Integrative analysis of the metabolome and transcriptome reveals the mechanism of flower color and antioxidant capacity in three Syringa cultivars

Abstract

Lilac plants (Syringa spp.) are extensively valued in landscaping and human health for their colorful cultivars and significantly medicinal and edible properties. The current works mainly focus on the anthocyanidin-induced pigmentation or the flavonoid-driven antioxidant activity, respectively. Nevertheless, the genetic and metabolic panorama of flavonoids, especially the metabolic flux partitioning of anthocyanidin and other flavonoids, remains ambiguous in the Syringa vulgaris plants. In this study, integrating phenotypic, RNA sequencing, and metabolomic data, this study delineates the metabolic flux partitioning of flavonoids underlying floral pigmentation and antioxidant capacity across three S. vulgaris cultivars (blue, purple, and white). A core regulatory module of seven genes governs the biosynthesis of four flavonoid metabolites (kaempferol-3,7-diglucoside, kaempferol-3-sophorose-7-glucoside, kaempferol-3-rutinoside, and rutin) and two anthocyanidin metabolites (cyanidin-4-rutinoside and delphinidin-3-rutinoside), while shikimate acyltransferase gene (SvHCT-4) acted as a pivotal node directing flux toward cyanidin-4-rutinoside and delphinidin-3-rutinoside accumulation. These flavonoids and anthocyanidins in S. vulgaris cultivars orchestrated stage-specific modulation of floral pigmentation and differentially enhanced the scavenging capacities of 2,2-Diphenyl-2-picrylhydrazyl radical (DPPH·) and hydroxyl radical (OH·). SvHCT-4 functions as a critical metabolic branch point governing metabolic flux partitioning from non-anthocyanin flavonoids to anthocyanin biosynthesis in the blue and purple cultivars of S. vulgaris, thereby enhancing floral pigmentation and antioxidant activity. The findings not only provided valuable insights into the intricate regulatory networks governing floral pigmentation and antioxidant capacity but also offered potential targets for future genetic engineering and breeding efforts aimed at enhancing these desirable traits in horticultural crops.

Introduction

Color is a key trait in plant development, classification, and evolution (Yamasaki et al., 1996). Flower pigmentation is primarily determined by the composition of six major anthocyanidins including pelargonidin, cyanidin, delphinidin, peonidin, petunidin, and malvidin in plant (Forkmann, 1991; Lepiniec et al., 2006; Qi et al., 2023). In Malus, red color is governed by the anthocyanins-to-flavonoids ratio in the fruits (Han et al., 2023). Furthermore, the spatial distribution and structural modifications of anthocyanidins modulate color transformation of flowers through sugar glycosidic linkages in ornamental flowers (Bueno et al., 2012; Grotewold, 2006; Houghton et al., 2021; Manolescu et al., 2019). On the other hand, flavonoids from edible plants exhibit well-documented therapeutic benefits, chiefly via antioxidant mechanisms, and are widely utilized as dietary bioactives to support health outcomes across diverse populations (Hanganu et al., 2021; Kim and Shin, 2012; Zhang et al., 2009; Zhou et al., 2013). Flavonoids scavenge free radicals and reduce oxidative stress in edible flowers (Jakubczyk et al., 2022; Yiğit et al., 2022). Flavonoids as well as anthocyanins from a conserved metabolic pathway synergistically regulate plant functions like tissue pigmentation and antioxidant capacity. Peonidin and cyanidin cooperatively modulate floral pigmentation in Hydrangea macrophylla and Carthamus tinctorius (Peng et al., 2021; Wang et al., 2021). Insertion of retrotransposons in Ruby1 orchestrates cold-induced anthocyanin biosynthesis in blood oranges by integrating environmental signal transduction and metabolic flux reprogramming of the entire flavonoid pathway, thereby coupling pigmentation phenotype with adaptive antioxidant functions (Butelli et al., 2012). While flavonoids and anthocyanins are well-established as key determinants of flower color and antioxidant capacity in ornamental plants, few studies focus on the mechanisms of flavonoids that coordinate pigment accumulation with antioxidant capacity.

The key regulators of the flavonoid biosynthesis encompass structural genes and transcription factors, which have been extensively investigated in plant species (Dai and Hong, 2016). The biosynthesis of flavonoids is initiated by phenylalanine ammonia-lyase (PAL) and 4-coumarate-CoA ligase (4CL), with subsequent enzymatic modifications carried out by products of structural genes (Punyasiri et al., 2004). Moreover, the shikimate acyltransferase (HCT) consumes the flavonoid precursor p-coumaroyl-CoA to generate caffeoyl-CoA, consequently suppressing flavonoid biosynthesis (Hoffmann et al., 2004). Subsequently, numerous studies reveal that the differential expression of core-structural genes such as chalcone synthase (CHS), flavonol synthase (FLS), flavone 3’-hydroxylase (F3’H), flavone 3,5-hydroxylase (F3’5’H), dihydroflavonol 4-reductase (DFR), anthocyanidin synthase (ANS), and glucuronosyltransferase (GT) across different flower colors in Rhododendron pulchrum, Tulipa fosteriana, and Grape Hyacinth (Liu et al., 2019; Xia et al., 2022; Yuan et al., 2016). UDP-glucuronosyltransferase (UGT) such as SoUGT1, PhUGT79B31, and NmF4’GT regulate anthocyanin accumulation through 3-O-glucosylation and acylation, altering flower in Syringa oblata, Petunia hybrida, and Nemophila menziesii (Knoch et al., 2018; Okitsu et al., 2018; Petit et al., 2020; Wang et al., 2025). The MYB-bHLH-WD40 (MBW) complex precisely regulates the spatiotemporal expression of structural genes in the flavonoid biosynthesis pathway (Ramsay and Glover, 2005). The R3-MYB transcriptional repressor MYBx precisely modulates the spatial distribution of anthocyanin pigmentation in grape hyacinth (Zhang et al., 2020). Integrative multi-omics profiling reveals transcriptional networks governing flavonoid and anthocyanin biosynthesis, deciphering the molecular basis of anthocyanin-mediated pigmentation in Malus, Morus alba, Clematis lanuginosa, and Cymbidium floribundum (Ma et al., 2024; Qian et al., 2025; Xia et al., 2024; Zhao et al., 2024). Despite the central role of flavonoid biosynthesis in modulating floral pigmentation and antioxidant potential in ornamental plants, few studies focused on the metabolic flux partitioning of flavonoids for determinants of flower color and antioxidant capacity.

Lilac plants (Syringa spp.) play the important role in courtyard design and landscape architecture due to the plentiful varieties with elegant branches, colorful flowers, and fragrant flowers. The Syringa plants also exhibit medicinal and edible properties in barks, fruits, and flowers, attributed to a rich array of bioactive compounds including proteins, phenolics, iridoid glycosides, lignans, flavonoids, and gallic acid (Gąsecka et al., 2023; Jakubczyk et al., 2022; Tóth et al., 2016; Zhou et al., 2008; Zhu et al., 2021a). For example, delphinidin-3-O-rutinoside and cyanidin-3-O-rutinoside are the major anthocyanins responsible for petal pigmentation in Syringa oblata and Syringa vulgaris (Deeva et al., 2022; Ma et al., 2022). Some evidences by transcriptome, genomic, and metabolomic analysis presented structural genes (CHS, DFR, ANS, F3H, F3’H, 4CL, and PAL) and transcription factors (WRKY and ERF) play key roles at different color transition stages, while the MBW complex is involved throughout the entire flower color formation process in Syringa species (Chen et al., 2022; Ma et al., 2022; Wang et al., 2022; Wang et al., 2025). Furthermore, the Syringa plants contains flavonoids like quercetin-3-O-rutinoside and kaempferol glucoside—polyphenolic compounds noted for their antioxidant properties (Jakubczyk et al., 2022). Bioactive phytochemicals from Syringa flowers and fruits regulate antioxidant activity by modulating ROS (El-Desouky and Gamal-Eldeen, 2009; Hanganu et al., 2021; Kurkin et al., 1989). The floral pigmentation pathways are linked to antioxidant flavonoid accumulation in Syringa plants. However, the current studies focused on the anthocyanidin-induced pigmentation or the flavonoid-driven antioxidant activity, respectively. Few works have concerned the dissection of the genetic and metabolic panorama, especially the metabolic flux partitioning in flavonoids that contribute to pigmentation and antioxidant abilities in Syringa.

In the study, the three cultivars of S. vulgaris with different petal colors are applied as experimental materials to investigate. By integrating phenotypic assays (total flavonoid and anthocyanin contents, radical scavenging capacity), flavonoid metabolomic profiling, and RNA sequencing, this study elucidated the physiological and molecular mechanisms governing corolla pigmentation and antioxidant capacity in three S. vulgaris varieties with distinct pigment phenotypes. Key regulatory genes and enzymes in flavonoid biosynthesis were identified, establishing a mechanistic framework for the metabolic allocation between anthocyanins and flavonoids. The study was promising to provide the theoretical insights into the regulatory networks governing floral pigmentation and antioxidant capacity, as well as the applicable potential for biobreeding for the desirable traits in lilac plants.

Materials and methods

Plant materials

The investigation employed the open-pollinated seed selection method for the blue cultivar S. vulgaris ‘Wedgewood Blue’ (SvB) to cultivate two unique varieties: the white cultivar S. vulgaris ‘Guifei III’ (SvW) and the purple cultivar S. vulgaris ‘Guifei IV’ (SvP). In detailed observations of three S. vulgaris cultivars, their corollas exhibited pronounced color transitions across developmental stages (Figure 1A). However, the metabolic and molecular mechanisms underlying these color changes remain poorly understood, establishing S. vulgaris corollas as an exemplary model for investigating flavonoid and anthocyanidin flux partitioning in relation to floral pigmentation and antioxidant capacity.

Figure 1

In the study, the SvB, as well as the offspring of the SvW and SvP, were cultivated on the germplasm nursery of Weifang Academy of Agricultural Sciences. Samples were collected from April 9^th to April 26^th, 2022. Based on the flower-bloom stage of S. vulgaris in a previous study (Ji et al., 2012), we collected samples at five distinct developmental stages: bud stage (S1), bud break stage (S2), half-open stage (S3), full-bloom stage (S4), and late-bloom stage (S5). The corollas of inflorescence heads were collected from three different cultivars of S. vulgaris. Each cultivar was represented by three independent biological replicates. For each biological replicate, three technical replicates were performed during subsequent analyses to account for instrumental and procedural variability. Subsequently, the samples were immediately snap-frozen in liquid nitrogen and stored in a refrigerator maintained at -80 °Cfor subsequent analysis.

Initial profiling of phenotypic traits, total flavonoids, and anthocyanin content across five developmental stages revealed that S2 and S3 represent metabolically stable transitional stages, characterized by minimal flux variation and absence of regulatory inflection points, unlike the dynamic shifts observed in S1, S4, and S5 stages. Given their highly similar metabolic signatures and lack of distinct transcriptional or enzymatic transitions, inclusion samples of S2 and S3 stages in follow-up metabolomic and RNA sequencing analyses would not enhance resolution of key regulatory events governing flavonoid biosynthesis. Therefore, only samples from the S1, S4, and S5 stages were subjected to comprehensive omics profiling to maximize mechanistic insight into stage-specific metabolic flux partitioning.

Determination of total flavonoid content

The 0.1 g sample was ground into a fine powder under liquid nitrogen conditions. The powder was extracted with 1 mL 60% ethanol using 500 W ultrasonic at 60 °C for 40 min, followed by centrifugation at 12,000 rpm at 25 °C for 12 minutes. The supernatant was measured using a microplate reader at 470 nm. A standard curve was constructed using the absorbance values of the rutin solutions at different concentrations, from which a regression equation was derived. The concentrations were calculated based on the regression equation. Finally, the formula for total flavonoid content was according to reported methods (Han et al., 2023) The total flavonoid content was determined as follows [V_Extraction w volume of added extraction, W was sample weight]:

Total flavonoid content (mg/g) = X × V_Extraction/W

Determination of total anthocyanin content

The 0.1 g sample was pulverized under liquid nitrogen. The powder was extracted using 1 mL of extraction solution, maintained at a temperature of 75 °C for a period of 40 minutes. Afterwards, the homogenate was centrifuged at 12,000 rpm for 10 minutes. The supernatant was adjusted to a final volume of 1 mL, and absorbance of the supernatant was measured using a microplate reader at 530 nm and 700 nm, respectively. The total anthocyanin content was determined as follows [A^sample and A^blank was the absorbance of sample and blank tube, respectively. D was dilution multiple, W was sample weight]:

ΔA =(A^sample530 - A^sample700) - (A^blank530 - A^blank700)

Total anthocyanin content (mg/g) = 0.0334 × ΔA/W × D

Determination of 2,2-Diphenyl-1-picrylhydrazyl radical clearance rate

The 0.1 g powder of sample that was ground under liquid nitrogen was mixed with 1 mL of ethanol and incubated at 40 °C for a duration of 30 minutes. Subsequently, the homogenate was centrifuged at 4000 rpm for 10 min at 4 °C. The supernatant was added to other reagents according to the instructions, vortexed, and incubated at room temperature for 30 minutes. Afterward, the absorbance of supernatant was measured at 515 nm using a microplate reader. The clearance rate of DPPH· was determined as follows [A^sample, A^control, and A^blank were absorbancea of sample, control, and blank tube, respectively]:

DPPH· clearance rate (%) = [(A^blank- (A^sample - A^control))/A^blank] × 100%

Determination of hydroxyl radical (OH·) clearance rate

The 0.1 g sample was pulverized in liquid nitrogen and homogenized with1 mL extract solution. The homogenate was centrifuged at 12,000 rpm for 10 minutes at a temperature of 4 °C. The resulting supernatant was added to the reagents in accordance with the instructions and incubated at 37 °C for one hour, followed by centrifugation at 12,000 rpm for 10 minutes at room temperature. The absorbance of supernatant was measured at 536 nm using a microplate reader. The clearance rate of OH· was determined as follows [A^sample, A^control, and A^blank were absorbances of sample, control, and blank tube, respectively]:

OH· clearance rate (%) = [(A^sample - A^control)/(A^blank - A^control)] × 100%

Flavonoids and anthocyanins metabolomic profiling

At the beginning, 0.5 g of the samples that thawed after undergoing liquid nitrogen grinding were placed within a 15 mL brown glass, and then 1 mg of peony 3-glucoside and 20 mg of myricetin-3-O-galactoside were added and thoroughly mixed with the samples. The resultant mixture was adjusted to a total volume of 4 mL with a methanol/formic acid solution (9:1, v/v) and stored in a refrigerator set at -4 °C for 12 hours. The supernatant was collected after centrifugation (2,000 rpm, 10 min), transferred to an amber vial, and subsequently dried under a gentle stream of nitrogen gas. To the remaining residue, 200 μL of methanol/formic acid solution (9:1, v/v) was added. The solution was thereafter filtered through a 0.22 μm nylon membrane to eliminate impurities for further analysis. The secondary metabolites of flavonoids and anthocyanins were quantified utilizing an ultra-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometer (UPLC-Q-TOF-MS).

RNA sequencing data analysis

Total RNA was isolated from the three cultivars at three critical developmental stages (S1, S3, and S5) utilizing the RNAprep pure plant kit (Tiangen, Beijing, China), and the purity and concentration was evaluated using a Nanodrop 2000C spectrophotometer (Thermo Scientific, Vermont, USA) and 1% (w/v) agarose gel electrophoresis. The construction of the cDNA libraries and the subsequent transcriptome sequencing were carried out by Biomarker Technologies Co., Ltd. (Beijing, China). For each sample, the mRNA was enriched using oligo (dT)-rich magnetic beads, then fragmented and used to synthesize first-strand cDNA with random primers. The second strand of cDNA was synthesized using RNase H and DNA polymerase I. Afterward, the cDNA underwent repair, poly(A) tailing, and adapter ligation. The cDNA fragments of the appropriate size were separated via agarose gel electrophoresis and amplified through PCR to generate the cDNA libraries for RNA-sequencing. The quality of the sample libraries was determined by an Agilent 2100 Bioanalyzer and Qubit 2.0. Utilizing the Sequencing By Synthesis (SBS) technology, the Illumina high-throughput sequencing platform was employed to sequence the cDNA libraries, yielding extensive and high-quality raw data.

Functional gene annotation and differential expression analysis

The sequencing data collected from the samples was trimmed using Trimmomatic-0.39 with parameter AVGQUAL 30. HISAT2 was subsequently employed to align the clean data with the reference genome of S. oblata (NGDC, https://ngdc.cncb.ac.cn/), thereby generating mapped data. The normalization process used the number of mapped reads and their transcript length, calculated using the FPKM metric (Trapnell et al., 2010). This FPKM metric serves as an indicator of the transcript or gene expression level, effectively illustrating the gene expression level. Genes were considered significantly differentially expressed if they exhibited a log2 FC ≥ 2 and a q-value < 0.05. The sequences of these differentially expressed genes (DEGs) were aligned to various databases, including Clusters of Orthologous Groups (COG), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Eukaryotic Orthologous Groups (KOG), Protein Family (Pfam), and the manually annotated and BLAST-reviewed protein sequence databases, including Swiss-Prot and Non-redundant (NR), were utilized. Through this alignment procedure, relevant annotation data were acquired. We performed Pearson correlation analysis to assess the relationship between the two variables.

Quantitative real-time PCR

Ten DEGs were chosen at random, and specific primers were designed using online software (https://www.genscript.com) and produced by Jin Weizhi Biotechnology (Beijing) Co., Ltd. β-actin (GenBank accession No. AF545569.1) was selected as the reference gene, and a specific primer pair was designed and validated for quantitative real-time PCR (qRT-PCR) applications. The qRT-PCR was carried out following the fluorescence quantification guidelines (Jędrzejuk et al., 2016). Relative expression levels were computed using the 2^{- △△CT} method.

Statistical analysis

Statistical analysis was conducted using Office 2020 (Microsoft, Redmond, WA, USA), IBM SPSS Statistics 2019 (SPSS Inc., Chicago, IL, USA), and Origin 2019 (Origin Lab, Northampton, MA, USA). Statistical significance was assessed via an independent-sample t-test or one-way ANOVA with Duncan’s multiple comparisons test. Pearson correlation coefficient was employed for the correlation analysis. Each group’s biological experiment was repeated three times, with five technical replicates, and results were presented as mean ± standard deviation.

Results

Total flavonoid and anthocyanin content analysis

Analysis of total flavonoid content was carried out in corollas of the three cultivars at S1, S2, S3, S4, and S5 stages (Figure 1A). Our analysis delineated a distinct three-cultivar trajectory of flavonoid dynamics throughout flower development. The flavonoids accumulated to peak levels in early stage flowers, then, a progressive but marked reduction occurred in parallel with the sequential stages of floral tissue differentiation and senescence. The content of total anthocyanin in SvB and SvP cultivars was consistent with the dynamics of total flavonoid content, with the peak content being reached at the S3 and S4 stages, respectively (Figure 1B). The total anthocyanin content was below the limit detected in SvW cultivars (Figure 1C). The SvP exhibited maximum content of total flavonoid at the S3 stage, while the SvB and SvW peaked at the S4 stage. Across all S. vulgaris cultivars, we pinpointed the S1, S4, and S5 as pivotal developmental stages characterized by substantial and reproducible shifts in flavonoid metabolism.

Flavonoid accumulation exhibited cultivar-specific dynamics across flower developmental stages, with distinct temporal patterns established during maturation. The SvW cultivar consistently exhibited lower total flavonoid content than SvB across all developmental stages, while the SvP cultivar maintained a higher total flavonoid content than the SvB cultivar during the S1 and S2 stages, but surpassed neither SvB nor SvW at the S4 and S5 stages, where its content declined markedly. Concurrently, anthocyanin concent in SvW remained below the limit (LOD) of detection throughout development. These results revealed that total flavonoid and anthocyanin accumulation exhibited pronounced spatiotemporal variation across developmental stages and cultivars, collectively shaping the distinct pigmentation profiles that differentiated the three genotypes.

DPPH· and OH· scavenging rates analysis

The measurements of scavenging rates of OH· and DPPH· were conducted in three cultivars of S. vulgaris at S1–S5 stages. The scavenging rate of DPPH· presented a peak at the S4 stage in SvB, with values ranging from 32.38% to 85.03% (Figure 1D). The scavenging rates of DPPH· correlated closely with total flavonoid content across all developmental stages: SvW ranged from 28.25% to 75.85%, while SvP ranged from 46.93% to 69.21%. The SvP exhibited significantly higher scavenging activity than both SvB and SvW at all stages except S4 stage, where SvB surpassed it. Conversely, SvW demonstrated significantly greater activity than SvB at S2 and S5 stages, whereas SvB showed superior activity at S1, S3, and S4 stages. The scavenging rates of OH· ranged from 10.75–30.34% for SvB, 12.45–16.39% for SvW, and 10.47–29.00% for cultivar SvP throughout the developmental stages (Figure 1E). During early developmental stages, SvB showed weaker OH· scavenging activity compared to SvW (before S3) and SvP (before S4). A striking shift occurred after the S3 stage, where SvB’s OH· scavenging capacity increased dramatically and exceeded that of the other two cultivars. Meanwhile, SvW displayed relatively consistent OH· scavenging rates across all stages, with significantly lower activity than SvP at S3 and S4 but higher activity at S5.

Across the three cultivars, radical scavenging activity exhibited a consistent biphasic trend throughout flower development, rising initially before declining progressively. Stages S1, S3, and S4 were identified as critical inflection points for both DPPH· and OH· scavenging rates. This comparative analysis suggested that differential radical scavenging capacities among the cultivars. The scavenging rates of DPPH· and OH· were consistent with the total flavonoid content, the SvB and SvP cultivars exhibiting significant DPPH· and OH· scavenging capacities at specific developmental stages, respectively. These results implied a correlation between flavonoid metabolism and antioxidant activity in the three cultivars of S. vulgaris.

The correlation analysis between the flavonoid content and radical scavenging

Correlation analysis demonstrated that total flavonoid content exhibited significant positive relationships with the scavenging rates of DPPH· and OH· throughout development in all three S. vulgaris cultivars (Supplementary Table 1). Significant correlations were found between the total flavonoid content and the scavenging rates of OH· and DPPH· in the S. vulgaris. Similarly, total anthocyanin content was positively correlated with both radical scavenging metrics, reinforcing their functional synergy in antioxidant defense. The consistent pattern across cultivars suggested that anthocyanins are not merely pigments but integral components of the antioxidant network in S. vulgaris.

Metabolome analysis

Anthocyanin and flavonoid metabolites were subjected to targeted profiling using UPLC-Q-TOF-MS. Two anthocyanin metabolites were identified in the SvB and SvP cultivar: delphinidin-3-rutinoside and cyanidin-4-rutinoside, neither of which were detected above the LOD in the SvW cultivar (Supplementary Table 2). In the two cultivars, delphinidin-3-rutinoside dominated, accounting for over 87.3% of the total anthocyanin content, underscoring its role as the principal chromophore driving coloration in these genotypes. The SvB cultivar exhibited a biphasic accumulation pattern of delphinidin-3-rutinoside during development, peaking at 197.15 μg/g in the S4 stage, followed by a decline (Figure 1F). Conversely, delphinidin-3-rutinoside content in SvP decreased progressively across the three developmental stages, peaking at 271.16 μg/g at S1 Stage. Before the S4 stage, the SvP cultivar displayed significantly higher content of delphinidin-3-rutinoside compared to the SvB cultivar. However, SvB experienced a marked increase in delphinidin-3-rutinoside, surpassing that of SvP at the S5 stage (Figure 1F). The biosynthetic trajectory of cyanidin-4-rutinoside differed markedly between cultivars: SvB displayed a monotonic decline, while SvP followed a biphasic pattern that accumulated to a transient maximum before S4, then declining (Figure 1G). Although SvP generally contained lower content than SvB at most stages, a statistically significant elevation was observed at S4 stage indicating a cultivar-and stage-specific regulatory switch in anthocyanin metabolism.

We identified four flavonoid metabolites (kaempferol-3,7-diglucoside, kaempferol-3-sophorose-7-glucose, kaempferol-3-rutinoside, and rutin) in the three cultivars of S. vulgaris (Supplementary Table 2). In the SvB and SvW: kaempferol-3,7-diglucoside became the dominant constituent (>61.7% of total flavonoid metabolites) at S4 and S5 stages, while kaempferol-3-rutinoside and rutin were the most abundant at S1 stage, implying stage-specific regulation of glycosyltransferase activity. In SvP, flavonoid metabolites remained evenly distributed throughout development, with relative proportions of all major glycosides fluctuating, indicating a lack of dominant metabolic flux toward any single pathway branch. The kaempferol-3,7-diglucoside was undetectable in all cultivars at the S1 stage (Figure 1I). In SvB and SvW, its content increased progressively across developmental stages, whereas SvP exhibited a biphasic pattern that increased to a peak followed by a decrease. At the S4 and S5 stages, the SvB cultivar contained lower content of kaempferol-3,7-diglucoside than the SvP, while the SvB was significantly higher than the SvW cultivar at the S4 stage. No significant difference existed in the content of kaempferol-3,7-diglucoside between SvW and SvP at S5 stage. In contrast, kaempferol-3-rutinoside content in SvB cultivar decreased monotonically throughout development. SvW displayed a biphasic trend, with an initial decrease followed by a marked increase, while SvP followed the opposite pattern: an initial increase followed by a sustained decrease (Figure 1I). Conversely, the kaempferol-3-rutinoside content of SvP increased initially and then decreased. The SvW was higher in content of kaempferol-3-rutinoside than in SvB but lower than in SvP at the S4 and S5 stages. The kaempferol-3-sophorose-7-glucose exhibited a sustained, stage-dependent accumulation in SvP, increasing progressively from S1 to S5, while levels in SvB and SvW remained stably low with no significant fluctuation across development (Figure 1J). By the S4 and S5 stages, SvP accumulated significantly higher content than both SvB and SvW. The content of rutin showed similar results to kaempferol-3-rutinoside in three S. vulgaris cultivars (Figure 1K). These different flavonoid metabolite accumulation trajectories across cultivars and developmental stages, which suggested differential regulation of flavonoid biosynthetic pathways in S. vulgaris.

Integrated physiological and metabolomics analysis

A correlation analysis was performed between flavonoid and anthocyanin metabolites and the scavenging rates of DPPH· and OH·, as well as the total flavonoid content in three cultivars of S. vulgaris. The lack of significant correlation between total flavonoid content and scavenging rates of DPPH· and OH· in SvB, SvW, and SvP cultivars implied that flavonoid metabolites do not serve as the principal direct antioxidants in these tissues. Nevertheless, cyanidin-4-rutinoside demonstrated a significant and positive correlation with the total anthocyanin content. These findings indicated that cyanidin-4-rutinoside serves as a dominant anthocyanin component across all three cultivars of S. vulgaris, playing a key role in modulating floral pigmentation.

Functional annotation and classification of unigenes

The results demonstrated that a total of 171.29 GB of clean data was acquired in this experiment, with an average of 5.76 GB per sample. Notably, reads containing more than 10% of N were not detected in the data. The Q30 base percentage reached 91.44% and above, and the GC content was between 43.22% and 44.21%. The high-quality clean reads of each sample were mapped to the reference genome, S. oblata, and the mapping efficiency ranged from 73.96% to 86.83%, which indicated a close genetic relationship between S. oblata and S. vulgaris (Supplementary Tables 3, 4). As above, and the GC was between 43.22% and 44.21%. Sequence alignment demonstrated that more than 80% of the conserved genes shared between the two species exhibited high nucleotide sequence similarity, thereby corroborating their close phylogenetic relationship. Additionally, annotations were identified for 47,981 unigenes in the NR database, 23,930 in KOG, 37,782 in GO, 30,845 in SwissProt, and 35,426 in Pfam (Supplementary Table 5). These results fully demonstrated the reliability of the test quality and can be used for subsequent analysis.

Differently expressed genes analysis by RNA-seq

Initially, we conducted an analysis of the differentially expressed genes (DEGs) in accordance with the screening criteria of Log₂ fold-change (Log₂FC) > 2 and false discovery rate (q) < 0.05. For deep analysis, DEGs were divided into gene sets for assay. The specific details of the gene-set-analysis groups were presented (Supplementary Tables 6, 7). Our analysis revealed that 899, 1524, and 3695 DEGs were identified in the B-d, W-d, and P-d groups, respectively, whereas 1742, 1563, and 1958 DEGs were detected in the BW-d, BP-d, and WP-d groups, respectively. The DEGs exhibited alterations in expression levels across the three stages and cultivars.

KEGG pathway annotation was carried out in the different gene-set-analysis groups (Supplementary Table 8). Across the SvB, SvW, and SvP developmental stages, DEGs were significantly enriched in a variety of biological pathways. In SvB (B-a, B-b, B-c groups), DEGs enriched included plant-pathogen interaction, plant hormone signal transduction, starch and sucrose metabolism, MAPK signaling pathway-plant, carbon metabolism, phenylpropanoid biosynthesis, biosynthesis of amino acids, and ribosome (Supplementary Table 8; Supplementary Figure 1A). In SvW (W-a, W-b, W-c groups), pathways such as plant-pathogen interaction, plant hormone signal transduction, starch and sucrose metabolism, mannose type O-glycan biosynthesis, glycerophospholipid metabolism, biosynthesis of unsaturated fatty acids, MAPK signaling pathway-plant, and phenylpropanoid biosynthesis were enriched (Supplementary Table 8; Supplementary Figure 1B). In SvP (P-a, P-b, P-c groups), enrichments covered plant-pathogen interaction, plant hormone signal transduction, amino acid biosynthesis, carbon metabolism, MAPK signaling pathway-plant, endoplasmic reticulum protein processing, pentose and glucuronate interconversions, starch and sucrose metabolism, and phenylpropanoid biosynthesis (Supplementary Table 8; Supplementary Figure 1C).

In DEGs between SvB and SvW cultivars, a large number of DEGs were enriched in plant-pathogen interaction, plant hormone signaling, starch and sucrose metabolism, MAPK signaling pathway-plant, carbon metabolism, biosynthesis of amino acids, protein processing in endoplasmic reticulum, and phenylpropanoid biosynthesis (Supplementary Table 8; Supplementary Figure 2A). In DEGs between SvB and SvP cultivars, DEGs were encompassed in ribosomes, plant-pathogen interaction, plant hormone signaling, starch and sucrose metabolism, MAPK signaling pathway-plant, phenylpropanoid biosynthesis, carbon metabolism, and biosynthesis of amino acids (Supplementary Table 8; Supplementary Figure 2B). Between SvB and SvP cultivars, most DEGs were predominantly represented in plant-pathogen interaction, plant hormone signal transduction, MAPK signaling pathway-plant, protein processing in endoplasmic reticulum, starch and sucrose metabolism, pentose and glucuronate interconversion, and endocytosis (Supplementary Table 8; Supplementary Figure 2C).

Across the six gene-set-analysis groups (B-d, W-d, P-d, BW-d, BP-d, WP-d), the DEGs were significantly enriched in canonical pathways including plant–pathogen interaction, plant hormone signal transduction, starch and sucrose metabolism, MAPK signaling, and ribosomal function (Supplementary Table 8; Figure 2). Crucially, flavonoid biosynthesis, anthocyanin biosynthesis, and flavone and flavonol biosynthesis exhibited consistent and significant enrichment in every group, underscoring their non-redundant role in cultivar-specific metabolic reprogramming. Notably, the B-d and P-d groups displayed the most pronounced enrichment in anthocyanin biosynthesis, while the W-d, BW-d, BP-d, and WP-d groups retained substantial activation across the broader flavonoid network. Additional pathways of glutathione metabolism, α-linolenic acid metabolism, and zeatin biosynthesis further delineated cultivar-specific metabolic priorities. Collectively, these data revealed that anthocyanin biosynthesis was not merely a co-activated process but emerged as a defining molecular signature that discriminates between S. vulgaris cultivars, likely underpinning divergent floral pigmentation and stress-responsive flavonoid partitioning.

Figure 2

DEGs analysis in flavonoid-related metabolism

We mined a large number of DEGs related to the flavonoid metabolism in different gene-set-analysis groups (Figure 3A; Supplementary Table 10). In this study, we discovered that HCTs constituted the most significant component of the DEGs, with 34 HCTs identified across various gene-set-analysis groups (Supplementary Figure 4B). We found that down-regulated genes outnumbered up-regulated genes across developmental stages in three S. vulgaris cultivars. Expression levels varied significantly among cultivars and stages, with genes up-regulated or down-regulated depending on these factors. These results indicated that these genes regulated a complex flavonoid mechanism in S. vulgaris, contributing to differences between cultivars and their developmental stages. Ten genes were randomly selected for validation via qRT-PCR, including SvPAL, Sv4CL-3, SvCHS, SvCHS-1, SvF3’H, SvDFR, SvANS, SvUFGT, SvUGT79B1-1, and glucoside malonyltransferase gene (Sv3AT) (Figure 3H). The expression profiles of these genes, as quantified by qRT-PCR, exhibited strong concordance with the transcript levels derived from RNA-seq data, thereby validating the reliability of the RNA-sequencing data for research.

Figure 3

We extracted flavonoid-related DEGs from the B-d, W-d, P-d, BW-d, BP-d, and WP-d gene-set-analysis groups, with 21, 19, 32, 13, 12, and 12 DEGs, respectively (Figure 3A). In the B-d group, most DEGs were significantly down-regulated (Figure 3B). However, SvHCT, SvANS, and SvUFT75C1 exhibited an initial up-regulation followed by down-regulation, whereas Sv4CL-2 and SvHCT-1 remained up-regulated throughout development. Similar expression patterns were observed in the W-d group (Figure 3C). In the P-d group, half of the flavonoid-related DEGs peaked at the S4 stage, while the remaining half showed sustained down-regulation (Figure 3D). These results indicated that multiple flavonoid biosynthesis genes were dynamically regulated during developmental transitions across the three S. vulgaris cultivars, with SvP appearing to regulate a distinct subset of these genes under special transcriptional regulation. Moreover, one 4CL, one coumaroylquinate 3’-Hydroxylase gene (C3’H), six HCTs, and four GTs in BW-d, had significantly different expression levels between the SvB and SvW S. vulgaris cultivars at the three stages (Figure 3E). Two 4CLs, five HCTs, one CHS, one phlorizin synthase gene (PGT), and one ANS always existed in the DEGs between the SvB and SvP cultivars (Figure 3F). There were continual expression differences in five 4CLs, three HCTs, one FLS, one CHS, and two AT/GTs during developmental stages of the SvW and SvP cultivars (Figure 3G). The results showed that different cultivars of S. vulgaris have different mechanisms for flavonoids, and 4CLs and HCTs may play an important role in the mechanisms.

A total of 11 flavonoid-related DEGs were identified across the B-d, W-d, P-d, BW-d, BP-d, and WP-d groups, revealing cultivar and development stage-specific transcript dynamics in S. vulgaris (Figure 4). In SvB, Sv4CL-2, SvHCT-4, SvANS, and SvUGT75C1 exhibited varying expression at three stages (Figure 4B). Notably, Sv4CL-2 expression differed significantly between SvW and SvP, while SvHCT-4 was consistently upregulated in SvB compared to SvW across the three stages. The SvANS and SvUGT75C1 in SvB showed significantly higher expression than in SvP and SvW, respectively. The Sv4CL-10 in SvB varied across developmental stages and was markedly suppressed relative to SvW. In SvP, the Sv4CL-12 and SvHCT-14 displayed stage-dependent expression, with Sv4CL-12 significantly elevated compared to both SvW and SvB, whereas SvHCT-14 was repressed relative to SvW. The SvHCT-22 and SvHCT-23 in SvP also exhibited developmental regulation and were significantly divergent from SvW and SvB, respectively. The SvCHS-1 expression in SvP varied across stages and was consistently lower than in both SvW and SvB. The isoflavone 7-O-glucoside-6’-O-malonyltransferase gene (SvIF7MAT) in SvP followed a declining trend during development but remained consistently higher than in SvW across all stages. Collectively, these spatiotemporal expression patterns implicate these DEGs as key regulators underlying the cultivar-specific divergence in flavonoid metabolism during flower development in the S. vulgaris.

Figure 4

DEGs analysis in antioxidant-related pathways

We conducted a further analysis of the DEGs associated with antioxidant activity. In our gene-set-analysis groups, we identified 317 DEGs involved in the ascorbate-glutathione cycle and enzyme defense system (Supplementary Table 10; Figure 5). These genes encode key components of the enzymatic antioxidant defense system, including ascorbate peroxidase (APX), glutathione peroxidase (GPX), glutathione reductase (GR), glutathione S-transferase (GST), dehydroascorbate reductase (DHAR), superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT). We extracted the antioxidant-related DEGs in the B-d, W-d, and P-d gene-set-analysis groups, with 13, 22, and 22 DEGs, respectively (Supplementary Table 10; Figures 5A-C). These DEGs regulated the antioxidant system during the developmental stages of the three cultivars. The number of DEGs was significantly higher in the P-d group than in the B-d and W-d groups, indicating antioxidant system significantly changes at SvP cultivars’ development. The DEGs associated with antioxidant activity were observed, with 9, 12, and 9 DEGs identified in the BW-d, BP-d, and WP-d gene-set-analysis groups, respectively (Supplementary Table 10; Figures 5D-F). These DEGs collectively orchestrate genotype-specific modulation of the antioxidant defense system. Furthermore, the core set of 5 DEGs (SvGST-1, SvGSTF11, SvPOD12, SvGR, glutamate cysteine ligase gene (SvGCL)) was found to regulate both developmental and genotypic variations in antioxidant systems, highlighting their critical role in the antioxidant mechanism of S. vulgaris (Supplementary Table 10; Figure 5G).

Figure 5

Integrative analysis in flavonoid-related metabolism

The structural DEGs that impact the metabolism of anthocyanins, flavones, and flavonols were identified from different gene-set-analysis groups (Figures 6A-E). Correlation analysis identified genes highly correlated with total flavonoid and anthocyanin content across three cultivars and stages (Figures 6F, G; Supplementary Figure 3; Supplementary Table 11). Among them, total flavonoid content was highly significant and positively correlated with the expression levels of SvHCT, SvCHS-1, and Sv4CL-4 in the cultivars, while SvPAL-1, Sv4CL-6, and chalcone isomerase (SvCHI-1) also exhibited significant positive correlations with it. There were significant and negative correlations with total flavonoid content in the expression of Sv4CL-3, Sv4CL-5, SvHCT-10, SvHCT-22, SvF3H-1, SvDFR-2, SvDFR-3, flavanone 7-O-glucoside 2’’-O-beta-L-rhamnosyltransferase (SvC12RT1), and SvUGT79B1, Sv3AT-1 in the cultivars. Our findings collectively demonstrate that these DEGs were pivotal regulators of flavonoid biosynthesis and accumulation in S. vulgaris, with SvCHS-1 and Sv4CL-4 emerging as particularly critical contributors to this metabolic process. Moreover, significant and positive correlations with total anthocyanin content were found in the Sv4CL, Sv4CL-4, SvHCT, Sv4CL-8, Sv4CL-12, and SvUGT75C1-1. They positively regulated the synthesis of anthocyanins in the three cultivars of S. vulgaris. Thus, the Sv4CL-4, SvHCT, SvCHS-1, SvCHI-1, SvUGT75C1-1, and SvC12RT1 were core regulatory genes that directly governed flavonoid and anthocyanin content in the S. vulgaris analyze (Figure 6H; Supplementary Figure 3; Supplementary Table 11).

Figure 6

A correlation analysis was conducted between DEGs and metabolites of anthocyanins and flavonoids. The expression levels of Sv4CL-8 and Sv4CL-12 exhibited extremely significant and positive correlations with delphinidin-3-rutinoside and cyanidin-4-rutinoside, respectively (Supplementary Figure 3; Supplementary Table 11). The expression level of SvHCT-4 exhibited extremely significant positive correlations with both delphinidin-3-rutinoside and cyanidin-4-rutinoside content (Figure 7A; Supplementary Figure 3; Supplementary Table 11), suggesting its potential role as a positive regulator in the biosynthesis of these anthocyanins and a key contributor to corolla pigmentation in S. vulgaris. In contrast, kaempferol-3,7-diglucoside accumulation was strongly positively correlated with the expression of SvPAL-1, SvFLS, and SvUGT75C1-2, while showing highly significant negative correlations with SvHCT-8, SvCHS, SvCHI, and SvDFR (Figure 7B; Supplementary Figure 3; Supplementary Table 11). Notably, a total of 15 structural genes and eight HCT family members were negatively associated with kaempferol-3,7-diglucoside levels. Furthermore, SvCHS-2 and SvFLS-1 expression was positively correlated with the accumulation of kaempferol-3-rutinoside and kaempferol-3-sophoroside-7-glucoside, respectively, highlighting their specialized roles in flavonol glycoside diversification (Figure 7C; Supplementary Figure 3; Supplementary Table 11). Meanwhile, the expression levels of SvHCT-24 and Sv4CL-14 showed highly significant and positive correlations with kaempferol-3-sophoroside-7-glucoside, while exhibited significant and positive correlations with kaempferol-3-rutinoside. The rutin maintained extremely significant and positive correlations with Sv4CL-13, SvF3H-2, and SvFLS-1, and also showed significant and positive correlations with the SvPAL-3 and SvF3H-3 (Figure 7D; Supplementary Figure 3; Supplementary Table 11). These findings indicated that specific DEGs might be implicated in the metabolic processes of four flavonoid derivatives.

Figure 7

Excavation and analysis identified a core set of genes essential for flavonoid synthesis in three cultivars, including Sv4CL-4, SvHCT, SvHCT-4, SvCHS-1, SvCHI-1, SvUGT75C1-1, and SvC12RT1. Among these, SvHCT-4 specifically targets delphinidin-3-rutinoside and cyanidin-4-rutinoside, which were the primary pigments driving color variation in S. vulgaris. Subsequent metabolite correlation analysis revealed that SvHCT, SvCHS-2, and SvFLS-1, and others, directly regulate the biosynthesis of six key flavonoid compounds (Figure 7E).

Integrative analysis in antioxidant-related pathways

A strong association was observed between flavonoid metabolism and the antioxidant capacity in S. vulgaris. Comprehensive correlation analysis among the DEGs that were involved in flavonoids synthesis and scavenging rates of DPPH· and OH· in S. vulgaris. Notably, Sv4CL-4, SvHCT, SvCHS-1, and SvC12RT1 exhibited extremely significant and positive correlations with the scavenging rate of DPPH· (Supplementary Table 11; Figure 8C). However, there were significant and negative correlations with scavenging rate of DPPH· in the expression levels of DEGs, including the SvHCT-7, SvHCT-8, SvHCT-10, SvHCT-12, SvHCT-16, and SvHCT-19 (Figure 8C; Supplementary Figure 3; Supplementary Table 11). These genes may negatively regulate the DPPH· and OH· scavenging activities in the three cultivars of S. vulgaris. Furthermore, the expression levels of Sv4CL-1, Sv4CL-4, SvHCT, SvCHI-1, and SvUGT75C1–1 also demonstrated extremely significant and positive correlations with the scavenging rate of OH· in the three cultivars. These outcomes further underscore the significant impact of these genes on the DPPH· and OH· scavenging activities within S. vulgaris. The expression levels of Sv4CL-4, SvHCT, SvCHS-1, SvCHI-1, and SvUGT75C1–1 may positively regulate the flavonoid and anthocyanin synthesis to increase the DPPH· and OH· scavenging activities in the three cultivars (Figure 9).

Figure 8

Figure 9

Additionally, the correlation analysis between the scavenging rates of DPPH· and OH·, and DEGs in antioxidant-related pathways, and 74 and 31 DEGs that demonstrated significant correlations in the three cultivars of S. vulgaris, respectively (Figures 8A, B; Supplementary Figure 3; Supplementary Table 11). Our correlation analysis identified 18 and 14 DEGs with extremely significant correlations to DPPH· and OH· scavenging rates, respectively. Among these, isocitrate dehydrogenase genes (SvIDH-4 and SvIDH-1) expression levels showed the strongest positive correlations with DPPH· and OH· scavenging rates, respectively. Conversely, SvGCL and SvCAT transcript levels exhibited the most significant negative correlations with these antioxidant metrics. We also observed highly significant positive correlations between SvPOD-3, SvGST, SvGST-2, SvGPX-2 expression and both DPPH· and OH· scavenging rates. Conversely, there were highly significant and positive correlations between the specific activity of SvCAT in the three cultivars and the scavenging capacities of DPPH· and OH·. The findings indicated that the molecular antioxidant mechanisms in the three cultivars of S. vulgaris were substantially modified by these DEGs. These DEGs likely constitute pivotal regulatory nodes within the flavonoid-mediated antioxidant pathways, and need deep research.

Discussion

Two anthocyanin metabolites were key factors influencing floral pigmentation in cultivars of S. vulgaris

Flower color variation is a common phenomenon in nature, such as the changes from red, purple, or pink to yellow or white. Flower color transition from blue/purple to white is observed in Ipomoea purpurea, Petunia hybrida, Parrya nudicaulis, and Torenia fournieri (Dick et al., 2011; Morita et al., 2005; Nishijima et al., 2013; Zhang and Lu, 2016). The flower color of ornamental plants is determined by flavonoids and anthocyanins (Shen et al., 2024). In orchids, flavonoid content and specific pigment profiles that govern the transition from red to purple floral pigmentation (Li et al., 2020; Wang et al., 2011). The anthocyanins of different types and concentrations cause various flower colors (red, pink, and purple) in Rosa, Pleione limprichtii, and Tulipa fosteriana (Wan et al., 2019; Zhang et al., 2019; Yeon and Kim, 2020; Yuan et al., 2014). Flavonols and flavanols frequently serve as co-pigments of anthocyanins, and on certain occasions, they may impart a white hue to the blueberry (Iwashina, 2015; Sun et al., 2022). The delphinidin-3-O-rutinoside and cyanidin-3-O-rutinoside are key anthocyanins in the color metabolism of S. oblata, while cyanidin-3-O-glucoside and peonidin-3,5-di-O-glucoside regulate spotting in Paeonia suffruticosa (Gu et al., 2019; Ma et al., 2022). In the present study, a marked divergence in floral pigmentation was observed among the three cultivars of S. vulgaris, which directly correlated with dynamic fluctuations in total flavonoid and anthocyanin accumulation (Figure 1). This was consistent with blue petal coloration depending on anthocyanin synthesis and accumulation (Yoshida et al., 2009). The metabolomic profiling identified delphinidin-3-O-rutinoside and cyanidin-4-O-rutinoside as the dominant anthocyanins in the blue and purple flowered cultivars (SvB and SvP), with content strongly correlated to total anthocyanin levels (Figure 9). The absence of specific anthocyanin metabolites in white S. vulgaris cultivar caused the loss of intense purple-blue pigmentation, whereas their accumulation in colored cultivars directly determines the petal color phenotype. Cyanidin-derived anthocyanins, the driver of purple pigmentation in our study, exhibit the same patterns observed in Jasminum officinale and Paeonia suffruticosa, demonstrating this pathway is evolutionarily conserved across phylogenetically distant genera (Yang et al., 2020; Zhou et al., 2022).

Moreover, the accumulation and depletion of anthocyanins may be associated with gene expression in anthocyanin biosynthesis. The 4CL exhibits substrate specificity and its activity positively correlated to total anthocyanin and flavonol content in plant (Liu et al., 2021). Silencing of CHS in Dahlia pinnata and Petunia hybrida show white areas in petals (Morita et al., 2012; Ohno et al., 2011). As color of petals change from red to white, anthocyanin content and UFGT expression significantly decrease in Nelumbo nucifera, suggesting UFGT protein may be key to inlaid petal formation (Deng et al., 2023; Liu et al., 2022). Previous studies identified and functionally analyzed many genes linked to floral pigmentation in S. oblata, such as CHS, C4H, and F3H (Wang et al., 2016, 2018, 2017; Zheng et al., 2015). The HCT gene of Physcomitrella patens acts as a critical regulatory node that orchestrates the metabolic flux of the phenylpropanoid pathway throughout its reproductive developmental process, laying a foundational metabolic basis for the adaptation of early land plants to terrestrial environments (Kriegshauser et al., 2021). In three cultivars of S. vulgaris, flavonoid and anthocyanin content were regulated by a core set of 7 DEGs (Sv4CL-4, SvHCT, SvHCT-4, SvCHS-1, SvCHI-1, SvUGT75C1-1, and SvC12RT1) (Figure 9; Supplementary Table 11). This indicated that these genes may affect the floral pigmentation of S. vulgaris by regulating flavonoid and anthocyanin metabolites. Notably, SvHCT-4 exhibited an extremely significant positive correlation with the contents of cyanidin-4-rutinoside and delphinidin-3-rutinoside (Figure 8). These results suggest that SvHCT-4 likely contributes to fine-tuning the coloration of S. vulgaris. Previous studies on HCT genes mainly focused on lignin biosynthesis and stress response, with little attention to floral pigmentation. This study ‌hypothesized SvHCT-4 as a positive regulator to key anthocyanins, filling a knowledge gap and offering new insights into petal color mechanisms in S. vulgaris. Further validation through over-expression or knockout could clarify its role and enable targeted pigment manipulation for horticulture.

Flavonoid biosynthesis is tightly governed by a network of transcription factors and environmental cues, with pigment transport and subcellular deposition further shaping the final petal color phenotype. WRKY and ERF transcription factors play distinct stage-specific roles in mediating flower color transitions of S. oblata (e.g., purplish-red to blue-purple, purple to light purple), while the MBW complex functions as a core regulator throughout the entire process of floral pigmentation (Ma et al., 2022). In lilac flower development, the lower expression of SobHLH130 reduces SoMYB44 transcripts and depresses transcriptional regulation of SoUFGT1, thus diminishing anthocyanin biosynthesis, leading to the fading of petal color (Wang et al., 2025). Transcription factors (PpBBX16, LvBBX24, and LvbZIP44) synergistically activate anthocyanidin biosynthesis genes to mediate light-responsive anthocyanidin accumulation in pear and petals of lilies (Bai et al., 2017; Gao Z. et al., 2024). Salt stress exerted an influence on the MYB5-ANR signaling pathway, leading to altered anthocyanins profiles in roses (Wang et al., 2024). Anthocyanin content dynamics were tightly coupled with SvHCT-4 expression across developmental stages in three S. vulgaris cultivars (Figure 8; Supplementary Table 11). However, the precise regulatory function of SvHCT-4 in flower color transitions awaits further investigation in the S. vulgaris. Comparative gene expression analysis will identify transcription factors that are co-expressed with SvHCT-4 or target structure genes of flavonoids, and then clarify their roles in anthocyanin metabolism and flower color. The investigation of interactions between SvHCT-4 and other enzymes may elucidate the basis of differential anthocyanin accumulation. Environmental factors, like light and temperature, should be considered for their interaction with regulatory pathways. Addressing these aspects can enhance understanding of flower color mechanisms in S. vulgaris, which can serve as a core reference for targeted breeding strategies toward desirable floral traits.

Flavonoids modulated the antioxidant activity in cultivars of S. vulgaris

Flower color polymorphism is influenced by various pigment content, which increases the ornamental value of the plants and may have other physiological and ecological effects (Paine et al., 2019; Vaidya et al., 2018). The varying colors of roses exhibit distinct pigment compositions, which lead to differing antioxidant activities (Alizadeh and Fattahi, 2021). Similarly, the antioxidant potency of violas (Viola × wittrockiana) is influenced by their flower color (Ikeura et al., 2023). Flavonoids contribute to antioxidant capacity by scavenging hydroxyl radicals, superoxide anions, and DPPH radicals, and increased flavonol accumulation, which can enhance both antioxidant defense and drought tolerance in Arabidopsis (Nakabayashi et al., 2014). The chalcone, flavanone, procyanidins, isoflavone, and flavanols exhibit significant antioxidant activity in plants (Barreca et al., 2017; Gourlay et al., 2020; Sahu et al., 2012; Tian et al., 2014; Wen et al., 2013). The anthocyanins also exhibit extensive biological activities, such as anti-inflammatory and anticancer in rats and plants (Fang et al., 2022; Manzoor et al., 2022). Kaempferol-3-O-glucoside, the major metabolite in the leaf extract of Erica multiflora, showed high antioxidant activity and reduced the levels of pro-inflammatory cytokines (Khlifi et al., 2020). In this study, the total flavonoid content of the three cultivars of S. vulgaris were positively correlated with DPPH· and OH· scavenging abilities, aligning with previous research findings (Supplementary Table 1). The flavonoid content exerted a significant impact on the antioxidant properties of the three cultivars of S. vulgaris, and these properties varied among different cultivars and developmental stages. There were 22 and 5 flavonoid-related genes exhibited extremely significant correlations with DPPH· and OH· scavenging rates in the cultivars of S. vulgaris, respectively (Supplementary Table 11). The differential expression of these genes underpinned the observed variation in antioxidant activity across the three S. vulgaris cultivars at different stages. The stage-specific genetic modulation represents a viable strategy for enhancing their antioxidant capacity.

Antioxidant enzymes serve as a secondary line of defense in the antioxidant mechanism of plants. Under ROS stress, the activity of antioxidant enzymes, including SOD, CAT, DHAR, and APX, demonstrated an elevation (Zhu et al., 2021b). Eighteen and fourteen DEGs related to antioxidant enzymes in S. vulgaris plants showed strong correlations with DPPH· and OH· scavenging activities, respectively (Supplementary Table 11). The SvPOD-3, SvGST, SvGST-2, and SvGPX-2 exhibited strong positive correlations with both DPPH· and OH· scavenging rates. These results further demonstrated that there were significant differences in the antioxidant activity of the three cultivars. The phenylpropanoids and glucosinolates are the two classes of secondary metabolites that can help plants survive during stress conditions (Kim et al., 2020). The flavonoids, such as luteolin, apigenin, and citrinin, display remarkable antioxidant properties through the suppression of pertinent gene expression, the stimulation of metabolic pathways, and the modulation of enzyme activity (Chagas et al., 2022; Heim et al., 2002). The significant correlations observed between these DEGs and the scavenging rates of DPPH· and OH· underscore their integral role in fortifying the plant’s antioxidant defense machinery against oxidative stress. Moreover, the flavonoid and anthocyanin metabolites may act as upstream regulators, fine-tuning antioxidant capacity through the transcriptional modulation of key genes that encode antioxidant enzymes in S. vulgaris. These findings elucidated the molecular basis of oxidative stress resilience in S. vulgaris and provided a genetic and metabolic framework for breeding programs to enhance its antioxidant potential for nutritional and pharmaceutical uses.

Numerous regulators associated with flavonoids and antioxidant activity have been identified across various plants in previous studies (Shen et al., 2022). The six flavonoid metabolites were detected in three S. vulgaris cultivars but showed no significant correlation with DPPH· and OH· clearance rates. The result indicated no direct influence between the flavonoid metabolites and antioxidant activity. This discrepancy may stem from several factors. First, flavonoids mediate antioxidant effects in concert with co-occurring phytochemicals through cooperative redox interactions (Varga et al., 2019). Second, DPPH· and OH· in vitro assays may not fully reflect in vivo mechanisms, where flavonoids may up-regulate cellular defense systems rather than directly scavenge radicals (Heim et al., 2002). Additionally, variations in flavonoid glycosylation or methylation could reduce their solubility, limiting reactivity under assay conditions (Tian et al., 2024). In this study, we characterized flavonoid-related and antioxidant-related genes that demonstrated significant correlations with the scavenging of DPPH^- and OH^- radicals. Future studies will focus on these genes, and will conduct regulatory mechanism analysis to further validate the functional roles of these genes. Subsequent investigations on the synergistic relationships between these six anthocyanin and flavonoid metabolites and other plant endogenous substances will expose novel antioxidant mechanisms that remain invisible in single-metabolite detection workflows.

Metabolic flux partitioning of the flavonoids coordinates floral pigmentation and antioxidant activity in cultivars of S. vulgaris

As major plant secondary metabolites, flavonoids including anthocyanins contribute both vibrant pigmentation and robust antioxidant activity. The anthocyanin content is generally closely related to the antioxidant capacity in black soybeans (Hashimoto et al., 2015). The different colors of potatoes exhibit varying antioxidant activities, and this activity significantly increases with the enhancement of anthocyanin content (Lachman et al., 2009; Xiao et al., 2016). Heterologous expression of snapdragon transcription factors Delila and Rosea1 in tomato fruit drove massive anthocyanin accumulation, conferring intense purple pigmentation and a threefold enhancement of antioxidant capacity (Butelli et al., 2008). The GbMYBF2 expression is negatively correlated with flavonoid accumulation in Ginkgo biloba leaves, and inhibits gene expressions such as F3H, CHS, FLS, and ANS, significantly reducing quercetin, kaempferol, and anthocyanin content (Xu et al., 2014). However, when flavonoid metabolism is perturbed, anthocyanin biosynthesis and non-anthocyanin sometimes flavonoids display fundamentally distinct regulatory trajectories. For instance, ectopic expression of the FLS gene from Camellia sinensis promotes the accumulation of kaempferol and reduced the anthocyanin content of flowers in tobacco (Jiang et al., 2020). When ectopically expressed genes of Meconopsis integrifolia in tobacco, MiDFR6 directed dihydromyricetin into the delphinidin branch of the anthocyanin pathway, while MiFLS2 siphoned the shared dihydroflavonol substrate toward flavonol synthesis (Ou et al., 2026). In the study, all three cultivars exhibited synchronized peaks in flavonoid accumulation, anthocyanin biosynthesis, and antioxidant capacity at the half-open and full-flowering stages. The result revealed a conserved temporal coordination of secondary metabolite dynamics across genotypes (Figure 1). This temporal congruence supported a shared regulatory framework governing both floral pigmentation and antioxidant capacity. However, the white cultivar was devoid of detectable anthocyanins, which retained the antioxidant activity induced by non-anthocyanin flavonoids. This finding demonstrated that anthocyanin biosynthesis and antioxidant activity by non-anthocyanin flavonoids are governed by distinct regulatory trajectories. Flavonoids orchestrated both floral pigmentation and antioxidant capacity in S. vulgaris, yet their co-regulation was not mediated by a unified pathway. The metabolic flux partitioning mechanisms of flavonoids is required to elucidate the exact mechanisms and fully exploit their potential.

However, few studies systematically investigated the underlying metabolic flux partitioning mechanism responsible for the biosynthesis and regulation of flavonoids, leaving a significant gap in our understanding of this critical biochemical pathway. In the study, the flavonoid metabolism of S. vulgaris was regulated by a core set of the 7 DEGs including SvHCT-4. Meanwhile, the SvHCT-4 redirected metabolic flux from the general phenylpropanoid pathway to anthocyanin biosynthesis, thereby enhancing the accumulation of cyanidin-4-O-rutinoside and delphinidin-3-O-rutinoside in the blue and purple cultivars S. vulgaris (Figures 8, 9). These findings revealed competitive yet coordinated mechanisms governing metabolic flux partitioning of flavonoids, which impact on floral pigmentation and antioxidant activity in S. vulgaris. HCT functions as a key regulatory node that that partitions p-coumaroyl-CoA flux between anthocyanin and lignin/flavonol biosynthesis by catalyzing its esterification with shikimate or quinate, thereby governing floral coloration and antioxidant capacity. Recent evidence shows HCT acts as a metabolic gatekeeper, with its expression and substrate affinity balancing pigments and flavonols to influence developmental signaling and environmental adaptation (Gao Y. et al., 2024). The HCT-encoding gene SvHCT-4 regulated the homeostasis of flavonoid and anthocyanin biosynthesis of S. vulgaris, while influencing the antioxidant activity of flavonoid derivatives. These mechanisms were manifested not only in the interdependent constraints across distinct metabolic pathways but also in the precise modulation of metabolic flux through differential expression of genes encoding key enzymes. This multi-level regulatory network ultimately determines the specific combination of flower color phenotypes and antioxidant capacity across different cultivars.

Since other factors also affect floral pigmentation and antioxidant properties, a comprehensive and in-depth analysis of specific regulatory mechanisms is still needed. Further studies are warranted to explore the interactions between these identified genes and metabolites, as well as their roles in response to environmental factors, to fully elucidate the complex mechanisms underlying the floral pigmentation and antioxidant properties of S. vulgaris flowers. Future research should focus on elucidating environmental factors such as light, temperature, and soil conditions to explore their interactions with specific genes and enzymes that are involved in flavonoid biosynthesis in S. vulgaris. Additionally, further elucidation of the applications of these flavonoids across horticultural breeding, pharmaceutical development, and nutritional science may yield fundamental insights into their biological roles while revealing their commercial promise.

Conclusion

In conclusion, this research had significantly advanced our understanding of the molecular and metabolic underpinnings of flower color and antioxidant activity in three different color cultivars of Syringa vulgaris. By employing a multi-omics approach, we had identified key metabolites and genes involved in flavonoid and anthocyanin biosynthesis, elucidating the genetic and biochemical pathways that contribute to the observed phenotypic variations. First, spatiotemporal dynamics of total flavonoid and anthocyanin accumulation in the S. vulgaris across genotypes and developmental stages were tightly coupled to floral pigmentation and radical scavenging capacities. Second, six flavonoid and anthocyanin metabolites were profiled in floral tissues, and cyanidin-4-rutinoside served as the primary determinant of inter-cultivar variation in total anthocyanin accumulation, directly shaping both petal chromaticity and radical scavenging efficacy. Third, RNA sequencing identified 95 and 317 key genes affecting flavonoid metabolism and antioxidant mechanisms, and 11 DEGs were crucial for flavonoids. Finally, multi-omics analysis showed that a core set of genes (Sv4CL-4, SvHCT, SvHCT-4, SvCHS-1, SvCHI-1, SvUGT75C1-1, and SvC12RT1) modulated flavonoid metabolism, impacting flower color and antioxidant mechanisms. The SvHCT-4 orchestrated floral pigmentation in S. vulgaris by selectively enhancing the biosynthesis of cyanidin-4-rutinoside and delphinidin-3-rutinoside. These flavonoids and anthocyanidins activated the antioxidant metabolism, and led to the DPPH· and OH· scavenging capabilities varying significantly in the S. vulgaris. This study constituted a substantial advancement in our pursuit of comprehending and regulating the metabolic flux partitioning related to flower color and antioxidant activity in S. vulgaris. It provided a reliable framework for subsequent investigations focused on improving the ornamental and health-promoting characteristics of this well-favored plant species.

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