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

doi:10.21203/rs.3.rs-4689992/v1

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

https://doi.org/10.21203/rs.3.rs-4689992/v1

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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.

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.

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.

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. 1A, B). The leaf area and thickness were measured, and the resulting averages were 11.18 cm² and 1.24 mm for TW, 9.54 cm² and 0.93 mm for PY, respectively. Subsequently, the scanning electron microscope (SEM) of the dried leaves showed that (Fig. 1C-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. 3A), 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. 3B) showed that two principal components (PC1 and PC2) accounted for 20.4% and 16.8%. From the figure (Fig. 3C), 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. 3D) 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. 3E), 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. 4A): 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. 4B). 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. 5A). 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. 5B) 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. 6C). 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. 7A). 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. 7B). 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.

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.

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.

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

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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. We deposited sequencing data in the Sequence Read Archive (SRA), National Center for Biotechnology Information (NCBI; accession no.PRJNA1127875).

Ethics approval and consent to participate

Experimental research and field studies on plants, including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Author details

¹Research Center of Chinese Herbal Resource Science and Engineering, School of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou 510006, China. ²Key Laboratory of Chinese Medicinal Resource from Lingnan Guangzhou University of Chinese Medicine, Ministry of Education, Guangzhou 510006, China. ³School of Chinese Materia Medica, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.

Lamiaceae. in: X.W. Li, C.H. Lan, Flora of China, editors, Editorial Committee of Flora of China, Science Press, Beijing, 1994, p. 297.
Tang DF, Wei F, Cai ZQ, Wei YY, Khan A, Miao JH, Wei KH. Analysis of codon usage bias and evolution in the chloroplast genome of Mesona chinensis Benth. Dev Genes Evol. 2021;231(1–2):1–9.
Guangdong Provincial Drug Administration. Guangdong Province Traditional Chinese Medicinal Materials Standards (Volume 3) [Book]. 1st edition. Guangzhou: Guangdong Science and Technology Publishing House. 2018, pages 380–385.
Lin LS, Zhang S, Zhan YL, Wang ZK, Lin HT. Progress in chemical composition and pharmacological effects research of Mesona chinensis [J]. Biotechnol Adv. 2013;3(6):448–52.
Yen GC, Duh PD, Hung YL. Contributions of major components to the antimutagenic effect of Hsian-tsao (Mesona procumbens Hemsl). J Agric Food Chem. 2001;49(10):5000–4.
Wongverawattanakul C, Suklaew PO, Chusak C, Adisakwattana S, Thilavech T. Encapsulation of Mesona chinensis Benth Extract in Alginate Beads Enhances the Stability and Antioxidant Activity of Polyphenols under Simulated Gastrointestinal Digestion. Foods. 2022;11(15):2378. Published 2022 Aug 8.
Huang LX, Shen MY, Zhang XW, Jiang L, Song QQ, Xie JH. Effect of high-pressure microfluidization treatment on the physicochemical properties and antioxidant activities of polysaccharide from Mesona chinensis Benth. Carbohydr Polym. 2018;200:191–9.
Xiao LH, Lu XY, Yang HL, Lin CQ, Li L, Ni C, Fang Y, Mo SF, Zhan RT, Yan P. The Antioxidant and Hypolipidemic Effects of Mesona Chinensis Benth Extracts. Molecules. 2022;27(11):3423. Published 2022 May 26.
Chen XX, Shen MY, Yang J, Yu Q, Chen Y, Wang X, Lu HY, Tao X, Li HZ, Xie JH. RNA-seq based elucidation of mechanism underlying Mesona chinensis Benth polysaccharide protected H2O2-induced oxidative damage in L02 cells. Food Res Int. 2022;157:111383.
Huang LX, Huang M, Shen MY, Wen PW, Wu T, Hong YZ, Yu Q, Chen Y, Xie JH. Sulfated modification enhanced the antioxidant activity of Mesona chinensis Benth polysaccharide and its protective effect on cellular oxidative stress. Int J Biol Macromol. 2019;136:1000–6.
Huang LX, Shen MY, Wu T, Yu Y, Yu Q, Chen Y, Xie JH. Mesona chinensis Benth polysaccharides protect against oxidative stress and immunosuppression in cyclophosphamide-treated mice via MAPKs signal transduction pathways. Int J Biol Macromol. 2020;152:766–74.
Lu HY, Shen MY, Chen T, Yu Y, Chen Y, Yu Q, Chen XX, Xie JH. Mesona chinensis Benth Polysaccharides Alleviate DSS-Induced Ulcerative Colitis via Inhibiting of TLR4/MAPK/NF-κB Signaling Pathways and Modulating Intestinal Microbiota. Mol Nutr Food Res. 2022;66(15):e2200047.
Huang GJ, Liao JC, Chiu CS, Huang SS, Lin TH, Deng JS. Anti-inflammatory activities of aqueous extract of Mesona procumbens in experimental mice. J Sci Food Agric. 2012;92(6):1186–93.
Lu XY, Lin CQ, Lai ZM, Jiang XX, Yan P, Zhan RT. Analysis of Volatile Constituents from Mesona chinensis Benth. by HS-GC-MS. J Guangzhou Univ Traditional Chin Med. 2021;38:1025–31.
Tang DF, Wei F, Xie JX, Zhu YX, Wei KH, Miu JH. Analysis of key techniques and problems in conservation of Mesona chiensis Benth in Guangxi. Mod Chin Med. 2022;2403:407–11.
Lin LH, Huang LX, Xie JH. Review on main chemical constituents and biological activities of Mesona chinensis. Sci Technol Food Ind. 2016;37(20):356–9.
Li B, Xie H, Zhong GY, Cheng BX, Dong YJ, Zhang SW. Research Progress on Quality Control of Mesona chinensis. J Guangzhou Univ Traditional Chin Med. 2016;33(05):755–8.
Yu XM, Wang J, Gao R, Gong BC, Ai CX. Integrated Metabolomic-Transcriptomic Analysis Reveals Diverse Resource of Functional Ingredients From Persimmon Leaves of Different Varieties. Front Plant Sci. 2022;13:904208. Published 2022 May 25.
Wan HF, Zhou L, Wu B, Han WJ, Sui C, Wei JH. Integrated metabolomics and transcriptomics analysis of roots of Bupleurum chinense and B. scorzonerifolium, two sources of medicinal Chaihu. Sci Rep. 2022;12(1):22335. Published 2022 Dec 26.
Liu YJ, Liu JN, Kong ZY, Huan XJ, Li L, Zhang P, Wang QC, Guo YR, Zhu WT, Qin P. Transcriptomics and metabolomics analyses of the mechanism of flavonoid synthesis in seeds of differently colored quinoa strains. Genomics. 2022;114(1):138–48.
Vasilev N, Boccard J, Lang G, Grömping U, Fishcer R, Goepfert S, Rudaz S, Schillberg S. Structured plant metabolomics for the simultaneous exploration of multiple factors. Sci Rep. 2016;6:37390. Published 2016 Nov 17.
Zelena E, Dunn WB, Broadhurst D, Francis-Mclntyre S, Carroll KM, Begley P, O’Hagan S, Knowles JD, Halsall A, Consortium H, Wilson ID, Kell DB. Development of a robust and repeatable UPLC-MS method for the long-term metabolomic study of human serum. Anal Chem. 2009;81(4):1357–64.
Want EJ, Masson P, Michopoulos F, Wilson ID, Theodoridis G, Plumb RS, Shockcor J, Loftus N, Holmes E, Nicholson JK. Global metabolic profiling of animal and human tissues via UPLC-MS. Nat Protoc. 2013;8(1):17–32.
Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, Cheng D, Jewell K, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007;35(Database issue):D521–6.
Horai H, Arita M, Kanaya S, Nihei Y, Ikeda T, Suwa K, et al. MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom. 2010;45(7):703–14.
Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, et al. LMSD: LIPID MAPS structure database. Nucleic Acids Res. 2007;35(Database issue):D527–32.
Abdelrazig S, Safo L, Rance GA, Fay MW, Theodosiou E, Topham PD, et al. Metabolic characterisation of Magnetospirillum gryphiswaldense MSR-1 using LC-MS-based metabolite profiling. RSC Adv. 2020;10(54):32548–60. Published 2020 Sep 2.
Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27(1):29–34.
Gagnebin Y, Tonoli D, Lescuyer P, Ponte B, Seigneux SD, Martin PY, et al. Metabolomic analysis of urine samples by UHPLC-QTOF-MS: Impact of normalization strategies. Anal Chim Acta. 2017;955:27–35.
Thévenot EA, Roux A, Xu Y, Ezan E, Junot C. Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses. J Proteome Res. 2015;14(8):3322–35.
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402–8.
Tang D, Huang Q, Wei K, Yang X, Wei F, Miao J. Identification of Differentially Expressed Genes and Pathways Involved in Growth and Development of Mesona chinensis Benth Under Red- and Blue-Light Conditions. Front Plant Sci. 2021;12:761068. Published 2021 Nov 25.
Mipeshwaree Devi A, Khedashwori Devi K, Premi Devi P, Lakshmipriyari Devi M, Das S. Metabolic engineering of plant secondary metabolites: prospects and its technological challenges. Front Plant Sci. 2023;14:1171154. Published 2023 May 12.
Havsteen B. Flavonoids, a class of natural products of high pharmacological potency. Biochem Pharmacol. 1983;32(7):1141–8.
Cushnie TP, Lamb AJ. Antimicrobial activity of flavonoids [published correction appears in Int J Antimicrob Agents. 2006;27(2):181]. Int J Antimicrob Agents. 2005;26(5):343–356.
Al-Khayri JM, Sahana GR, Nagella P, Joseph BV, Alessa FM, Al-Mssallem MQ. Flavonoids as Potential Anti-Inflammatory Molecules: A Review. Molecules. 2022;27(9):2901. Published 2022 May 2.
Ginwala R, Bhavsar R, Chigbu DI, Jain P, Khan ZK. Potential Role of Flavonoids in Treating Chronic Inflammatory Diseases with a Special Focus on the Anti-Inflammatory Activity of Apigenin. Antioxid (Basel). 2019;8(2):35. Published 2019 Feb 5.
Maleki SJ, Crespo JF, Cabanillas B. Anti-inflammatory effects of flavonoids. Food Chem. 2019;299:125124.
Badshah SL, Faisal S, Muhammad A, Poulson BG, Emwas AH, Jaremko M. Antiviral activities of flavonoids. Biomed Pharmacother. 2021;140:111596.
Ullah A, Munir S, Badshah SL, Khan N, Ghani L, Poulson BG, Emwas AH, Jaremko M. Important Flavonoids and Their Role as a Therapeutic Agent. Molecules. 2020;25(22):5243. Published 2020 Nov 11.
Procházková D, Boušová I, Wilhelmová N. Antioxidant and prooxidant properties of flavonoids. Fitoterapia. 2011;82(4):513–23.
Baldim JL, de Alcântara BGV, Domingos ODS, Soares MG, Caldas IS, Novaes RD, Oliveira TB, Lago JHG, Chagas-Paula DA. The Correlation between Chemical Structures and Antioxidant, Prooxidant, and Antitrypanosomatid Properties of Flavonoids. Oxid Med Cell Longev. 2017;2017:3789856.
Zhuang WB, Li YH, Shu XC, Pu YT, Wang XJ, Wang T, Wang Z. The Classification, Molecular Structure and Biological Biosynthesis of Flavonoids, and Their Roles in Biotic and Abiotic Stresses. Molecules. 2023;28(8):3599. Published 2023 Apr 20.
Chen S, Wang X, Cheng Y, Gao H, Chen X. A Review of Classification, Biosynthesis, Biological Activities and Potential Applications of Flavonoids. Molecules. 2023;28(13):4982. Published 2023 Jun 25.
Li J, Li CF, Gou JB, Wang X, Fan RY, Zhang YS. An Alternative Pathway for Formononetin Biosynthesis in Pueraria lobata. Front Plant Sci. 2016;7:861. Published 2016 Jun 14.
LaFountain AM, Yuan YW. Repressors of anthocyanin biosynthesis. New Phytol. 2021;231(3):933–49.
Shimada N, Sasaki R, Sato S, Kaneko T, Tabata S, Aoki T, Ayabe SI. A comprehensive analysis of six dihydroflavonol 4-reductases encoded by a gene cluster of the Lotus japonicus genome. J Exp Bot. 2005;56(419):2573–85.
Fang YM, Cui MY, Liu J, Pei TL, Wei YK, Zhao Q. Study advance in biosynthesis of flavone from Scutellaria. Zhongguo Zhong Yao Za Zhi. 2020;45(20):4819–26.
Liu WX, Feng Y, Yu SH, Fan ZQ, Li XL, Li JY, Yin HF. The Flavonoid Biosynthesis Network in Plants. Int J Mol Sci. 2021;22(23):12824. Published 2021 Nov 26.
Deng XB, Bashandy H, Ainasoja M, Kontturi J, Pietiäinen M, Laitinen RAE, Albert VA, Valkonen JPT, Elomaa P, Teeri TH. Functional diversification of duplicated chalcone synthase genes in anthocyanin biosynthesis of Gerbera hybrida. New Phytol. 2014;201(4):1469–83.
Lin S, Singh RK, Moehninsi, Navarre DA. R2R3-MYB transcription factors, StmiR858 and sucrose mediate potato flavonol biosynthesis. Hortic Res. 2021;8(1):25. Published 2021 Feb 1.
Li H, Li D, Yang Z, Zeng QW, Luo YW, He NJ. Flavones Produced by Mulberry Flavone Synthase Type I Constitute a Defense Line against the Ultraviolet-B Stress. Plants (Basel). 2020;9(2):215. Published 2020 Feb 7.
Yang CX, Ye ZB. Trichomes as models for studying plant cell differentiation. Cell Mol Life Sci. 2013;70(11):1937–48.
Roka L, Koudounas K, Daras G, Zoidakis J, Vlahou A, Kalaitzis P, Hatzopoulos P. Proteome of olive non-glandular trichomes reveals protective protein network against (a)biotic challenge. J Plant Physiol. 2018;231:210–8.
Riddick EW, Simmons AM. Do plant trichomes cause more harm than good to predatory insects? Pest Manag Sci. 2014;70(11):1655–65.
Naydenova G, Georgiev G. Physiological function of non-glandular trichomes in red clover (Trifolium pratense L). J oftaAgricultural Sci Belgrade. 2013;58(3):217–22.
Lusa MG, Cardoso EC, Machado SR, Appezzato-da-Glória B. Trichomes related to an unusual method of water retention and protection of the stem apex in an arid zone perennial species. AoB Plants. 2014;7:plu088. Published 2014 Dec 19.
Ramaroson ML, Koutouan C, Helesbeux JJ, Clerc VL, Hamama L, Geoffriau E, Briard M. Role of Phenylpropanoids and Flavonoids in Plant Resistance to Pests and Diseases. Molecules. 2022;27(23):8371. Published 2022 Nov 30.
Jiang MY, Zhang JH. Role of abscissic acid in water stress-induced antioxidant defense in leaves of maize seedlings. Free Radic Res. 2002;36(9):1001–15.
Li JH, Azam M, Noreen A, Umer MA, Ilahy R, Akram MT, Qadri R, Khan MA, Rehman SU, Hussain I, Lin Q, Liu HR. Application of Methyl Jasmonate to Papaya Fruit Stored at Lower Temperature Attenuates Chilling Injury and Enhances the Antioxidant System to Maintain Quality. Foods. 2023;12(14):2743. Published 2023 Jul 19.
Polidoros AN, Mylona PV, Scandalios JG. Transgenic tobacco plants expressing the maize Cat2 gene have altered catalase levels that affect plant-pathogen interactions and resistance to oxidative stress. Transgenic Res. 2001;10(6):555–69.
Yang L, Yang CQ, Li CY, Zhao Q, Liu L, Fang X, Chen XY. Recent advances in biosynthesis of bioactive compounds in traditional Chinese medicinal plants. Sci Bull (Beijing). 2016;61:3–17.
Ramaroson ML, Koutouan C, Helesbeux JJ, Clerc VL, Hamama L, Geoffriau E, Briard M. Role of Phenylpropanoids and Flavonoids in Plant Resistance to Pests and Diseases. Molecules. 2022;27(23):8371. Published 2022 Nov 30.
Mauch-Mani B, Slusarenko AJ. Production of Salicylic Acid Precursors Is a Major Function of Phenylalanine Ammonia-Lyase in the Resistance of Arabidopsis to Peronospora parasitica. Plant Cell. 1996;8(2):203–12.
Karthikeyan M, Jayakumar V, Radhika K, Bhaskaran R, Velazhahan R, Alice D. Induction of resistance in host against the infection of leaf blight pathogen (Alternaria palandui) in onion (Allium cepa var aggregatum). Indian J Biochem Biophys. 2005;42(6):371–7.
Prakasha A, Umesha S. Biochemical and Molecular Variations of Guaiacol Peroxidase and TotalPhenols in Bacterial Wilt Pathogenesis of Solanum melongena. Biochem Anal Biochem. 2016;5(3):1–7.

No competing interests reported.

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

Status:

Version 1

Abstract

Background Platostoma palustre

Results

Conclusion

Figures

Background

Materials and methods

Plant materials

Screening micromorphological traits using scanning electron microscopy

Metabolite extraction and profiling analysis

LC-MS conditions

Qualitative and quantitative analysis of metabolites

RNA extraction, library construction, and sequencing

Verification of candidate genes by quantitative real-time PCR (qRT-PCR)

Results

The leaf appearance and shape of TW and PY

Flavonoid contents and antioxidant capacities between cultivars

Qualitative analysis of metabolites

Principal component analysis (PCA)

Discriminant analysis of orthogonal partial least squares(OPLS-DA)

Differentially accumulated metabolites(DAMs) analysis

Transcriptomics Analysis

Differentially expressed genes(DEGs) analysis

Correlation analysis between the Metabolomic and transcriptomic data

Confirmation of DEGs via qRT-PCR

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1