Transcriptome and Metabolome Profiling Provide Insights into Flavonoid Synthesis in Acanthus ilicifolius Linn

Acanthus ilicifolius is an important medicinal plant in mangrove forests, which is rich in secondary metabolites with various biological activities. In this study, we used transcriptomic analysis to obtain differentially expressed genes in the flavonoid metabolic pathway and metabolomic methods to detect changes in the types and content in the flavonoid metabolic synthesis pathway. The results showed that DEGs were identified in the mature roots vs. leaves comparison (9001 up-regulated and 8910 down-regulated), mature roots vs. stems comparison (5861 up-regulated and 7374 down-regulated), and mature stems vs. leaves comparison (10,837 up-regulated and 11,903 down-regulated). Furthermore, two AiCHS genes and four AiCHI genes were up-regulated in the mature roots vs. stems of mature A. ilicifolius, and were down-regulated in mature stems vs. leaves, which were highly expressed in the A. ilicifolius stems. A total of 215 differential metabolites were found in the roots vs. leaves of mature A. ilicifolius, 173 differential metabolites in the roots vs. stems, and 228 differential metabolites in the stems vs. leaves. The metabolomic results showed that some flavonoids in A. ilicifolius stems were higher than in the roots. A total of 18 flavonoid differential metabolites were detected in the roots, stems, and leaves of mature A. ilicifolius. In mature leaves, quercetin-3-O-glucoside-7-O-rhamnoside, gossypitrin, isoquercitrin, quercetin 3,7-bis-O-β-D-glucoside, and isorhamnetin 3-O-β-(2″-O-acetyl-β-D-glucuronide) were found in a high content, while in mature roots, di-O-methylquercetin and isorhamnetin were the major compounds. The combined analysis of the metabolome and transcriptome revealed that DEGs and differential metabolites were related to flavonoid biosynthesis. This study provides a theoretical basis for analyzing the molecular mechanism of flavonoid synthesis in A. ilicifolius and provides a reference for further research and exploitation of its medicinal value.


Introduction
A. ilicifolius Linn, a member of the family Acanthaceae, is a shrub or small tree that can reach up to two meters in height, with sturdy stalks. It grows in mangroves and tidal areas in tropical and subtropical regions [1,2]. Its unique growth environment results in a diverse array of structurally specific secondary metabolites, which exhibit a variety of biological activities and have potential medicinal value. The leaves, roots, and whole plant of A. ilicifolius are used in traditional medicine in India and China. It is considered an important medicinal plant in mangroves.
The properties of A. ilicifolius in traditional Chinese medicine are described as cold and mild in nature [3]. It is considered effective for clearing heat and detoxifying, eliminating swelling and dispersing knots, and relieving cough and asthma [4]. In China, India, and Southeast Asian countries such as Thailand, A. ilicifolius is used to treat conditions including neuralgia, rheumatism, snakebite, and paralysis [5]. In Thai folk medicine, the Acanthus genus is commonly used as a laxative, anti-inflammatory treatment for

Metabolome Analysis Methods
The samples were freeze-dried in a freeze-dryer (Scientz-100F, Ningbo Xinzhi Freeze Drying Equipment Co., Ningbo, China), and each sample was accurately weighed to 0.1 g after grinding and was dissolved in 0.6 mL of 70% methanol extract. The dissolved samples were refrigerated at 4 • C overnight, during which the samples were vortexed six times to improve the extraction rate. After centrifugation of the liquid (10,000× g, 10 min), the supernatant was aspirated, and the samples were filtered through a microporous membrane (0.22 µm pore size) and stored in a feed bottle for UPLC-MS/MS analysis [32,33]. Three biological replicate samples and four mixed samples for quality control were set up for widely targeted metabolome analysis.

UPLC Conditions
The data acquisition instrumentation system consisted mainly of Ultra Performance Liquid Chromatography (UPLC) (Shim-pack UFLC SHIMADZU CBM30A, https://www. shimadzu.com.cn/, accessed on 13 November 2022) and tandem mass spectrometry (Tandem mass spectrometry, MS/MS) (Applied Biosystems 4500 QTRAP, https://www. Liquid chromatography was performed using a Waters ACQUITY UPLC HSS T3 C18 (Waters, Milford, MA, USA) 1.8 µm, 2.1 mm × 100 mm column. The mobile phase consisted of ultra-pure water for phase A (with 0.04% acetic acid added) and acetonitrile for phase B (with 0.04% acetic acid added). The elution gradient involved a B-phase ratio of 5% at 0.00 min, which increased linearly to 95% within 10.00 min and was maintained at 95% for 1 min. From 11.00 to 11.10 min, the B-phase ratio decreased to 5% and equilibrated with 5% until 14 min. The flow rate was 0.35 mL/min, and the column temperature was set to 40 • C. The injection volume was 4 µL.
The mass spectrometry conditions used in this study involved electrospray ionization (ESI) at 550 • C, mass spectrometry at 5500 V, curtain gas (CUR) at 30 psi, collision-activated dissociation (CAD), and a mass spectrometer at 5000 V. A high parameter was set for each analysis. In the triple quadrupole (QQQ) mass spectrometer, each ion pair was scanned and detected using optimized declustering potential (DP) and collision energy (CE) values, as previously described by Chen et al. [34].
Specific fragment ions were compared to the reference for identifying secondary metabolites and additional amino acids [35]. Metabolites that shared close fragment ions were deemed to be identical compounds. Statistical analysis of secondary metabolite data was performed using Analyst 1.6.1 software (AB SCIEX, Framingham, MA, USA). Variable importance in projection (VIP) values were determined through partial least squares discriminant analysis. The differentially changed metabolites (DCMs) were selected based on the thresholds of VIP ≥ 1 and absolute Log2FC (fold change) ≥ 1. Identified metabolites were annotated using the KEGG Compound database (http://www.kegg.jp/ kegg/compound/, accessed on 15 January 2023), and annotated metabolites were mapped to the KEGG Pathway database (http://www.kegg.jp/kegg/pathway.html, accessed on 21 January 2023). Pathways with significantly regulated metabolites were mapped into MSEA (metabolite sets enrichment analysis), and their significance was determined by the hypergeometric test's p-values.

Transcriptomic Analysis and Differentially Expressed Genes
To explore genes related to flavonoid metabolism in A. ilicifolius, transcriptomic analysis was conducted on young and mature roots, stems, and leaves. Before transcriptome sequencing data were ready for subsequent analysis, quality control of raw data was required to obtain high quality data, also called clean reads. The overall clean read counts in each sample ranged from 20,006,083 to 29,156,661, producing a total of 123.5 Gb of clean data. The sequence reads were aligned to the reference genome of A. ilicifolius, with more than 84.65% of reads mapped successfully. The high-quality transcriptomic results were supported by a GC concentration of 47% and a Q30 score of over 93.39% (Table S2).
The high sensitivity of transcriptomic data enabled the detection of the gene expression levels. In this study, protein-coding genes with expression levels represented as FPKM values were sequenced and found to span six orders of magnitude, ranging from 0.01 to 10,000 ( Figure S2). Pearson's Correlation Coefficient R (PCC) was used to assess biological repeat correlation and to screen for reliable differentially expressed genes. The r 2 in this study was close to 1, indicating a strong correlation of duplicate samples, which facilitated the follow-up analysis ( Figure 1).
in each sample ranged from 20,006,083 to 29,156,661, producing a total of 123.5 Gb of clean data. The sequence reads were aligned to the reference genome of A. ilicifolius, with more than 84.65% of reads mapped successfully. The high-quality transcriptomic results were supported by a GC concentration of 47% and a Q30 score of over 93.39% (Table S2).
The high sensitivity of transcriptomic data enabled the detection of the gene expression levels. In this study, protein-coding genes with expression levels represented as FPKM values were sequenced and found to span six orders of magnitude, ranging from 0.01 to 10,000 ( Figure S2). Pearson's Correlation Coefficient R (PCC) was used to assess biological repeat correlation and to screen for reliable differentially expressed genes. The r 2 in this study was close to 1, indicating a strong correlation of duplicate samples, which facilitated the follow-up analysis ( Figure 1).
This study focused on flavonoids, a class of secondary metabolites. Flavonoid accumulation was higher in the roots, stems, and leaves of mature A. ilicifolius, prompting an analysis of the DEGs in these organs. In mature A. ilicifolius, a total of 9001 DEGs were up-regulated and 8910 DEGs were down-regulated in the mature roots vs. leaves comparison (Figure 2a). Similarly, 5861 DEGs were up-regulated and 7374 DEGs were down-regulated in the mature roots vs. stems comparison (Figure 2b), and 10,837 DEGs were up-regulated and 11,903 DEGs were down-regulated in the mature stems vs. leaves comparison (Figure 2c).  This study focused on flavonoids, a class of secondary metabolites. Flavonoid accumulation was higher in the roots, stems, and leaves of mature A. ilicifolius, prompting an analysis of the DEGs in these organs. In mature A. ilicifolius, a total of 9001 DEGs were up-regulated and 8910 DEGs were down-regulated in the mature roots vs. leaves comparison (Figure 2a). Similarly, 5861 DEGs were up-regulated and 7374 DEGs were down-regulated in the mature roots vs. stems comparison (Figure 2b), and 10,837 DEGs were up-regulated and 11,903 DEGs were down-regulated in the mature stems vs. leaves comparison (Figure 2c). DEGs in mature roots vs. leaves, roots vs. stems, and stems vs. leaves of A. ilicifolius were mainly enriched in metabolic processes, single-organism process, cell, cell part membrane, organelle, binding, and catalytic activity ( Figure 3). KEGG enrichment further indicated that the DEGs of mature roots vs. leaves and roots vs. stems of A. ilicifolius were mainly enriched in the plant-pathogen interaction and plant hormone signal transduction (Figure 4a DEGs in mature roots vs. leaves, roots vs. stems, and stems vs. leaves of A. ilicifolius were mainly enriched in metabolic processes, single-organism process, cell, cell part membrane, organelle, binding, and catalytic activity ( Figure 3). KEGG enrichment further indicated that the DEGs of mature roots vs. leaves and roots vs. stems of A. ilicifolius were mainly enriched in the plant-pathogen interaction and plant hormone signal transduction (Figure 4a,b). KEGG enrichment further indicated that DEGs of mature stems vs. leaves were mainly enriched in carbon metabolism and the biosynthesis of amino acids (Figure 4c).

Flavonoid Biosynthesis Differential Genes
In mature A. ilicifolius, four DEGs related to flavonoid biosynthesis were identified in the roots vs. leaves comparison, with two up-regulated and two down-regulated DEGs ( Figure 5a). The up-regulated DEGs were two AiC4H genes (Ail10G007150 and Ail34G006900), which encode cinnamate 4-hydroxylase. The down-regulated DEGs were AiLAR (Ail29G011710), which encodes leucoanthocyantin reducase, and AiCHI1 (Ail07G00902), which encodes chalcone isomerase. In the roots vs. stems comparison, seven flavonoid biosynthesis-related DEGs were identified, with six being up-regulated and one downregulated ( Figure 5b). The up-regulated DEGs were two AiCHS genes (Ail32G006860 and Ail08G007320), which encode chalcone synthase, and four AiCHI genes (Ail36G002000, Ail10G010430, Ail34G010000, and Ail12G001980), which encode chalcone isomerase. The down-regulated DEG was AiLAR (Ail29G011710). In the stems vs. leaves comparison, eight flavonoid biosynthesis-related DEGs were identified, with two being up-regulated and six down-regulated (Figure 5c). The up-regulated DEGs were AiCFI2 (Ail36G002000), which encodes chalcone flavanone isomerase, and AiANR (Ail41G004950), which encodes anthocyanidin reductase. The down-regulated DEGs were four AiCHI genes (Ail07G009020, Ail10G010430, Ail34G010000, and Ail31G008660), which encode chalcone isomerase, and two AiCHS genes (Ail0G005110 and Ail06G020390), which also encode chalcone synthase. The AiLAR gene encoding leucoanthocyanidin reductase was highly expressed in the mature roots of A. ilicifolius ( Figure 6). AiLAR has been shown to reduce colorless anthocyanins and anthocyanins to flavanols, mainly catechin and epicatechin, and ultimately produce proanthocyanins through the processes of translocation, oxidation, and polymerization [37]. In contrast, AiCHI genes encoding chalcone isomerase were found to be highly expressed in the mature leaves and stems of A. ilicifolius. CHI catalyzes the specific cyclization of naringenin chalcone into naringenin, which is a common intermediate of several flavonoid subclasses, including flavonoids, flavanols, flavonols, anthocyanins, proanthocyanidins, terephthalic acid, and isoflavones [38]. The AiLAR gene encoding leucoanthocyanidin reductase was highly expressed in the mature roots of A. ilicifolius ( Figure 6). AiLAR has been shown to reduce colorless anthocyanins and anthocyanins to flavanols, mainly catechin and epicatechin, and ultimately produce proanthocyanins through the processes of translocation, oxidation, and polymerization [37]. In contrast, AiCHI genes encoding chalcone isomerase were found to be highly expressed in the mature leaves and stems of A. ilicifolius. CHI catalyzes the specific cyclization of naringenin chalcone into naringenin, which is a common intermediate of several flavonoid subclasses, including flavonoids, flavanols, flavonols, anthocyanins, proanthocyanidins, terephthalic acid, and isoflavones [38].

qRT-PCR
The nine DEGs were randomly selected, and transcriptional data showed that the gene expression was greater in the mature roots compared with the mature stems and lowest in mature leaves. These were further screened for qRT-PCR analysis, and the results were consistent with the transcriptome results (Figure 7). cyanins and anthocyanins to flavanols, mainly catechin and epicatechin, and ultimately produce proanthocyanins through the processes of translocation, oxidation, and polymerization [37]. In contrast, AiCHI genes encoding chalcone isomerase were found to be highly expressed in the mature leaves and stems of A. ilicifolius. CHI catalyzes the specific cyclization of naringenin chalcone into naringenin, which is a common intermediate of several flavonoid subclasses, including flavonoids, flavanols, flavonols, anthocyanins, proanthocyanidins, terephthalic acid, and isoflavones [38]. ilicifolius mature tissues. mOR, mOS, and mOL represent mature roots, mature stems, and mature leaves, respectively. The differential expression of genes in the heatmap are treated with log2.

qRT-PCR
The nine DEGs were randomly selected, and transcriptional data showed that the gene expression was greater in the mature roots compared with the mature stems and lowest in mature leaves. These were further screened for qRT-PCR analysis, and the results were consistent with the transcriptome results ( Figure 7).

Metabolome Analysis and Differential Metabolites
PCA analysis classified overall variation as PC1 and PC2, contributing 35.43% and 22.11%, respectively (Figure 8a). The correlations between samples with the same organ were high, indicating a good repeatability of samples, as well as stability and reliability of the experimental data. The results of the hierarchical clustering of metabolite profiles of different samples showed that the same parts from the same stage were basically clustered into one subclass, while the root, stem, and leaf samples were clustered into three major classes, respectively (Figure 8b). The results of the metabolome analysis revealed that the metabolites in the young and mature parts were different.

Metabolome Analysis and Differential Metabolites
PCA analysis classified overall variation as PC1 and PC2, contributing 35.43% and 22.11%, respectively (Figure 8a). The correlations between samples with the same organ were high, indicating a good repeatability of samples, as well as stability and reliability of the experimental data. The results of the hierarchical clustering of metabolite profiles of different samples showed that the same parts from the same stage were basically clustered into one subclass, while the root, stem, and leaf samples were clustered into three major classes, respectively (Figure 8b). The results of the metabolome analysis revealed that the metabolites in the young and mature parts were different. . mRY, mSY, and mLY represent young roots, young stems, and young leaves, respectively, and mOR, mOS, and mOL represent mature roots, mature stems, and mature leaves, respectively. Figure 9a shows four mixed samples represent the quality, which were prepared from a mixture of sample extracts and used to analyze the reproducibility of the samples under the same processing method. In Figure 9b, the number after the sample name represents the different biological replicate number of samples.  (Table  S3). Our results indicate there were 377, 316, and 373 metabolites in total that were detected for mature leaf, root, and stem (Figure 9a) Of the 215 differential metabolites in the  . mRY, mSY, and mLY represent young roots, young stems, and young leaves, respectively, and mOR, mOS, and mOL represent mature roots, mature stems, and mature leaves, respectively. Figure 9a shows four mixed samples represent the quality, which were prepared from a mixture of sample extracts and used to analyze the reproducibility of the samples under the same processing method. In Figure 9b, the number after the sample name represents the different biological replicate number of samples.  (Table  S3). Our results indicate there were 377, 316, and 373 metabolites in total that were detected for mature leaf, root, and stem (Figure 9a) Of the 215 differential metabolites in the  (Table S3).
Our results indicate there were 377, 316, and 373 metabolites in total that were detected for mature leaf, root, and stem (Figure 9a) Of the 215 differential metabolites in the roots vs. leaves of mature A. ilicifolius, (Figure 9b) 68 were flavonoids (Table S4). There were 173 differential metabolites in the roots vs. stems of mature A. ilicifolius. Among them, there were 59 flavonoids (Table S5). There were 228 differential metabolites in the stems vs. leaves of mature A. ilicifolius. Among them were 59 flavonoids (Table S6). Furthermore, the KEGG pathway enrichment analysis showed that the significantly enriched pathways were a biosynthesis of the secondary metabolism, flavonoid biosynthesis, and flavone and flavonol biosynthesis in the roots and leaves (Figure 10a); flavonoid biosynthesis in the roots and stems (Figure 10b); and flavone and flavonol biosynthesis in the stems and leaves of mature A. ilicifolius (Figure 10c).  (Table S4). There were 173 differential metabolites in the roots vs. stems of mature A. ilicifolius. Among them, there were 59 flavonoids (Table S5). There were 228 differential metabolites in the stems vs. leaves of mature A. ilicifolius. Among them were 59 flavonoids (Table S6). Furthermore, the KEGG pathway enrichment analysis showed that the significantly enriched pathways were a biosynthesis of the secondary metabolism, flavonoid biosynthesis, and flavone and flavonol biosynthesis in the roots and leaves (Figure 10a); flavonoid biosynthesis in the roots and stems (Figure 10b) ; and flavone and flavonol biosynthesis in the stems and leaves of mature A. ilicifolius (Figure 10c).  (Table 1).   (Table 1). (c) mSO vs. mLO. The horizontal coordinate indicates the rich factor of each pathway, the vertical coordinate is the pathway name, and the dot color is the p value; the redder it is, the more significant the enrichment. The size of the dots represents the number of differential metabolites enriched.

Combined Analysis of Transcriptome and Metabolome Analysis
The combined analysis of the metabolome and transcriptome revealed that DEGs and differential metabolites occurred in the roots and leaves of mature A. ilicifolius, and were found to be enriched in 48 metabolic pathways (Figure 12a

Transcriptomic Analysis of Key Genes in the Flavonoid Synthesis Pathway in A. ilicifolius
Higher plants share a common core flavonoid pathway, while distinct species frequently create specialized branches to adapt to varying environmental situations [39]. Flavonoid anabolism begins with the metabolic pathway of phenylpropanoids. Phenylalanine deaminase catalyzes the conversion of phenylalanine to cinnamic acid, which is then hydroxylated to coumaric acid by cinnamic acid-4-hydroxylase [11]. Coumaric acid is converted to coumaroyl-CoA by 4-coumarate coenzyme A ligase. Chalcone synthase then catalyzes the condensation of coumaroyl-CoA and malonyl-CoA to produce chalcone, a precursor of various flavonoids. Subsequently, chalcone enters various branching pathways to produce different flavonoid classes via a series of enzymatic reactions [40]. In the present study, in A. ilicifolius mature roots vs. leaves, two AiC4H up-regulated genes, which played an important role in flavonoid synthesis, belong to the upstream genes.
The flavonoid synthesis pathway is broadly divided into two phases [41,42]. The pre-synthesis stage includes chalcone synthase (CHS), chalcone isomerase (CHI), and flavanone 3-hydroxylase (F3H), which are common genes involved in all downstream flavonoid biosynthesis pathways [43]. In this study, we found that two AiCHS genes and four AiCHI genes were up-regulated in mature roots vs. stems of mature A. ilicifolius, and down-regulated in stems vs. leaves, and these genes were highly expressed in A. ilicifolius stems. While CHS plays an important role in the first stage of flavonoid biosynthesis, where it catalyzes the stepwise condensation of 4-coumaryl-CoA and malonyl-CoA into naringenin chalcone [43].
Plant flavonoids perform various biological functions, such as protecting against UV radiation, protecting plants from pathogens and herbivores, regulating auxin transport, and signaling between microorganisms and plants, and are important pigments for flowers, fruits, seeds, and leaves [43][44][45]. The metabolomic results of this study showed that some flavonoids in A. ilicifolius stems were higher than those in roots and leaves, indicating that they played an important role in the synthesis of A. ilicifolius flavonoids.
Naringenin is a widely distributed flavonoid intermediate that serves as a precursor for various flavonoid subclasses, including anthocyanins, proanthocyanidins, terephthalic acid, and isoflavones [38]. Naringin flavanones are hydroxylated by flavanone 3-hydroxylase (F3H) to form dihydrosanninol [46], which is further hydroxylated by flavanone 3 -hydroxylase (F3 H) and flavanone 3 5 -hydroxylase (F3 5 H), to produce dihydroquercetin and dihydromyricetin. Dihydromyricetin, dihydroquercetin, and dihydromyricetin are dihydroflavonol compounds, representing new branch points in the flavonoid synthesis pathway. Dihydroflavonols are able to catalyze the formation of flavonols under the action of flavonol synthase (FLS) [47]. Alternatively, dihydroflavonols can be reduced to colorless anthocyanins by dihydroflavonol reductase (DFR) and NADPH [48]. Further oxidation by anthocyanin synthase (ANS) and modifications by various glycosylation modifying enzymes results in the formation of different types of anthocyanins [49]. Transcriptomic data from this study revealed that the AiLAR gene encoded leucoanthocyantin reductase was down-regulated in mature roots vs. leaves, and in mature roots vs. stems of mature A. ilicifolius, and the AiANR gene was up-regulated in mature roots vs. stems, indicating its potential role in flavonoid synthesis in A. ilicifolius.
Colorless anthocyanin reductase (LAR) and anthocyanin reductase (ANR) are capable of reducing colorless anthocyanins and anthocyanins to flavanolic substances, such as catechins and epicatechins, which can then be translocated, oxidized, and polymerized to form proanthocyanidins [37].
In our study, 68 differential flavonoids were detected in the roots vs. leaves of mature A. ilicifolius, 59 differential flavonoids in the roots vs. stems of mature A. ilicifolius, and 59 differential flavonoids in the stems vs. leaves of mature A. ilicifolius. Flavonoids vary greatly in content and type in different species [54]. The content of flavonoid substances also varies in different tissues or developmental stages [55]. The study results show that 18 differential flavonoid metabolites were detected in the roots, stems, and leaves of mature A. ilicifolius, and the active ingredients of many traditional Chinese medicines were flavonoid substances [56].
We also found that quercetin-3-O-glucoside-7-O-rhamnoside, gossypitrin, isoquercitrin (Quercetin 3-O-β-D glucoside), quercetin 3,7-bis-O-β-D-glucoside, and isorhamnetin 3-O-β-(2 -O-acetyl-β-D-glucuronide) were high in mature A. ilicifolius leaves, and di-Omethylquercetin and isorhamnetin were high in mature A. ilicifolius roots. The natural substance quercetin could inhibit the proliferation of human nasopharyngeal carcinoma cells (CNE1), causing them to undergo natural apoptosis [57]. The isorhamnetin in the extract had antioxidant, antiplatelet, and anticoagulant effects and could be used for the prevention and treatment of cardiovascular diseases [58]. In mature A. These results suggest that different parts of the plant may have unique flavonoid profiles, which could have implications for their potential medicinal or commercial applications. In addition, the metabolomic analysis revealed that some flavonoids were found in specific tissues/parts.
Currently, limited research has been conducted on the mechanism underlying flavonoid biosynthesis in A. ilicifolius. In our study, gene upregulation and the accumulation of metabolites (as shown in Figure 12) were not associated with several biological processes. These indicate that the biosynthesis of flavonoids is a complex process that may involve feedback loops or other regulatory mechanisms. Additionally, it is possible that many factors can influence gene expression and metabolic pathways. For example, some metabolites may be produced in response to environmental stimuli rather than as a result of the gene expression [60]. Therefore, it is important to use multiple approaches to study biological processes, including for analyzing both the gene expression and metabolite levels [61].
In the present study, differentially expressed genes were identified through transcriptome analysis, and their impact on various substances in the metabolic pathway was assessed. In addition, metabolite types and content changes within the synthetic pathway were detected using metabolomic methods. These findings are crucial for comprehensively analyzing the mechanism underlying flavonoid biosynthesis in the medicinal A. ilicifolius. The findings of this study provide valuable insights into the biosynthesis and diversity of flavonoids in A. ilicifolius, which could have implications for the development of new medicinal or commercial products.

Conclusions
Our study focused on A. ilicifolius, a significant medicinal plant in mangrove forests with valuable biological activities. We employed transcriptomic and metabolomic methods to explore changes in the flavonoid synthesis pathway, which revealed significant findings. The transcriptomic analysis identified differentially expressed genes (DEGs) with more DEGs down-regulated than up-regulated. Two AiCHS genes and four AiCHI genes were up-regulated in the mature roots vs. stems of A. ilicifolius, which were highly expressed in the stems. Metabolomic analysis also revealed that differential metabolites were related to flavonoid biosynthesis. Our study identified the key genes closely associated with flavonoid biosynthesis, elucidated the molecular mechanisms underlying flavonoid synthesis in A. ilicifolius, and analyzed the metabolite types and accumulation patterns, and will provide a reference for further research and for exploitation of the medicinal value of A. ilicifolius's flavonoid products.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/genes14030752/s1. Figure S1: Total flavonoid content of roots, stems, and leaves of A. ilicifolius. Figure S2: Comparison of FPKM density distribution of each sample. Table S1: Sequences of primers. Table S2: RNA sequencing data and quality control. Table S3: All metabolites detected in roots, stems, and leaves of A. ilicifolius. Table S4: Differential metabolites in the roots vs. leaves of mature A. ilicifolius. Table S5: Differential metabolites in the roots vs. stems of mature A. ilicifolius. Table S6: Differential metabolites in the stems vs. leaves of mature A. ilicifolius.

Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.

Data Availability Statement:
The transcriptome raw read data that support the findings of this study are available in the NCBI BioProject database (transcriptome raw read data, SRA submission: SUB12510474; and A. ilicifolius genome sequence, genome submission: SUB12869169).