 20.4% and 16.8%. From the figure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), Metabolites contributing significantly to PC1 included saccharopine, mesaconate, geniposidic acid, 3,4-methylenedioxyamphetamine, etc. Metabolites contributing significantly to PC2 included (S)-2, 3-epoxysqualene, cellotetraose, 3-(2-hydroxyphenyl)propanoic acid, naringenin, etc. PCA results showed that the content of compounds identified in TW and PY samples was different to some extent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDiscriminant analysis of orthogonal partial least squares(OPLS-DA)\u003c/h2\u003e \u003cp\u003eHowever, the detailed differences in each cluster remained unknown. OPLS-DA is a supervised pattern recognition method that can be used to analyze, classify and reduce the dimensionality of complex datasets. The OPLS-DA scores plot(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) shows the TW and the PY groups were clearly separated, indicating that the metabolites of TW and PY tended to separate. The OPLS-DA model was validated by the permutation test (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the figure, the R2 and Q2 values on the left were lower than those on the right, indicating good classification and predictability of the model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially accumulated metabolites(DAMs) analysis\u003c/h2\u003e \u003cp\u003eAccording to the variable importance of the inter-group prediction (VIP) value and the significance test of the OPLS-DA model, the potential metabolites causing resource differences were screened. The variables, only under the condition of VIP\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (t-test), were considered to have a meaningful contribution to the OPLS-DA model. Using this model as a basis, we conducted a screening for distinct metabolites within the TW and PY. The screening results were presented as volcano plots(Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE), where 85 DAMs were screened, of which 39 were up-regulated and 46 were down-regulated. The 85 DAMs can be classified in detail according to their properties (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA): 13 organic acids, 12 amino acids, 10 lipids, 7 saccharides and alcohols, 6 esters, 4 alkaloids, 4 phenols, 4 flavonoids, 3 aldehydes, 3 terpenoids, 3 lignans and coumarins, 2 amines, 2 steroids, 1 nucleotides and their derivatives, 1 vitamin and 10 others. The DAMs between TW and PY are mainly organic acid compounds, accounting for 15.29%, most of which are involved in amino acid metabolism, followed by amino acids and lipids, accounting for 14.12% and 11.76%, respectively. It can be seen that the DAMs of flavonoids were all up-regulated in TW varieties, including Naringenin (14.78 times), Cyanidin 3-glucoside (4.91 times), 5, 7-dihydroxyflavone (3.95 times), Formononetin (1.39 times).\u003c/p\u003e \u003cp\u003eSignificance analysis of the Kyoto Encyclopedia of Genes and Genomes (KEGG) can elucidate the primary biological processes associated with DAMs. The DAMs were involved in 56 pathways, and the enriched results also presented in a bubble chart (only the results of the top 20) are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The figure showed that the top five significantly enriched KEGG pathways were tyrosine metabolism, phenylalanine biosynthesis, Synthesis and degradation of ketone bodies, vitamin B6 metabolism, and phenylalanine metabolism. Naringenin and 5, 7-dihydroxyflavone were enriched in the KEGG pathway of Flavonoid biosynthesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomics Analysis\u003c/h2\u003e \u003cp\u003eFrom Illumina sequencing, raw data and raw reads were obtained from the TW and PY cultivars. Q30 scores of all samples ranged from 93.91\u0026ndash;94.46%(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), indicating high sequencing data quality. After trimming of connector sequences and low-quality reads, 199,574 clean sequences were obtained and 71220 Unigene was concatenated by Trinity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The maximum and average lengths of the assembled genes were 16568bp and 993.30bp, respectively. The length of the annotated gene is shown in (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\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\u003eRaw data analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \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\u003eReads No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBases (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ30 (bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ20 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eQ30 (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePYL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39378418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5946141118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5597142022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePYL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45207198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6826286898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6419085392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePYL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40596726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6130105626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5783013118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44939142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6785810442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6409878654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWL2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41389498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6249814198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5882005998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e98.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e94.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTWL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42570664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6428170264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6037231591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e97.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e93.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSequence statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTranscript\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnigene\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e258023747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70742520\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequence Number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71220\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax. Length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16568\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Length (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1292.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e993.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50 (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN50 Sequence No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN90 (bp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN90 Sequence No.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e137542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51160\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGC%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eDifferentially expressed genes(DEGs) analysis\u003c/h2\u003e \u003cp\u003eAccording to the conditions that |log2FoldChange| \u0026lt; 1 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 3384 DEGs were screened out, including 1454 up-regulated genes and 1930 down-regulated genes. Through matching with the GO, SwissProt, PFAM, KEGG and eggNOG database information, 2503 DEGs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). According to functional classification, 798 DEGs with known functions were further partitioned into 5 initial categories, including Metabolism, Genetic Information Processing, Cellular Processes, Environmental Information Processing, Organismal Systems. Then, we performed KEGG enrichment analysis of the second KEGG pathway category, which revealed the following pathways: Amino acid metabolism (92), Biosynthesis of other secondary metabolites (37), Carbohydrate metabolism (144), Energy metabolism (32), Environmental adaptation(47), Folding, sorting and degradation (61), Glycan biosynthesis and metabolism (14), Lipid metabolism (53), Membrane transport (21), Metabolism of cofactors and vitamins (32), Metabolism of other amino acids (27), Metabolism of terpenoids and polyketides (18), Nucleotide metabolism(13), Replication and repair (17), Signal transduction (64), Transcription(17), Translation (72), Transport and catabolism (37). KEGG enrichment annotation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB) showed that DEGs were mainly enriched in plant-pathogen interaction, ABC transporters, MAPK signaling pathway - plant, etc.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis between the Metabolomic and transcriptomic data\u003c/h2\u003e \u003cp\u003eWe used the O2PLS (Two-way Orthogonal Partial Least Squares) method to assess the intrinsic correlation between transcriptomics and metabolomics by calculating the score for each sample and obtaining the joint score, and by calculating the loading values for each differential gene and differential metabolite to obtain the loading plot and the absolute values of the load values of the first 20 DAMs/DEGs are selected to construct the histogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). From the results it was seen that DAMs such as Chorismate, Naringenin, Cyanidin 3-glucoside are associated with the phenylpropanoid pathway and are involved in the synthesis of flavonoids by the phenylpropanoid pathway. Among the DEGs, \u003cem\u003eTRINITY_DN4104_c0_g3\u003c/em\u003e was related to photosynthesis.\u003c/p\u003e \u003cp\u003eCorrelation analysis of metabolomics and transcriptomics of TW and PY cultivars of \u003cem\u003eP. palustre\u003c/em\u003e was performed in combination with KEGG pathway enrichment analysis, and the same pathway was enriched in the transcriptomics and metabolomics(Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). 5 KEGG pathways are involved in biological processes related to phenylpropanoid metabolism, including Phenylpropanoid biosynthesis (map00940), Phenylalanine metabolism (map00360), and Flavonoid biosynthesis (map00941), Tyrosine metabolism (map00350), Tryptophan metabolism(mapmap00380). In the Phenylpropanoid biosynthesis pathway, it was discovered that 10 DEGs have also been involved in the biological processes of Cyanoamino acid metabolism and Starch and sucrose metabolism.\u003c/p\u003e \u003cp\u003eIn transcriptomics, 7 DEGs were found to be enriched in KEGG pathways associated with flavonoid biosynthesis, while 22 DEGs were found to be enriched in KEGG pathways associated with phenylpropanoid biosynthesis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). To better understand the molecular mechanism of flavonoid accumulation in the two cultivars of \u003cem\u003eP. palustre\u003c/em\u003e, we performed a predictive reconstruction of flavonoid biosynthesis based on the results of the screened flavonoid biosynthesis-related differential genes and KEGG pathway analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). As illustrated in the pathway diagram, while the flavonoid DAMs exhibited a significant upregulation in TW, the stronger expression of functional genes \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eCHS\u003c/em\u003e, and \u003cem\u003eANS\u003c/em\u003e related to flavonoid synthesis was observed in PY. This result can be attributed to the intricate regulation of flavonoid synthesis and metabolism, as well as several feedback and regulatory mechanisms, including post-transcriptional modification, enzyme activity adjustment, and metabolite feedback regulation. A comprehensive investigation into the functional pathway of flavonoid synthesis in \u003cem\u003eP. palustre\u003c/em\u003e has yet to be conducted, and further research is required.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eConfirmation of DEGs via qRT-PCR\u003c/h2\u003e \u003cp\u003eTo validate the reliability and stability of the RNA sequencing (RNA-seq) data of the DEGS, we selected 9 DEGS for qRT-PCR validation (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Among them 3 DEGs (\u003cem\u003eANS\u003c/em\u003e, \u003cem\u003eFLS\u003c/em\u003e, \u003cem\u003eCHS\u003c/em\u003e) were associated with flavonoid biosynthesis, 2 DEGs (\u003cem\u003eHPD\u003c/em\u003e, \u003cem\u003eAOC\u003c/em\u003e) were associated with phenylalanine metabolism and tyrosine metabolism, and 4 DEGs (\u003cem\u003eCAD\u003c/em\u003e, \u003cem\u003eUGT72E\u003c/em\u003e, \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eblgx\u003c/em\u003e) were associated with phenylpropanoid biosynthesis. The results are consistent with the transcriptomic gene expression trends, suggesting that the transcriptomic data were reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this experiment, 85 DAMs (39 up-regulated and 46 down-regulated) and 3384 DEGs (1454 up-regulated and 1930 down-regulated genes) were identified between TW and PY cultivars. In metabolomics, 21 flavonoids and 16 terpenoids, as well as 11 alkaloids, were identified among the secondary metabolites (Table S3).\u003c/p\u003e \u003cp\u003ePlants can produce a variety of secondary metabolites with different chemical characteristics in response to the changing environment [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Flavonoids are secondary metabolites of a phenolic nature that exist in nature and possess a broad spectrum of pharmacological actions [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], such as antibacterial activity [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], anti-inflammatory activity [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], antiviral activity [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], antioxidant activity [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and play a defense role in plants in response to various biological and abiotic stresses through dynamic changes [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. We have detected 21 flavonoids in total, comprising six flavonoids, five anthocyanins, four flavonols, three dihydroflavonols, two isoflavones, and one dihydroisoflavone. All four flavonoid DAMs were up-regulated in TW. Naringenin, the DAMs measured in the metabolome, is a significant intermediate in the flavonoid synthesis pathway [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], as it is a flavanone produced through enzymatic catalysis of chalcone. Formononetin is the conversion of chalcone to isoflavones after a series of enzyme-catalyzed conversions, and liquintigenin generates Formononetin under the regulation of \u003cem\u003eHIDH\u003c/em\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Flavanones are catalysed by \u003cem\u003eF3H\u003c/em\u003e to form dihydroflavonols, which are then regulated by \u003cem\u003eDFR\u003c/em\u003e, \u003cem\u003eANS\u003c/em\u003e, etc. to form Cyanidin [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and Cyanidin combines with glycosides to form Cyanidin 3-glucoside; 5,7-Dihydroxyflavone is formed by cinnamoyl-coenzyme A under the constant regulation of \u003cem\u003eCHS\u003c/em\u003e, \u003cem\u003eCHI\u003c/em\u003e, and \u003cem\u003eFNS II\u003c/em\u003e to form salicin, which is formed by dehydrogenation of salicin [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSubsequently, we demonstrated via sodium nitrite-aluminum nitrate spectrophotometry and antioxidant activity experiments that the TW possesses a greater total flavonoid content and antioxidant capacity than PY. Furthermore, we confirmed a positive correlation between antioxidant activity and total flavonoid content in \u003cem\u003eP. palustre\u003c/em\u003e. We hypothesized that the differences in the ability of TW and PY cultivars to synthesize flavonoids were related to the phenylpropanoid pathway. It is widely accepted that flavonoid synthesis primarily occurs via the phenylpropanoid pathway [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Coumaroyl coenzyme A, with phenylalanine and tyrosine as precursors, undergoes catalysis by \u003cem\u003eCHS\u003c/em\u003e and \u003cem\u003eCHI\u003c/em\u003e to form dihydroflavonoids. These are then processed by various enzymes to produce different types of flavonoid compounds [\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. As evidenced by the transcriptomics and metabolomics findings of this experiment, there was a significant enrichment of DAMs and DEGs in the phenylpropanoid pathway. In transcriptomics, 5, 6, and 16 DEGs were enriched in KEGG pathways for tyrosine, phenylalanine, and phenylpropanoid biosynthesis, respectively. In metabolomics, 5, 3, and 4 DAMs were enriched in KEGG pathways for tyrosine, phenylalanine, and phenylpropanoid biosynthesis, respectively. Chorismate, Naringenin, and Cyanidin 3-glucoside were also found to be associated with the phenylpropane pathway and involved in flavonoid synthesis in O2PLS analysis. Enrichment analyses aim to identify biological pathways that are significant in various biological processes, phenylpropanoid metabolism is one of the important secondary metabolic pathways in plants. Therefore, it is reasonable to hypothesize that the functional differences in flavonoid synthesis in \u003cem\u003eP. palustre\u003c/em\u003e may be linked to the phenylpropanoid pathway.\u003c/p\u003e \u003cp\u003eThe two cultivars of \u003cem\u003eP. palustre\u003c/em\u003e have different resistance traits that can be interpreted and explored from a variety of perspectives. As can be seen from the SEM images, TW has more non-glandular hairs than PY. Non-glandular hairs, as specialised accessory structures embedded in the plant epidermis, separating the external environment from the plant epidermis. They perform important functions, including defense [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] and resistance [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These functions are particularly important in the context of insect and pathogen resistance. Non-glandular hairs can affect insect development, feeding, movement, and delay or limit the access of phytophagous insects to the plant epidermis [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Additionally, research studies have indicated that non-glandular hairs may possess disease resistance. For instance, the legume red clover has been observed to exhibit resistance to powdery mildew, which facilitates normal plant development [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Non-glandular hairs safeguard the apical shoots of plant stems from external harm during the preliminary stages of growth. It has been observed that plants with longer non-glandular hairs exhibit greater resilience towards cold temperatures [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Concurrently, flavonoids have significant antioxidant effects and reduce oxidative damage caused by the accumulation of reactive oxygen species, which play a role in plant growth, development, and defense [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. To a certain extent, the antioxidant activity of plants can reflect the strength of stress tolerance and disease resistance. It has been demonstrated that maize seedlings treated with exogenous abscisic acid (ABA) showed an increase in antioxidant enzyme activities CAT and SOD, resulting in improved resistance to drought [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Additionally, papaya treated with methyl jasmonate (MEJA) displayed increased antioxidant activity, leading to enhanced tolerance to low temperatures [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Transfecting tobacco with maize Cat2 resulted in higher catalase (CAT) activity in comparison to untransfected plants, thus inhibiting pathogen growth [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In this experiment, the flavonoid content and antioxidant activity of the TW cultivar exceeded that of the PY cultivar in this investigation. These may explain why the TW cultivar experiences less disease.\u003c/p\u003e \u003cp\u003eIn addition to flavonoids, we have identified 11 types of alkaloids, including isoquinoline, pyridine, and indole alkaloids, in the study. 4 of the DAMs, Isocorypalmine, Isoquinoline, 17-O-Acetylnorajmaline, and Nicotine, were found to be up-regulated in TW expression. The composition of the 16 terpenoids includes 7 monoterpenoids, 2 sesquiterpenes, 3 diterpenes, and 4 triterpenoids. DAMs xanthotoxin was significantly up-regulated in TW, while that of Soyasapogenol A was down-regulated. The phenylpropanoid metabolic pathway is a significant pathway for the synthesis of plant secondary metabolites. Numerous studies have uncovered the enzymatic reaction process of phenylpropanoid metabolism in plants, along with the entire metabolic pathway's regulatory mechanism. \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003eC4H\u003c/em\u003e, and \u003cem\u003e4CL\u003c/em\u003e, which are the key enzymes in the phenylpropanoid metabolic pathway [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], successively reacted to produce cinnamic acid, p-hydroxycinnamic acid, and p-coumaroyl coenzyme A. These substrates are eventually converted into a variety of phenylpropanoid compounds, including flavonoids, lignans, terpenoids, alkaloids, and other secondary metabolites. Research has shown that the phenylpropanoid metabolic pathway is linked to plant resistance [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. When plants are under stress, enzymes in this pathway, including \u003cem\u003ePAL\u003c/em\u003e, \u003cem\u003e4CL\u003c/em\u003e, and \u003cem\u003eC4H\u003c/em\u003e, become more active. This increase in activity leads to the metabolic synthesis of lignin, which promotes the degree of cellular lignification. Additionally, the pathway produces a variety of metabolites such as phenols, flavonoids, and terpenes, which further synthesize phytophysical proteins. It has been shown that the phytopropane pathway plays a role in producing salicylic acid, which is essential for activating the plant's immune pathway and enhancing its defense against diseases. As a result, this regulates the plant's disease resistance and defense ability [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe have identified 10 DEGs in the phenylpropanoid pathway that are involved in cyanoamino acid metabolism. Among them, \u003cem\u003eTRINITY_DN7521_c3_g1\u003c/em\u003e, \u003cem\u003eTRINITY_DN10843_c0_g2\u003c/em\u003e, and \u003cem\u003eTRINITY_DN39702_c0_g1\u003c/em\u003e are peroxidases that break down peroxides. Although the exact mechanism has not yet been determined, peroxidases are known to strengthen plant defenses against pathogens [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. We suggest that these three genes are involved in redox processes in \u003cem\u003eP. palustre\u003c/em\u003e which are associated with its antioxidative properties.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDifferences in phenotypic characteristics, total flavonoid content, and antioxidant activity of \u003cem\u003eP. palustre\u003c/em\u003e samples from TW and PY cultivars were identified. Analyzing the transcriptome and metabolome, we observed the impact of the phenylpropanoid pathway on the synthesis of secondary metabolites in \u003cem\u003eP. palustre\u003c/em\u003e, which may account for functional variations, and explored the correlation between antioxidant activity and plant resistance. Our investigation has provided a new understanding of the regulatory mechanisms behind flavonoid biosynthesis in \u003cem\u003eP. palustre\u003c/em\u003e. This has led to critical theoretical data that significantly aids the process of identifying \u003cem\u003eP. palustre\u003c/em\u003e from various cultivars, as well as facilitating its selection and cultivation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDEGs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferential expression genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAMs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDifferentially accumulated metabolites\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKEGG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKyoto encyclopedia of genes and genomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eqRT-PCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eQuantitative real-time PCR\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003ePAL\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhenylalanine ammonia lyase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eC4H\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCinnamate4-hydroxylase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003e4CL\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e4\u0026mdash;coumarate:CoA ligase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCHS\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChalcone synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCHI\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChalcone isomerase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eF3\u0026rsquo;H\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFlavonoid 3-hydroxylase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eFLS\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFlavonol synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eDFR\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDihydroflavonol 4-reductase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHIDH\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e2-hydroxyisoflavanone dehydratase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eANS\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnthocyanidin Synthase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eHCT\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eShikimate O-hydroxycinnamoyltransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eCAD\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCinnamyl-alcohol dehydrogenase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cem\u003eUGT72E\u003c/em\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConiferyl-alcohol glucosyltransferase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003ePublisher\u0026rsquo;s Note\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.Z. and G.Z. designed the experiments. J.Y. conducted the experiments and analyzed the data. L.Z., Z.W., Y.X., and Y.L. performed the research. J.Y. wrote the\u0026nbsp;manuscript. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was financially supported by National Survey of Traditional Chinese Medicine Resources Project of the State Administration of Traditional Chinese Medicine (GZY-KJS-2018-004), the Key Field Project of \u0026quot;Serving Rural Revitalization Plan\u0026quot; of Colleges and Universities in Guangdong Province -- Construction of Scientific and Technological service System of Southern Medicine Industry based on Guangdong Rural Revitalization (2019KZDZX2017) and Guangdong Provincial Rural Revitalization Strategy (Agricultural Science and Technology Innovation and Promotion System Construction) Special Project -- Guangdong Modern Southern Medicine Industry Technology System Innovation Team (2023KJ148).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant materials were grown in our resource nursery. These materials are available from the corresponding author upon reasonable request. We deposited sequencing data in the Sequence Read Archive (SRA), National Center for Biotechnology Information (NCBI; accession no.PRJNA1127875).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExperimental research and field studies on plants, including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eResearch Center of Chinese Herbal Resource Science and Engineering, School of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou 510006, China. \u003csup\u003e2\u003c/sup\u003eKey Laboratory of Chinese Medicinal Resource from Lingnan Guangzhou University of Chinese Medicine, Ministry of Education, Guangzhou 510006, China. \u003csup\u003e3\u003c/sup\u003eSchool of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLamiaceae. in: X.W. 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Biochem Anal Biochem. 2016;5(3):1\u0026ndash;7.\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":true,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4689992/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4689992/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground Platostoma palustre\u003c/h2\u003e \u003cp\u003eis a kind of plant resource with medicinal and food value, which has been differentiated into many different varieties after a long period of breeding. The cultivars of Taiwan(TW) and Pingyuan(PY) are widely grown in Guangdong, but a clear basis for species differentiation has not yet been established, resulting in the mixing of different species which limits their production and application.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eRegarding leaf surface morphology, the TW exhibited greater leaf area, non-glandular hairs, and the number of stomata than the PY. Regarding chemical activities, the TW exhibited higher total flavonoid content and antioxidant activity than the PY. In metabolomics, a total of 85 DAMs were detected, among which four flavonoid DAMs were identified, all of which were up-regulated in TW expression. Transcriptome analysis identified 2503 DEGs, which were classified according to their functional roles. The results demonstrated that the DEGs were primarily involved in amino acid metabolism, carbohydrate metabolism, sorting and degradation. Combined analysis of metabolome and transcriptome indicated that the phenylpropanoid pathway plays a significant role in flavonoid synthesis. Furthermore, real-time fluorescence qrt-PCR validation demonstrated that the expression trend of 10 DEGs was consistent with the transcriptomics data.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe phenylpropanoid pathway affects the synthesis of secondary metabolites, resulting in functional differences. In this study, metabolomic and transcriptomic analyses were performed to elucidate the regulatory mechanisms of flavonoid synthesis in \u003cem\u003eP. palustre\u003c/em\u003e and to provide a theoretical basis for the identification, differentiation and breeding cultivation of different cultivars of \u003cem\u003eP. palustre\u003c/em\u003e.\u003c/p\u003e","manuscriptTitle":"Integrated metabolomic and transcriptomic analyses of flavonoid accumulation in different cultivars of Platostoma palustre","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 13:49:31","doi":"10.21203/rs.3.rs-4689992/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-01T07:03:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-29T08:02:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67516663038255201604593451076564283849","date":"2024-10-18T17:12:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298096760362924641426595775849464061216","date":"2024-10-18T13:43:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-13T02:17:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180766434577879646021725348174414860058","date":"2024-09-26T08:21:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"147195872809041916454847191012903190008","date":"2024-07-13T06:05:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-12T06:47:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-11T12:01:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-11T11:57:42+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-11T11:56:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2024-07-05T06:14:26+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":"b2127004-010a-4b82-8f42-8df7cfc5ff28","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:02:42+00:00","versionOfRecord":{"articleIdentity":"rs-4689992","link":"https://doi.org/10.1186/s12870-024-05909-5","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2024-12-20 15:57:37","publishedOnDateReadable":"December 20th, 2024"},"versionCreatedAt":"2024-08-05 13:49:31","video":"","vorDoi":"10.1186/s12870-024-05909-5","vorDoiUrl":"https://doi.org/10.1186/s12870-024-05909-5","workflowStages":[]},"version":"v1","identity":"rs-4689992","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4689992","identity":"rs-4689992","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}