Abstract
Phenylpropanoids and flavonoids are major classes of bioactive metabolites that contribute to plant defense and functional value in noni (Morinda citrifolia). Although feruloyl-CoA 6′-hydroxylase 1 (F6′H1) is recognized as a key enzyme in coumarin biosynthesis, its broader role in secondary metabolic regulation and antifungal responses in perennial medicinal plants remains unclear. In this study, we investigated the regulatory function of McF6'H1 by transgenic analysis, HPLC-MS/MS-based metabolomics, and extract-based antifungal assays in noni. Based on an established genetic transformation system, stable McF6'H1 overexpression (OE) and RNA interference (RNAi) lines were obtained and subsequently subjected to HPLC–MS/MS based metabolomic profiling and antifungal evaluation against Phytophthora cactorum, powdery mildew, and Diaporthales. Metabolomic analysis showed that altered McF6'H1 expression was associated with pronounced changes in secondary metabolism, including phenylpropanoid, flavonoid, flavone, amino acid, and organic acid pathways. McF6'H1 overexpression was associated with increased accumulation of several phenylpropanoid and flavonoid related metabolites, including rutin, luteoloside, and other flavonol derivatives, and with stronger antifungal activity against a powdery mildew-associated fungal isolate and a Diaporthales fungal isolate. Interestingly, both OE and RNAi lines showed increased inhibition of P. cactorum, possibly because altered McF6'H1 expression triggered metabolic compensation or flux redistribution within the phenylpropanoid/coumarin network, leading to non-linear changes in antimicrobial metabolites. Overall, our results indicated that McF6'H1 was involved in phenylpropanoid/flavonoid metabolic reprogramming and antifungal responses in noni, providing a basis for further studies on metabolic regulation and disease resistance in medicinal plants.
Keywords: McF6'H1, Phenylpropanoids, Differentially accumulated metabolites, Metabolomics, Antifungal Activity
Introduction
Noni (Morinda citrifolia) is a functional plant with both ornamental and medicinal value and medicinal efficacy, which is widely cultivated in tropical and subtropical regions [1, 2]. It exhibits remarkable ecological adaptability with low specificity for soil types. Not only is it tolerant of barren conditions, but it can also establish roots in both acidic and alkaline soils, and even thrive in harsh environments such as sandy soils or rock crevices. As a strongly photophilic plant, it has high water demands, drought stress during the seedling stage and harvest period can significantly impair its growth and yield. From the perspective of medicinal components, noni contains abundant secondary metabolites, including phenylpropanoids, flavonoids, and iridoids [3–5]. Among these, phenylpropanoids and flavonoids have emerged as a focus of research in the field of natural medicine development due to their significant biological activities such as anti inflammatory and antioxidant properties [6–8]. They provide an important material basis and research direction for the exploration of new medicinal resources.
Phenylpropanoids and flavonoids exert anti inflammatory and antioxidant activities through the synergistic effects of multiple targets and pathways. In terms of anti inflammatory mechanisms, phenylpropanoids such as chlorogenic acid and caffeic acid can inhibit the activation of nuclear Factor-kappa B (NF-κB), block the cascade reaction of inflammatory signaling pathways, and reduce the release of pro-inflammatory factors such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), thereby alleviating chronic inflammatory responses [9–11]. For instance, caffeic acid can exert anti inflammatory effects in a noise induced sensorineural hearing loss model. Researchers induced hearing loss in Wistar rats through noise exposure and found that caffeic acid was able to protect auditory function and inhibit cell death in the middle/basal turns of the cochlea damaged by noise exposure [12]. The anti inflammatory effects of flavonoids such as quercetin and kaempferol involve inhibiting the activities of cyclooxygenase (COX) and lipoxygenase (LOX), reducing the synthesis of inflammatory mediators like prostaglandin E2 (PGE2), while enhancing immune homeostasis by regulating the balance of T cell subsets [13]. Quercetin primarily exerts its effects by inhibiting the activation of NF-κB, suppressing the activation of microglia and astrocytes, reducing the release of proinflammatory factors such as TNF-α, IL-6, and IL-1β, and simultaneously downregulating the expression of inflammation related enzymes including cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS) [14]. Phenylpropanoids and flavonoids can activate antioxidant signaling pathways, promote the expression of endogenous antioxidant enzymes such as superoxide dismutase (SOD) and glutathione peroxidase (GSH-Px), and enhance the cellular defense system against oxidative stress, ultimately alleviating lipid peroxidation and protein oxidative damage [15]. These properties make phenylpropanoids and flavonoids important material bases for anti inflammatory and antioxidant activities in natural products.
Coumarins are among the core products of the downstream branches in the phenylpropanoid metabolic pathway, directly connecting the conversion node from primary metabolism to secondary metabolism. Scopoletin (7-hydroxy-6-methoxycoumarin) was a coumarin derivative, and its biosynthesis occurs within the phenylpropanoid metabolic pathway [16]. Genes affecting scopoletin synthesis are mainly involved in the phenylpropanoid metabolic pathway and specific coumarin synthesis steps, including PAL (phenylalanine ammonia-lyase), C4H (cinnamate-4-hydroxylase), 4CL (4-coumarate coenzyme A ligase), CYP82C2 (cytochrome P450 enzyme), and F6'H1 (feruloyl-CoA 6′-hydroxylase 1) [17]. McF6'H1, as the rate limiting enzyme in the final step of scopoletin biosynthesis, executed a critical ortho-hydroxylation step and catalyzes the formation of the 6’-O-hydroxylated trans-isomer of feruloyl-CoA. This intermediate undergoes enolate anion isomerization and lactonization to form scopoletin [18, 19]. As upstream universal genes in the phenylpropanoid metabolism, PAL, C4H, and 4CL are responsible for converting phenylalanine into precursors such as coumaric acid, providing raw materials for various phenylpropanoid compounds (e.g., coumarins, lignins, flavonoids) and thus exhibiting broad spectrum functions. In contrast, F6'H1 shows stronger substrate specificity and metabolic pathway directionality.
It mainly targets flavonoids (e.g., apigenin) and, by catalyzing the 6-hydroxylation reaction, not only directly participates in the structural modification of flavonoids but also diverts part of the intermediate products to the scopoletin synthesis pathway through metabolic flux shunting [20, 21]. Studies on Convolvulus prostratus have shown that the expression of F6’H in the stems 90 d post-sowing is positively correlated with scopoletin accumulation [22]. Within the phenylpropanoid metabolic pathway, most compounds exhibit potent antibacterial activity by disrupting cell membranes and inhibiting cellular energy metabolism, ultimately leading to bacterial cell death [23–25]. In this study, McF6'H1, an enzymatic gene involved in the scopoletin biosynthetic pathway, was likely to play an important role in the accumulation of scopoletin in noni.
While the role of F6'H1 in coumarin biosynthesis is well recognized, current studies have largely focused on individual metabolites or single biosynthetic branches. This narrow focus has left the broader impact of F6'H1 on secondary metabolic network reprogramming and antifungal defense insufficiently understood particularly in perennial medicinal plants. Moreover, it remains unclear whether the manipulation of F6'H1 expression leads to linear metabolic responses or involves compensatory and alternative regulatory mechanisms. Addressing these gaps is required for elucidating the complexity of phenylpropanoid and flavonoid mediated plant defense. In this study, we systematically investigated the function of McF6'H1 in noni by integrating transgenic overexpression and RNA interference with transgenic analysis, HPLC-MS/MS-based metabolomics, and extract-based antifungal assays. This integrative approach enabled us to delineate the global reprogramming of secondary metabolism triggered by altered McF6'H1 expression, to link these metabolic changes with antifungal responses against multiple phytopathogens, and to further explore the transcriptional regulatory framework underlying these processes. By providing functional-metabolomic evidence that McF6'H1 mediates non-linear regulation of secondary metabolism and antifungal defense, this work advances current understanding of phenylpropanoid pathway regulation and offers new perspectives for metabolic engineering and disease-resistant breeding in medicinal plants.
Materials and methods
Plant materials and sampling
The McF6'H1 transgenic noni lines used in this study were generated and preliminarily characterized in a related manuscript currently under review. Briefly, the overexpression lines were obtained using the pCAMBIA1300-35 S-McF6'H1 vector, whereas the RNA interference lines were generated using the pCAMBIA1300-35 S-McF6'H1-RNAi vector. Both constructs contained the hygromycin phosphotransferase gene (Hpt), which was used as a selectable marker for hygromycin resistance.
In the present study, these transgenic materials were further screened and analyzed to investigate the effects of altered McF6'H1 expression on scopoletin accumulation, secondary metabolic reprogramming, and extract-based antifungal activity. Twenty-five putative McF6'H1-overexpression lines and twenty-five putative McF6'H1-RNA interference lines were obtained from independent hygromycin-resistant regeneration events and were considered independent T0 transgenic lines. Leaf samples were collected from these independent T0 lines for PCR confirmation using primers specific to the hygromycin phosphotransferase gene (Hpt-F/R; Table 1). PCR amplification was performed under the following conditions: 35 cycles of denaturation at 94 °C for 30 s, annealing at 56 °C for 30 s, and extension at 72 °C for 1 min, followed by a final extension at 72 °C for 10 min. The confirmed positive T0 transgenic lines were further analyzed for McF6'H1 expression levels by quantitative real-time PCR (qRT-PCR).
Table 1.
The qRT-PCR and PCR primers
| Primer name | Sequence (5’–3’) |
|---|---|
| Hygromycin phosphotransferase gene F (Hpt-F) | CTCGGTACCATGAAAACCCTTGTT |
| Hygromycin phosphotransferase gene R (Hpt-R) | AGAGGATCCTCAATTGATCTTGAG |
| Actin-F | TGTATGGCAACATCGTTCTCAGT |
| Actin-R | CCACCTTAATCTTCATGCTGCT |
| McF6'H1-OE-qF | AGGTCATGGAGTGAAGGGTCT |
| McF6'H1-OE-qR | GGATCAGCCCAGTTCGACAA |
| McF6'H1-RNAi-qF | GGGAGAAGCGAATATACAAGCA |
| McF6'H1-RNAi-qR | TATCTTGGCGAAATCCACTGTC |
Based on McF6'H1 expression levels and scopoletin content, representative overexpression and RNA interference lines were selected for HPLC-MS/MS-based metabolomic profiling and extract-based antifungal assays. These analyses allowed us to evaluate the metabolic consequences of McF6'H1 overexpression and interference at the metabolite level and to further examine their association with the inhibitory activity of leaf extracts against phytopathogens.
RNA extraction, cDNA synthesis and qRT-PCR analysis of transgenic lines
Total RNA was extracted from overexpression and interference expression transgenic lines using the PaPure Plant RNA Kit (Vazyme). First-strand cDNA was synthesized using Hifair® AdvanceFast 1st Strand cDNA Synthesis Kit for qRT-PCR. The cDNA was used for qRT-PCR. qRT-PCR was performed on a Roche LightCycler PCR instrument as follows: pre-denaturation at 94 °C for 30 s, followed by 40 cycles of denaturation at 94 °C for 10 s, annealing at 60 °C for 30 s, and extension at 55 °C for 60 s. It was conducted in a 20 µL volume with 10 µL of Blas Taq 2X qPCR MasterMix, 2 µL of cDNA, 6.8 µL of ddH2O, and 0.6 µL of each primer, and specific primer sequence of McF6'H1 qF/qR is shown in Table 1. Template-free control was established before the examination. The results were represented by three biological replicates (each with three technical replicates) for each sample, and the 2−ΔΔCt method was used to analyze the expression data.
Scopoletin detection in McF6'H1 transgenic lines
Leaf samples with similar positions and comparable maturity levels were collected from McF6'H1 transgenic noni lines for scopoletin detection. Three biological replicates were included in the experimental design. Leaf samples were initially fixed by heating in an oven at 100 °C for 30 min, followed by drying at 65 °C until a constant weight was achieved. Subsequently, 0.5 g of each dried sample was ground into a fine powder, transferred into a 50 mL centrifuge tube, and thoroughly mixed with 25 mL of 60% ethanol. The mixtures were subjected to ultrasonic extraction using an ultrasonic cleaner at 320 W for 40 min. After extraction, the samples were centrifuged at 12,000 rpm for 15 min at 4 °C. The supernatants were collected and filtered three times through 0.22 μm organic membrane filters. The filtrates were then transferred into 1.5 mL HPLC vials for analysis using a high-performance liquid chromatography (HPLC) system (Agilent 1260) equipped with an Agilent ZORBAX Eclipse XDB C18 column (250 × 4.6 mm, 5 μm). The mobile phase used for HPLC analysis consisted of acetonitrile and water at a ratio of 20∶80 (v/v). This HPLC method was used for targeted scopoletin detection, whereas the HPLC-MS/MS-based metabolomics analysis used a different mobile phase system for broad metabolite profiling.
A standard stock solution of scopoletin was prepared by dissolving the compound in 60% ethanol to a concentration of mg/mL and stored in the dark at 4 °C until use. A series of standard working solutions with concentrations of 10, 20, 40, 80, and 100 µg/mL were prepared by gradient dilution of the stock solution. These were filtered three times through 0.22 μm organic membrane filters, transferred into 1.5 mL vials, and analyzed by HPLC to generate a standard calibration curve.
HPLC-MS/MS-based metabolomics analysis
Sequenced lines were screened based on McF6'H1 expression and scopoletin content, with further metabolomic analysis performed subsequently. The extensively targeted metabolites were determined by Shanghai Biotree Biomedical Technology Co., Ltd. HPLC-MS/MS was used to analyze the broad-target metabolites. Plant samples (20 mg ± 1 mg) were collected, lyophilized, and mixed with beads and 1000 µL of extraction solution (MeOH∶ACN∶H2O, 2∶2∶1, v/v). The extraction solution contained deuterated internal standards. The mixed solution was vortexed for 30 s. The samples were then homogenized at 35 Hz for 4 min and sonicated for 5 min in a 4 °C water bath. This process was repeated three times. The samples were incubated at -40 °C for 1 h to precipitate proteins. Afterward, the samples were centrifuged at 12,000 rpm (RCF = 13,800 ×g, R = 8.6 cm) for 15 min at 4 °C. The supernatant was transferred to a fresh glass vial for analysis. A quality control (QC) sample was prepared by mixing equal aliquots of the supernatant from the samples. LC-MS/MS analysis was performed using a HPLC system (Thermo Fisher Scientific, Vanquish) coupled to a Phenomenex Kinetex C18 column (2.1 mm × 50 mm, 2.6 μm) and an Orbitrap Exploris 120 mass spectrometer (Thermo). The mobile phase A consisted of 0.01% acetic acid in water, and mobile phase B was a mixture of isopropyl alcohol and acetonitrile (IPA∶ACN, 1∶1, v/v). The auto-sampler temperature was set to 4 °C, with an injection volume of 2 µL. The Orbitrap Exploris 120 mass spectrometer was used to acquire MS/MS spectra in information-dependent acquisition (IDA) mode, controlled by acquisition software (Xcalibur, Thermo). The software continuously evaluated the full-scan MS spectrum in this mode. The ESI source conditions were as follows: sheath gas flow rate at 50 Arb, aux gas flow rate at 15 Arb, capillary temperature at 320 °C, full MS resolution at 60,000, MS/MS resolution at 15,000, collision energy: SNCE 20/30/40, and spray voltage at 3.8 kV (positive mode) or -3.4 kV (negative mode).
Library construction and data analysis
Multivariable statistical analyses, including principal component analysis (PCA), hierarchical clustering analysis (HCA), and orthogonal partial least square-discriminant analysis (OPLS-DA), were applied to process the metabolic data. Significantly changed metabolites in different pairwise comparisons were screened by variable importance in the project (VIP). Those with a VIP value ≥ 1, P < 0.05, and a fold change ≥ 2 or ≤ 0.5 were considered differential metabolites.
Preparation of noni extract
After analyzing the data, the leaves of the transgenic lines McF6'H1-OE#26, McF6'H1-RNAi#5 and wild-type lines were selected and placed in an oven at 105 °C for 30 min to deactivate enzymes, followed by drying at 60 °C in a constant temperature oven. The dried leaves were ground into a fine powder and subjected to ultrasonic treatment for 60 min using methanol as the solvent. The treated solution was then filtered into a distillation flask and concentrated using a rotary evaporator (50 °C, 70 rpm) to obtain the noni extract required for the antimicrobial test.
Analysis of antifungal activity
To evaluate the antifungal activity of extracts from McF6'H1 transgenic noni lines, a surface-contact in vitro extract-based assay was conducted. Leaf tissues were oven-dried, ground into fine powder, and extracted with methanol at a ratio of 2 g dry weight in 20 mL methanol. The mixture was subjected to ultrasonic extraction for 60 min, followed by concentration using a rotary evaporator. The resulting residue was re-dissolved in methanol to a final concentration of 100 mg/mL.
For each PDA plate, 500 µL of the methanolic extract was evenly spread onto the surface of the solidified PDA medium and air-dried under sterile conditions to ensure complete evaporation of methanol before pathogen inoculation. This surface-application method was used because the assay was designed to evaluate the direct inhibitory effect of leaf methanolic extracts on fungal mycelial growth at the medium surface, rather than to distribute the extract homogeneously throughout the agar medium. In addition, this method avoided possible thermal degradation or precipitation of extract components that might occur if methanolic extracts were mixed with warm PDA before agar solidification.
A 5 mm diameter fungal plug was excised from the actively growing margin of each pathogen culture and placed at the center of each treatment plate. The tested phytopathogens included P. cactorum, a powdery mildew-associated fungal isolate, and a Diaporthales fungal isolate. The powdery mildew-associated fungal isolate was previously isolated from tobacco leaves showing powdery mildew symptoms and maintained in our laboratory, but it was not identified to the species level. The Diaporthales fungal isolate was also described at the order level because species-level identification within Diaporthales is difficult based only on morphological characteristics, owing to overlapping morphological features and limited molecular information for some taxa [26]. Therefore, this isolate was conservatively referred to as a Diaporthales fungal isolate in the present study.
Methanol-treated PDA plates that were air-dried under the same conditions were used as solvent controls. All plates were incubated at 25–28 °C for 144 h, depending on the growth rate of each pathogen. Colony diameters were measured using a digital caliper, and inhibition rates were calculated using the following formula:
Statistical analysis
Statistical analysis of the main chemicals was performed by SPSS software (version 26.0; Chicago, IL, USA) using one-way ANOVA. All treatments were performed in triplicate, and the results were presented as mean ± standard deviation (SD). Values in the same row that were labeled with different letters represent a significant difference (P < 0.05); annotation and enrichment analysis of SCMs using the KEGG database; All data were plotted in GraphPad Prism v10.0.2.
Results
Expression of McF6'H1 and contents of noni scopoletin in different transgenic lines
The 25 overexpression lines detected by hpt-specific primers were subjected to qRT-PCR analysis and screening, of which 10 overexpression lines had higher relative expression than the wild-type lines (Fig. 1A). Among them, OE #26, OE #34, OE #37, and OE #45 lines had expression levels higher than 2, which was a significant difference compared with the wild-type lines. The expression of the other lines was higher than that of the wild-type lines, but the difference was less significant. The expression analysis of 10 overexpression lines showed that the expression of the McF6'H1 was significantly increased by constructing the McF6’H1 overexpression vector and genetic transformation. The 25 RNA interference lines detected by hpt-specific primers were subjected to qRT-PCR analysis and screening, and 10 of them had lower relative expression than the wild-type lines (Fig. 1B). Among them, the expression of RNAi #11, RNAi #12, RNAi #21, and RNAi #31 lines was higher than 0.1 and less significant than that of wild-type lines. In comparison, the expression of RNAi #4, RNAi #5, RNAi #23, and RNAi #39 lines was lower than 0.1 and more significant than that of wild-type lines. The expression analysis results of these 10 interference lines indicated that the expression of the McF6'H1 was significantly reduced by constructing the McF6'H1 interference expression vector and genetic transformation.
Fig. 1.
The expression of McF6'H1 gene and scopoletin content in transgenic lines (A) Expression analysis of McF6'H1 gene in overexpressed lines. B Expression analysis of McF6'H1 gene in RNA interference lines. C Analysis of the content of scopoletin in the noni lines with overexpression of the McF6'H1 gene. D Analysis of the content of scopoletin in the noni lines with interference of the McF6'H1 gene. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 (Student’s t test)
Based on the qRT-PCR results, four overexpression lines (OE #26, OE #34, OE #37, OE #45) with higher gene expression than the wild-type lines, which were used to determine the content of scopoletin. Combined with the qRT-PCR results for analysis (Fig. 1C), the changes in the scopoletin content of the four overexpression-positive lines were consistent with the trend of changes in their expression of McF6'H1, which were all significantly higher than that of the wild-type lines, indicating that overexpression of McF6'H1 was associated with increased scopoletin content in noni leaves. Four RNA interference lines (RNAi #4, RNAi #5, RNAi #23, and RNAi #39) with lower gene expression of McF6'H1 than the wild-type lines were selected after qRT-PCR assay and then assayed for scopoletin content. Combined with the qRT-PCR results for analysis (Fig. 1D), changes in scopoletin content in individuals of the four interference expression-positive lines were not consistent with changes in their expression of McF6'H1, but the overall trend was the same, with all of them being significantly lower than that of the wild-type lines, indicating that interfering with the expression of McF6'H1 could reduce the content of scopoletin in the leaves of noni.
Effects of different transgenic lines on the metabolite profile of noni by HPLC-MS/MS
The noni leaf samples from the McF6'H1 overexpression transgenic lines, McF6'H1 interference transgenic lines, and wild-type lines were analyzed using an HPLC-MS/MS system, which was used to determine the metabolites of the samples in the positive and negative ion mode, respectively. The quality control information was good, indicating that the stability of the instrument data acquisition was excellent. The results showed that 2,763 metabolites were detected (Fig. 2A). The metabolites were analyzed and classified, including 386 lipids, 308 organoheterocyclic compounds, 202 benzenoids, 183 shikimates and phenylpropanoids, 157 organic acids, and derivatives, 144 phenylpropanoids and polyketides, 95 fatty acids, 89 terpenoids, etc. accounting for 13.97%, 11.15%, 7.31%, 6.62%, 5.68%, 5.21%, 3.44%, 3.22%, etc., respectively.
Fig. 2.
The classification and proportion, Venn Diagram and Score scatter plot of PCA model for the metabolites of transgenic lines of McF6'H1 and wild-type lines in noni (A) This pie chart depicts the proportion of different types of metabolites among the metabolites detected by HPLC-MS/MS. B Each circle represents a comparison group, and the numbers in the overlapping part represent the different metabolites shared between the comparison groups; Numbers that do not overlap represent the number of different metabolites unique to the comparison group. C The abscissa PC1 and ordinate PC2 represent the scores of the first and second principal components, respectively. Each scatter point represents a sample, and the color and shape of the scatter point represent different groups. The closer the distribution of sample points, the more similar the type and content of metabolites in the sample are. On the contrary, the farther away the sample, the greater the difference in overall metabolic level. All samples are within the 95% confidence interval (Hotelling’s T-squared ellipse). OE meant that the sample was from the McF6'H1-overexpression lines; RNAi meant that the sample was from the McF6'H1-interference lines; WT meant that the sample was from the wild-type lines. The same as below
As shown in the Venn diagram (Fig. 2B), a total of 759 metabolites overlap among the three groups, thereby indicating that these metabolites were shared among the three groups; individually, the sample from the McF6'H1 overexpression lines had 121 unique metabolites, the sample from the McF6'H1 interference lines had 151 unique metabolites, and the wild-type lines had 36 unique metabolites, suggesting that both overexpression and interference of the McF6'H1 could lead to drastic changes in metabolites.
Unsupervised pattern recognition PCA was used to compare whether the secondary metabolites of noni were affected under the overexpression or interference of the McF6'H1 gene from the profiles (Fig. 2C). The points representing the samples from the McF6'H1 overexpression lines, McF6'H1 interference lines, and wild-type lines were significantly distinguished in the PC1 and PC2 directions and did not intersect. The contribution of PC1 was 41.1%, and that of PC2 was 24.7%. These results suggested that the transgenics of the McF6'H1 significantly affected the metabolite profile of noni.
Differences in metabolites of different transgenic lines of noni
The permutation histogram test of the OPLS-DA model for groups WT vs. OE and WT vs. RNAi showed R²Y values of 0.999 and 1, respectively, and Q² values of 0.959 and 0.967, respectively (Fig. 3A-B), which indicated that OPLS-DA analysis models were stable and valid, and the variable importance projection values (VIP values) generated by the models were also reliable. The points representing the samples from the different transgenic lines and wild-type lines were clearly distinguished. In order to screen the most representative variable metabolites, the criteria were set to satisfy the conditions of VIP value > 1, fold change > 2 or < 0.5, and P < 0.05. Compared with the samples from wild-type lines, the contents of 303 metabolites in the samples from McF6'H1 overexpression lines up-regulated significantly and 174 metabolites down-regulated significantly (Fig. 3C); the contents of 261 metabolites in the samples from McF6'H1 interference lines up-regulated significantly and 261 metabolites down-regulated significantly (Fig. 3D).
Fig. 3.
Permutation histogram test of OPLS-DA model and Volcano plot (A-B) A supervised OPLS-DA model was used to compare the data. The x-axis represents the permutations and the y-axis represents the frequency. Each point represents one sample: A compares the samples from WT vs. OE with parameters R2Y: 0.998 and Q2:0.96. B compares the samples from WT vs. RNAi with parameters R2Y:1 and Q2:0.975. C-D Volcano maps of differential metabolites in different pairwise comparisons: (C) WT vs. OE; (D) WT vs. RNAi
The screening criteria were further set to VIP value > 1.5, fold change > 4 or < 0.05, and P < 0.05 to show more clearly and effectively in the heat map. The 310 metabolites were altered significantly in the samples from different transgenic lines of noni in the heat map. In the samples from McF6'H1 overexpression lines, phenylpropanoids, flavonoids, lipids, terpenoids, and glucosides changed dramatically, among which, phenylpropanoids, flavonoids, glucosides were up-regulated dominantly, including 15 up-regulated glucosides and only one down-regulated glucoside (Fig. 4A). In the samples from McF6'H1 interference lines, flavonoids showed the most drastic changes, with 43, followed by terpenoids, with 36, 30 downregulated (Fig. 4B).
Fig. 4.
Heat map of significantly changed metabolites in McF6'H1 transgenic noni lines (A) WT vs. OE (B) WT vs. RNAi. On the left side of the graph, the color was used to show the classification of the metabolites. On the right side of the graph, the ID number of the metabolite was listed. N meant that the determination was in negative ion mode, and P meant that the determination was in positive ion mode. The red color represents an increase in metabolite content and the blue color represents a decrease in metabolite content
Enrichment analysis of different metabolites in KEGG metabolic pathways for different transgenic lines of noni
The enrichment and analysis of the KEGG pathway showed that the differential metabolites were involved in various metabolic pathways (Fig. 5A-B). To explore the metabolite information in different transgenic lines of noni, we used the KEGG database to annotate and enrich the differential metabolites. In the WT vs. OE and WT vs. RNAi groups, 21 and 20 DAMs were annotated by KEGG and showed significant differences, respectively. The major pathways are presented in bubble plots (Fig. 5C-D). In the WT vs. OE comparison group, the KEGG enrichment analysis revealed significant changes in most of the top 15 metabolic pathways, such as flavone and flavonol biosynthesis, aminoacyl-tRNA biosynthesis, phenylalanine, tyrosine and tryptophan biosynthesis, and cyanoamino acid metabolism. In the WT vs. RNAi comparison group, the DAMs were primarily enriched in flavone and flavonol biosynthesis, ABC transporters, galactose metabolism, nucleotide metabolism, flavonoid biosynthesis, and purine metabolism (p-value < 0.05). Most importantly, the flavone and flavonol biosynthesis pathway was identified as the most significantly enriched in both the WT vs. OE and WT vs. RNAi groups.
Fig. 5.
The effects of different transgenic lines of noni on the metabolic pathways (A-C) WT vs. OE. (B-D) WT vs. RNAi. Different KEGG pathways for metabolite enrichment from different transgenic lines of noni. The horizontal coordinate indicates the enrichment factor and the vertical coordinate indicates the name of the pathway. The color of the dots reflects the magnitude of the P-value; the redder the color, the more significant the degree of enrichment
Flavonoids play important roles in plant stress responses due to their antimicrobial and antioxidant properties. In the present study, altered McF6'H1 expression was associated with pronounced changes in flavonoid-related metabolite profiles. Specifically, metabolomic profiling showed that the relative abundance of several flavonoid-related metabolites, such as luteoloside and rutin, increased in the McF6'H1-overexpression lines. In contrast, several metabolites, including myricetin, chrysoeriol, and apiin, showed decreased relative abundance in the RNAi lines. These metabolite shifts suggest that McF6'H1 may be associated with flavonoid metabolic reprogramming, although the underlying transcriptional regulatory mechanisms remain to be further validated.
Altered McF6'H1 expression was also associated with changes in metabolites mapped to phenylalanine-, phenylpropanoid-, coumarin-, and flavonoid-related pathways (Fig. 6). Representative differentially accumulated metabolites supporting this metabolite-based pathway interpretation are summarized in (Table 2). These changes indicate that McF6'H1 perturbation may influence the redistribution of metabolites within the phenylpropanoid/coumarin/flavonoid network. However, the enzyme names shown in Fig. 6 indicate the corresponding biochemical steps in the KEGG pathway and do not represent experimentally validated gene-expression changes in the present study.
Fig. 6.
The impact on key metabolic pathways through the regulation of McF6'H1 expression XanB2, chorismate lyase; PAL, phenylalaninammo nialyase; 4CL, 4-coumarate-CoA ligase; CYP98A, 5-O-(4-coumaroyl)-D-quinate 3'-monooxygenase; CHS, chalcone synthase; OMT1, 5'-demethylyatein 5'-O-methyltransferase; ansA, Anthocyanidin synthase; FG2, flavonol-3-O-glucoside L-rhamnosyltransferase; FNSI, flavone synthase I; CYP93B2-16, flavone synthase II; CYP75B1, flavonoid 3'-monooxygenase; C12RT1, flavanone 7-O-glucoside 2''-O-beta-L-rhamnosyltransferase. The key differentially accumulated metabolites supporting this KEGG-based pathway overview are summarized in Table 2. Enzyme names indicate the corresponding biochemical steps and do not represent experimentally validated gene-expression changes in the present study
Table 2.
Key differentially accumulated metabolites associated with phenylpropanoid, coumarin, and flavonoid-related pathways
| Pathway category | Metabolite | Comparison | Fold change | P value |
|---|---|---|---|---|
| Phenylalanine and phenylpropanoid-related metabolites | Phenylalanine | WT vs. OE | 2.624 | 1.39E-03 |
| Ferulate | WT vs. RNAi | 2.638 | 4.43E-04 | |
| 4-Hydroxybenzoic acid | WT vs. OE | 5.181 | 9.88E-03 | |
| 4-O-p-Coumaroylquinic acid | WT vs. OE | 3.236 | 3.39E-04 | |
| WT vs. RNAi | 3.516 | 1.06E-05 | ||
| Methyl caffeate | WT vs. RNAi | 2.804 | 5.76E-04 | |
| Coumarin-related | Isoscopoletin | WT vs. OE | 0.206 | 2.79E-03 |
| WT vs. RNAi | 0.382 | 1.04E-03 | ||
| Fraxetin | WT vs. OE | 0.179 | 1.34E-03 | |
| WT vs. RNAi | 0.286 | 1.13E-02 | ||
| Skimmin | WT vs. OE | 3.341 | 3.10E-05 | |
| WT vs. RNAi | 6.343 | 2.33E-03 | ||
| Isofraxoside | WT vs. OE | 0.258 | 4.27E-03 | |
| WT vs. RNAi | 0.260 | 1.65E-02 | ||
| 7-Hydroxycoumarin | WT vs. OE | 7.127 | 1.05E-02 | |
| 7-Hydroxycoumarin-4-acetic acid | WT vs. OE | 4.384 | 5.30E-04 | |
| WT vs. RNAi | 2.673 | 1.43E-03 | ||
| Flavonoid / flavonol-related | Rutin | WT vs. OE | 2.906 | 1.26E-05 |
| Luteoloside | WT vs. OE | 2.310 | 5.16E-03 | |
| WT vs. RNAi | 2.170 | 2.52E-05 | ||
| Isoquercitrin | WT vs. OE | 2.728 | 3.05E-04 | |
| WT vs. RNAi | 26.793 | 2.93E-03 | ||
| Myricetin | WT vs.OEo | 0.304 | 1.48E-04 | |
| WT vs. RNAi | 0.023 | 1.39E-10 | ||
| Apiin | WT vs. OE | 0.150 | 2.21E-02 | |
| WT vs. RNAi | 0.102 | 2.12E-02 | ||
| Chrysoeriol | WT vs. RNAi | 0.106 | 9.64E-06 | |
| Kaempferol-3-O-glucoside | WT vs. OE | 26.029 | 4.36E-03 | |
| WT vs. RNAi | 114.389 | 4.98E-03 | ||
| Kaempferol 3-neohesperidoside | WT vs. OE | 2.040 | 6.78E-05 | |
| WT vs. RNAi | 2.125 | 1.69E-05 | ||
| Mauritianin | WT vs. OE | 2.936 | 1.90E-05 | |
| WT vs. RNAi | 3.418 | 2.70E-05 | ||
| 6-Hydroxyluteolin 7-glucoside | WT vs. OE | 4.348 | 2.80E-02 | |
| WT vs. RNAi | 6.630 | 1.89E-06 |
Metabolites listed in this table were selected from the differentially accumulated metabolites identified in the WT vs. OE and WT vs. RNAi comparisons using the criteria VIP ≥ 1, P < 0.05, and fold change ≥ 2 or ≤ 0.5. Fold change was calculated as WT/treatment. Values greater than 1 indicate increased abundance in OE or RNAi lines, whereas values less than 1 indicate decreased abundance. Metabolite names are based on MS2 annotation and should be interpreted as putative annotations. This table was used to support the KEGG-based pathway overview shown in Fig. 6
To further support the metabolite-based pathway interpretation shown in Fig. 6, representative differentially accumulated metabolites associated with phenylpropanoid, coumarin, and flavonoid-related pathways are summarized in (Table 2).
Effect of different transgenic lines on the antifungal activity of noni
The extracts from both overexpressing and interference expressing noni transgenic lines exhibited significant inhibitory effects on the colony growth of three pathogenic fungi (Fig. 7C). The strongest inhibitory effect was observed against P. cactorum, and leaf extracts from the RNA interference lines showed inhibitory activity comparable to that of the overexpression lines. Furthermore, the difference in inhibition rates gradually increased over time. At 48 h, 96 h, and 144 h, the differences in inhibition rates were 0.84%, 3.575%, and 16.5%, respectively, but the inhibition rates were significantly higher than those of the wild-type lines (Fig. 7A). The overexpression lines showed the most significant inhibitory effects on powdery mildew (Fig. 7B) and Diaporthales (Fig. 7C), with inhibition rates gradually increasing over time. At 144 h, the inhibition rates against these pathogenic fungi reached 100% and 79.75%, respectively.
Fig. 7.
The inhibitory effects and inhibition curves of extracts from noni transgenic lines with McF6'H1 overexpression and interference expression on three pathogenic fungi (A) Phytophthora cactorum; B powdery mildew-associated fungal isolate; C Diaporthales fungal isolate. Asterisks indicate significant differences compared with WT at the same time point. ns, not significant; ***P < 0.001; ****P < 0.0001
Association between candidate metabolites and extract-based antifungal activity
To further link the metabolomic results with the extract-based antifungal assay, candidate metabolites potentially associated with antifungal activity were summarized in (Table 3). These metabolites were selected from representative differentially accumulated metabolites associated with phenylpropanoid-, coumarin-, and flavonoid-related pathways and were compared with the inhibition patterns observed in (Fig. 7). Several metabolites, including rutin, luteoloside, isoquercitrin, kaempferol-3-O-glucoside, skimmin, 7-hydroxycoumarin, 7-hydroxycoumarin-4-acetic acid, and 4-O-p-coumaroylquinic acid, showed changes that were consistent with enhanced extract-based inhibitory activity in one or both transgenic groups. However, because purified compounds were not individually tested, these metabolites should be regarded as candidate antifungal-associated compounds rather than confirmed causal factors.
Table 3.
Candidate metabolites potentially associated with extract-based antifungal activity
| Candidate metabolite | Pathway category | Main change in transgenic lines | Associated antifungal phenotype | Possible interpretation |
|---|---|---|---|---|
| Rutin | Flavonoid / flavonol-related | Up in OE | OE extracts showed stronger inhibition against the powdery mildew-associated fungal isolate and Diaporthales fungal isolate | Candidate flavonoid associated with enhanced inhibitory activity of OE leaf extracts |
| Luteoloside | Up in OE and RNAi | Enhanced extract-based inhibition was observed in transgenic lines | Candidate flavonoid-related compound potentially contributing to antifungal activity | |
| Isoquercitrin | Up in OE and RNAi | Both OE and RNAi extracts showed increased inhibition, especially against Phytophthora cactorum | Candidate flavonol glycoside associated with the non-linear antifungal response | |
| Kaempferol-3-O-glucoside | Up in OE and RNAi | Increased abundance was consistent with stronger inhibition in transgenic extracts | Candidate flavonoid glycoside potentially contributing to crude extract activity | |
| 6-Hydroxyluteolin 7-glucoside | Up in OE and RNAi | Increased abundance was consistent with enhanced extract-based antifungal activity | Candidate luteolin derivative associated with inhibitory activity | |
| Myricetin | Down in OE and RNAi | Decreased abundance despite increased inhibition in some assays | Suggests that enhanced extract-based inhibition may involve alternative metabolites or metabolite ratios | |
| Skimmin | Coumarin-related | Up in OE and RNAi | Both OE and RNAi extracts showed increased inhibition against Phytophthora cactorum | Candidate coumarin-related compound potentially associated with the shared inhibition pattern |
| 7-Hydroxycoumarin | Up in OE | OE extracts showed strong inhibition against the tested phytopathogens | Candidate coumarin compound associated with enhanced OE extract activity | |
| 7-Hydroxycoumarin-4-acetic acid | Up in OE and RNAi | Both OE and RNAi extracts showed increased inhibition against Phytophthora cactorum | Candidate coumarin-related compound potentially contributing to the non-linear inhibition pattern | |
| 4-O-p-Coumaroylquinic acid | Phenylpropanoid-related | Up in OE and RNAi | Increased abundance was consistent with enhanced inhibition in both transgenic groups | Candidate phenylpropanoid-related compound associated with extract-based antifungal activity |
| Methyl caffeate | Up in RNAi | RNAi extracts showed increased inhibition against Phytophthora cactorum | Candidate phenylpropanoid derivative that may contribute to compensatory antifungal activity in RNAi extracts | |
| Ferulate | Up in RNAi | RNAi extracts maintained strong inhibition against Phytophthora cactorum | Candidate upstream phenylpropanoid metabolite associated with metabolic compensation | |
| Apiin | Flavonoid-related | Down in OE and RNAi | Decreased abundance despite increased inhibition in some assays | Indicates that antifungal activity may not depend on a single flavonoid but on combined metabolite composition |
| Chrysoeriol | Down in RNAi | RNAi extracts still showed increased inhibition against Phytophthora cactorum | Supports the possibility of compensatory metabolic adjustment rather than a simple single-metabolite effect |
Candidate metabolites were selected from representative differentially accumulated metabolites associated with phenylpropanoid, coumarin, and flavonoid-related pathways. The associations shown here are based on consistency between metabolite changes and extract-based antifungal phenotypes. Because purified compounds were not individually tested, these metabolites should be regarded as candidate antifungal-associated compounds rather than confirmed causal factors
Discussion
The effect of McF6'H1 on scopoletin accumulation in noni
Noni is a medicinal and edible plant, with scopoletin being an important bioactive secondary metabolite synthesized within it, exhibiting various pharmacological activities [27]. As a coumarin derivative, scopoletin biosynthesis is directly linked to McF6'H1, which acts as the rate-limiting enzyme in the final step of its synthesis [28]. The deletion of the F6'H1 significantly reduces scopoletin accumulation in Arabidopsis roots. In the coumarin biosynthetic pathway, F6'H1 has successfully restored reduced coumarin production in T-DNA-inserted soybean mutants [29, 30]. In noni, overexpression of McF6'H1 resulted in higher transcript levels in the transgenic lines than in the wild-type lines and was accompanied by a significant increase in scopoletin content, which is consistent with the role of McF6'H1 in the phenylpropanoid metabolic pathway. Conversely, interference with McF6'H1 expression decreased scopoletin levels, although some inconsistencies were observed between gene expression and metabolite accumulation. This discrepancy may stem from compensatory mechanisms within the phenylpropanoid pathway or post-transcriptional regulation. Additionally, phenylalanine and its derivatives were upregulated, whereas L-Asparagine, a nitrogen storage compound, was significantly downregulated. This suggests that transgenic lines prioritized synthesizing secondary metabolites over primary nitrogen metabolism, indicating a feedback loop to maintain metabolic flux, a common strategy in plants under stress conditions [31]. Overall, the overexpression and interference of McF6'H1 in noni significantly affected scopoletin accumulation. The activation of phenylpropanoid metabolism provided sufficient precursors for scopoletin biosynthesis [17], highlighting the important role of McF6'H1 in the phenylpropanoid biosynthetic pathway while also revealing the feedback regulatory mechanisms balancing secondary and primary metabolism in plants.
McF6'H1 enhances the antifungal activity of noni by regulating phenylpropanoid and flavonoid metabolic pathways
The regulatory role of McF6'H1 extends beyond a single metabolic branch and may influence the overall balance of the secondary metabolic network through downstream metabolic effects. Downstream metabolites in the phenylpropanoid pathway, including flavonoids and lignin-related compounds, are widely shared among multiple biosynthetic branches [32]. Although McF6'H1 directly drives scopoletin biosynthesis, its interaction with the flavonoid biosynthetic pathway is associated with pronounced differences in metabolite composition between transgenic and wild-type lines.
The metabolomic profiles observed in the present study suggest that the effects of altered McF6'H1 expression were not restricted to a single coumarin-related branch but extend to broader changes in phenylpropanoid/flavonoid metabolic profiles. In the overexpression lines, metabolites such as luteoloside, rutin, and scolymoside showed relatively higher abundance, whereas several related metabolites, including myricetin, chrysoeriol, and apiin, showed relatively lower abundance in the RNAi lines. These observations indicate that altered McF6'H1 expression is associated with broader flavonoid metabolic reprogramming rather than changes in only one downstream product. It should be noted that scopoletin was independently quantified in this study, whereas the reported changes in luteoloside, rutin, scolymoside, and other flavonoid-related metabolites were derived from HPLC–MS/MS-based metabolomic profiling and thus represent relative abundance differences rather than targeted absolute quantification.
The changes in phenylalanine metabolism shown in Fig. 6 further support this interpretation. The increased relative abundance of L-phenylalanine in both OE and RNAi lines suggests that manipulating McF6'H1 expression affects upstream precursor allocation and metabolic demand, rather than merely altering one terminal pathway. In contrast, the lower relative abundance of L-asparagine in the transgenic lines may indicate a shift in nitrogen allocation associated with altered secondary metabolic activity. Therefore, the metabolic consequences of perturbed McF6'H1 expression extend beyond scopoletin accumulation and involve the redistribution of metabolic flux between primary precursor metabolism and downstream phenylpropanoid/flavonoid pathways.
This coordination is particularly meaningful when comparing OE and RNAi lines, as it reveals that McF6'H1 expression affects not only downstream secondary metabolites but also the upstream metabolic context that supplies precursors and resources for their biosynthesis. Primary metabolism provides the carbon and nitrogen skeletons required for the production of phenylpropanoids, flavonoids, and coumarins, and changes in precursor-related metabolites may therefore reflect reallocation of metabolic resources rather than simple activation or suppression of a single branch pathway. The fact that both OE and RNAi lines showed elevated relative abundance of L-phenylalanine, while differing in the accumulation patterns of downstream flavonoid-related metabolites and antifungal phenotypes, indicates that McF6'H1-mediated regulation does not follow a simple opposite-response pattern. Instead, OE and RNAi lines may each trigger distinct metabolic adjustment programs, leading to different balances between precursor supply, downstream pathway flux, and defense-related metabolite output. From this perspective, the OE and RNAi materials used in this study provide complementary evidence that McF6'H1 is associated with broader coordination between primary metabolism and secondary metabolism, rather than acting only as a local regulator of one terminal product.
As a core precursor of phenylpropanoid metabolism, L-phenylalanine has been widely reported to promote flavonoid accumulation and plant defense responses [33, 34]. In the McF6'H1 overexpression lines, metabolomic profiling suggested increased relative accumulation of several flavonoid-related metabolites, which may be partially associated with altered phenylalanine metabolism and enhanced flux through phenylpropanoid-related pathways. Several flavonoids, including luteoloside, rutin, and isoquercitrin, are key components of the flavonoid biosynthetic pathway [35] and have been widely reported to exhibit antibacterial, anti-inflammatory, and stress-resistance activities [36–38]. Previous studies have shown that such compounds can exert antifungal effects by disrupting pathogen cell membranes, inhibiting hyphal growth, or interfering with pathogen metabolism [39–41].
Accordingly, flavonoid content in noni has been reported to be positively correlated with antifungal activity both in vitro and in vivo [42]. In the present study, metabolomic data indicated higher relative abundance of flavonoid-related metabolites such as luteoloside and rutin in the McF6'H1 overexpression lines, whereas metabolites such as myricetin and apiin showed lower relative abundance in the RNAi lines. The strong inhibitory effects of the overexpression lines against a powdery mildew-associated fungal isolate and a Diaporthales fungal isolate may therefore be associated with the coordinated accumulation of multiple flavonoid-related metabolites. It should be emphasized that greenhouse-based in vivo inoculation assays were not conducted in the present study, and the lines were not artificially inoculated with pathogen spores or mycelial suspensions before leaf collection. The leaves used for extract preparation were collected from normally grown plants without pathogen stress treatment. Therefore, the antifungal assay performed here evaluated the inherent in vitro inhibitory activity of basal metabolites present in leaf extracts against fungal mycelial growth, rather than whole-plant disease resistance induced by pathogen infection. The antifungal potential of natural leaf metabolites and the pathogen-induced defense mechanisms of intact plants represent two different research levels. Further greenhouse inoculation or in vivo infection assays will be required to determine whether McF6'H1-mediated metabolic changes contribute to enhanced disease resistance at the whole-plant level.
However, RNAi lines also exhibited enhanced inhibition of P. cactorum despite an overall reduction in many flavonoid compounds, indicating that antifungal activity cannot be solely attributed to the abundance of a limited set of measured metabolites. The increased inhibition of P. cactorum observed in both OE and RNAi lines suggests that the antifungal activity of leaf extracts was not controlled by a simple linear relationship between McF6'H1 expression level and one specific metabolite. In the OE lines, enhanced inhibition may be mainly associated with the increased accumulation of scopoletin and several flavonoid-related metabolites, such as rutin, luteoloside, and other flavonol derivatives. These compounds may jointly contribute to the stronger inhibitory activity of leaf extracts by affecting fungal mycelial growth or membrane stability. In contrast, the increased inhibition of P. cactorum in RNAi lines may reflect a compensatory metabolic adjustment rather than the same mechanism as that in OE lines. When McF6'H1 expression is suppressed, the normal metabolic flux toward McF6'H1-dependent products may be disturbed, which could redirect intermediates within the phenylpropanoid/coumarin network and alter the relative proportions of antimicrobial metabolites. Therefore, although several flavonoid-related metabolites decreased in RNAi lines, other coumarin-related or phenylpropanoid-derived compounds, or changes in metabolite ratios, may partially compensate for this reduction and maintain inhibitory activity against P. cactorum.
This interpretation is also consistent with related transcriptomic evidence from McF6'H1 transgenic noni lines, which suggested that altered McF6'H1 expression was associated with changes in phenylpropanoid biosynthesis, flavonoid biosynthesis, and plant hormone signal transduction pathways. In particular, hormone-related pathways, including jasmonic acid-related signaling and other hormone signaling branches, may participate in broader metabolic adjustment under McF6'H1 perturbation. However, pathogen-induced transcriptomic analysis and targeted validation of acquired or induced systemic response marker genes were not performed in the present study. Therefore, these pathways should be interpreted as possible explanations rather than confirmed mechanisms. Further transcriptomic and qRT-PCR analyses under pathogen-inoculated conditions will be required to determine whether systemic defense-related genes, such as NPR1, PR1, PR2, PR5, LOX, AOS, JAZ, and ERF, participate in McF6'H1-mediated antifungal responses in noni. Together, these observations highlight the complexity of McF6'H1-associated metabolic regulation and underscore the importance of considering network-level responses rather than simple linear gene–metabolite relationships.
Finally, some phenotypic variation among independent transgenic lines is expected in stable transformation systems, due to random transgene insertion and potential position effects. Thus, individual OE or RNAi lines may differ slightly in expression level, metabolite accumulation, or extract-based antifungal activity. However, the overall trends in McF6'H1 expression, scopoletin-related responses, and metabolomic shifts were consistent within each transgenic group, supporting that the major trends observed are associated with altered McF6'H1 expression rather than being solely attributable to random insertion effects.
Conclusion
In this study, we systematically elucidated the regulatory role of McF6'H1 in shaping secondary metabolic reprogramming and antifungal responses in noni (Morinda citrifolia) through an integrated analysis combining transgenic analysis, HPLC-MS/MS-based metabolomics, and extract-based antifungal assays. Modulation of McF6'H1 expression resulted in pronounced alterations in phenylpropanoid- and flavonoid-associated metabolic pathways, highlighting the central position of this gene within the secondary metabolic network. Overexpression of McF6'H1 promoted the accumulation of multiple flavonoids and was closely associated with enhanced resistance to a powdery mildew-associated fungal isolate and a Diaporthales fungal isolate, supporting a positive contribution of phenylpropanoid-derived metabolites to antifungal defense. Importantly, both overexpression and RNA interference lines exhibited increased inhibition of Phytophthora cactorum, indicating that McF6'H1-mediated defense regulation does not follow a simple linear relationship between individual metabolite abundance and antifungal activity. Instead, these observations point to the involvement of non-linear and potentially compensatory metabolic and transcriptional regulatory mechanisms. Together, our findings underscore the importance of considering network-level regulation when evaluating the functional consequences of manipulating key biosynthetic genes. By providing functional-metabolomic evidence for the complex role of McF6'H1 in coordinating secondary metabolism and antifungal defense, this work advances current understanding of phenylpropanoid pathway regulation and offers a valuable conceptual framework for future studies on metabolic engineering and disease resistance in medicinal plants (Fig. 8).
Fig. 8.
Mechanistic diagram of McF6'H1 regulating specific secondary metabolism and antifungal defense in noni
Acknowledgements
The authors thank all colleagues and institutions for technical support and assistance during this study.
Abbreviations
- F6'H1
feruloyl-CoA 6′-hydroxylase 1
- OE
overexpression
- RNAi
RNA interference
- DAMs
differentially accumulated metabolites
- HPLC
high-performance liquid chromatography
- HPLC-MS/MS
high-performance liquid chromatography-tandem mass spectrometry
- IPA
isopropyl alcohol
- ACN
acetonitrile
- MeOH
methanol
- PCA
principal component analysis
- OPLS-DA
orthogonal partial least squares discriminant analysis
- KEGG
Kyoto Encyclopedia of Genes and Genomes
- TFs
transcription factors
- Hpt
hygromycin phosphotransferase gene
Authors' contributions
**Tian Wu and Can Wang** : Writing - review & editing, Supervision. **Mingjing Wang** and **Mei Dao** : Writing - original draft, Software, Resources, Methodology, Formal analysis, Conceptualization. **Li Xu, Chang Liu, Gangqiang Dong, and Xuan Wang: ** Software, Resources, Methodology, Formal analysis, Conceptualization.
Funding
This research was supported by grants from Hainan Joint Breeding Project for Key Plants (Am20230493BC), the Key Research and Development Program of Wanning, Hainan (2023wnkj06), and the Extension Project of State Administration of Forestry and Grassland, China ([2019]27).
Data availability
The datasets generated and analysed during the current study are available in the [NCBI] repository, [UBK24468.1]; the role of *McF6'H1* was analyzed using RNA-seq data from two groups (NCBI accession number PRJNA882219).
Declarations
Competing interests
The authors declare no competing interests.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Mingjing Wang and Mei Dao contributed equally to this work.
Contributor Information
Can Wang, Email: wangcan_1983@catas.cn.
Tian Wu, Email: wutianpotato@swfu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analysed during the current study are available in the [NCBI] repository, [UBK24468.1]; the role of *McF6'H1* was analyzed using RNA-seq data from two groups (NCBI accession number PRJNA882219).