Integrated metabolomic and transcriptomic analysis highlight the flavonoid response to antioxidant activity and Aspergillus flavus resistance in peanut seed coats

Abstract

Background

Peanut seeds exhibit a high susceptibility to infection by Aspergillus flavus (A. flavus) post-harvest, which significantly limits their storage and marketability. The peanut seed coat acts as a crucial barrier against A. flavus, containing abundant flavonoids. Despite ongoing research, a comprehensive understanding of the inhibitory effects of flavonoids present in peanut seed coats on A. flavus remains elusive.

Results

This study reveals significant differences in antioxidant activities and resistance to A. flavus among seed coat extracts from different peanut cultivars. A total of 1,314 metabolites were identified through untargeted metabolomics, among which flavonoids enhance both antioxidant activity and pigmentation in the seed coat. Compounds such as anthocyanins, aurones, and chalcones were strongly associated with increased resistance to A. flavus. Notably, cyanidin-3-O-sophoroside exhibited the highest concentration (10.83 mg/g dry weight) and demonstrated a minimum inhibitory concentration (MIC) of 50 µg/mL. By integrating transcriptomic and metabolomic data, we revealed candidate genes and metabolic pathways potentially associated with anthocyanin biosynthesis.

Conclusions

This study provides novel insights into the biochemical mechanisms underlying peanut resistance to A. flavus, while also supporting the valorization of peanut by-products and the breeding of resistant cultivars.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12870-025-07159-5.

Introduction

Peanut (Arachis hypogaea L.) is a globally significant oilseed legume, recognized for its high protein and oil content [1, 2]. However, its rich nutrient profile makes it particularly susceptible to infestation by Aspergillus flavus, a fungus known for producing carcinogenic aflatoxins [3]. This contamination significantly undermines both peanut yield and safety [4]. Under the hot and humid conditions prevalent in many peanut-growing regions, A. flavus readily colonizes developing seeds, leading to aflatoxin accumulation both pre-harvest and post-harvest that may exceed international safety limits [5]. Aflatoxin B₁ (AFB₁), in particular, is a potent hepatocarcinogen, resulting in stringent regulatory limits and substantial economic losses when these limits are surpassed [6]. Therefore, understanding the natural resistance mechanisms in peanuts is crucial for mitigating aflatoxin risk.

The peanut seed coat serves as a primary defense against external stressors, both physical and biochemical. Research on A. flavus infection with and without seed coats revealed that the peanut seed coat is critical in the process of protecting the kernel from A. flavus invasion [6]. In recent years, its role in resistance to A. flavus infection has attracted increasing research interest. According to research, peanuts’ resistance to A. flavus decreases significantly once the seed coat is damaged, and the aflatoxin content is significantly higher in infected peanuts than in those with intact seed coats [7]. In addition to the physical role of the seed coat, secondary metabolites within it, such as tannic acid, phenolic compounds, and flavonoids, provide an important chemical barrier against A. flavus infection. Flavonoids, among various compounds, are particularly notable for their strong antioxidant and antibacterial activities [8, 9]. The production of aflatoxin B1 was inhibited by anthocyanidins, with 3-hydroxy compounds exhibiting greater activity than their 3-deoxy counterparts [10]. Quercetin has been shown to effectively inhibit the growth of Aspergillus flavus, with its inhibitory effect exhibiting significant dose-dependent characteristics [11]. The variations in seed coat color reflect differences in the plant metabolite composition, particularly flavonoids, which are known to confer health benefits [12–14]. This observation suggests a potential link between pigment metabolism and disease resistance.

The biosynthetic pathway of flavonoids has been thoroughly elucidated in species such as Arabidopsis thaliana, soybean (Glycine max), and petunia (Petunia hybrida). Generally, the pathway for flavonoids and the genes that encode the related enzymes show a high degree of conservation across higher plants [15]. The biosynthesis begins with the phenylpropanoid pathway, where phenylalanine is converted to 4-coumaroyl-CoA through the sequential action of phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL). 4-Coumaroyl-CoA then enters the flavonoid pathway, catalyzed by chalcone synthase (CHS) to produce chalcone, which is subsequently converted to naringenin by chalcone isomerase (CHI). Naringenin undergoes hydroxylation and glycosylation reactions, mediated by enzymes such as flavanone 3-hydroxylase (F3H), dihydroflavonol 4-reductase (DFR), and anthocyanidin synthase (ANS), sequentially synthesize flavanonols, leucoanthocyanidins, and anthocyanidins, respectively. Flavonols are synthesized from flavanonol intermediates through the action of flavonol synthase (FLS). Additionally, leucoanthocyanidin reductase (LAR) and anthocyanidin reductase (ANR) reduce leucoanthocyanidins and anthocyanidins, respectively, to produce flavan-3-ols. These flavan-3-ols are then polymerized to form proanthocyanidins. Ultimately, these flavonoids are further stabilized and diversified by glycosyltransferases, acyltransferases, and methyltransferases to produce various flavonoid derivatives [16]. It is well known that the expression of anthocyanin biosynthetic genes is transcriptionally activated by an evolutionarily conserved ternary protein complex, the MYB-bHLH-WD410 (MBW) complex [17]. This complex comprises R2R3-MYB transcription factors (TFs), basic helix-loop-helix (bHLH) TFs, and WD-repeat (WD40) proteins. These components regulate the spatio-temporal expression of flavonoid biosynthesis by binding to the promoters of structural genes, thereby activating their transcription [18].

Alterations in the phytochemical and nutritional components of peanut seed coats, including bioactive compounds, are recognized as significant contributors to resistance against A. flavus infection. However, the specific secondary metabolites in peanut seed coats of various colors that are believed to enhance this resistance, remain poorly defined. This gap in detailed characterization limits our ability to comprehensively assess the nutritional value and composition of antibacterial substances in these seed coats. To bridge this gap, our study utilized integrated metabolomic and transcriptomic analyses across peanut cultivars with white, yellow, pink, red, and black seed coats. We employed multivariate analysis to explore the relationship between phenotypic variations and metabolite profiles. The primary objective of this research is to elucidate the metabolic variances among peanuts with differently colored seed coats, providing essential data to evaluate their nutritional value and their role in enhancing resistance to A. flavus. These insights are crucial for informing future peanut breeding strategies that aim to optimize health benefits and improve disease resistance.

Materials and methods

Peanut materials

Five peanut cultivars were selected for this study: black peanut (BP), red peanut (RP), pink peanut (PP), yellow peanut (YP), and white peanut (WP). These cultivars were sourced from the Henan Academy of Agricultural Sciences and cultivated in the experimental fields of Zhengzhou University, located in Zhengzhou City, Henan Province, China (34°16′N, 112°42′E). Samples for metabolome and transcriptome studies were collected from the seed coats mature peanut 120 days after planting. The seed coats were isolated and immediately flash-frozen in liquid nitrogen. All samples were then stored at −80 °C until they underwent comprehensive transcriptome and metabolome analysis.

Chromatic aberration analysis

The color properties of peanut seed coats of various colors were determined using the 3nh NR 110 precision colorimeter, focusing on the L* (lightness), a* (redness and greenness), and b* value (yellowness and blueness) values. Measurements were conducted in triplicate. The L* value indicates lightness to darkness on a scale from 0 to 100, where a* represents redness (positive) and greenness (negative), while b* denotes yellowness (positive) and blueness (negative).

Sample preparation and extraction

The seed coats from five peanut cultivars were pulverized into a fine powder using liquid nitrogen, and then freeze-dried. A 100 mg sample of this dry powder was mixed with 2 mL of an extraction solution, consisting of 85% methanol and 0.1% formic acid (methanol-to-water ratio of 85:15). The mixture was vortexed for 5 min, ultrasonicated for 30 min, and then refrigerated at 4 °C for 24 h with periodic mixing. After complete extraction, the sample was centrifuged at 6,000 g for 10 min. The supernatant was carefully aspirated, and the sample was subsequently passed through a 0.22 μm microporous filter for further processing.

In vitro assays for Aspergillus flavus resistance

The in vitro antifungal activity assay was carried out following the protocol described earlier [19]. The pathogenic fungus Aspergillus flavus was employed to test antifungal activity using DG-18 agar medium. Briefly, A. flavus was inoculated on DG-18 medium supplemented with extracts from peanut seed coats and incubated in the dark at 30 °C for 7 days to allow the hyphae to spread outward from the center. The growth diameter of A. flavus was measured after 7 days, and the inhibition rate was calculated using the equationprovided below:

Note: N_c represents the A. flavus colony diameter of the control group; N_t denotes the A. flavus colony diameter of the experimental group.

Determination of antioxidant capacity

DPPH radical scavenging activity was assessed using a modified version of a previously reported method [20]. Briefly, 200 µL of DPPH solution and 50 µL of extract from the peanut seed coat were added to a 96-well microplate. The mixture was incubated and the absorbance at 517 nm was measured using microplate reader. (Bio-Rad Model1680, USA). The ABTS radical cation was assessed using a method described in a previous study [21]. The results of the ABTS assay were expressed as l µmol of Vitamin C equivalents/g dry weight of the sample. The reducing power was determined according to the previously reported method [22].

UHPLC-Q-Orbitrap HRMS analysis

The filtrate was analyzed using an ultra-high-performance liquid chromatography (UHPLC) system (Vanquish, Thermo Fisher Scientific, Bremen, Germany) paired with a Q-Exactive mass spectrometer (Thermo Fisher Scientific, Bremen, Germany) for UHPLC-Q-Orbitrap high-resolution mass spectrometry (HRMS). For liquid chromatographic separation, an ACQUITY UPLC BEH C18 column (150 × 2.1 mm, 1.7 μm, Thermo Fisher Scientific, USA) was utilized to separate target analytes at 40 °C. For the 40-min LC gradient, the mobile phase consisted of the following: phase A (0.1% formic acid in water) and phase B (0.1% formic acid in acetonitrile). This program began with 3% of phase B at the start of the run, progressing to 20% of phase B at the 20-minute mark, increasing further to 45% of phase B by 32 min, and then rising linearly to 95% of phase B by the 33-minute point, a concentration that was maintained for an additional 3 min. Afterward, the eluent was returned to the original conditions in 1 min and maintained for 3 min to re-establish equilibrium in the column for the subsequent injection. Furthermore, the autosampler’s needle and sample loop were rinsed three times with a solution of methanol and deionized water in a 1:1 volume ratio.

A volume of 3 µL of standard or sample was injected into the UHPLC-Q-Orbitrap-MS system via the autosampler. The flow rate remained consistent at 0.35 mL/min during the chromatographic analysis, with eluates being detected simultaneously at wavelengths of 535 nm and 360 nm. Xcalibur 2.3 was used for mass data acquisition and the identification of the analytes was conducted as previously described [23]. Full MS scan and MS² scan were performed in positive and negative ionization mode at resolutions of 70,000 and 17,500, respectively. Quality control (QC) samples were generated by mixing all the sample extracts. Untargeted differential comparisons were conducted using Compound Discoverer 3.3 (Thermo Fisher Scientific) to produce a ranked list of significant compounds, accompanied by tentative identifications sourced from BioCyc, the Kyoto Encyclopedia of Genes and Genomes, and internal databases.

Data analysis

Untargeted analysis of metabolomics data was conducted using Compound Discoverer software. Peaks with 10,000 ion intensity and 10 S/N (Signal-to-noise ratio) were identified, and various adducts were consolidated using m/z and the retention time. The identified compounds were then queried against the ChemSpider, Human Metabolome Database, and MZ Cloud fragmentation database to obtain putative identifications. Peaks with multiple identities were confirmed through manual fragmentation analysis. Principal Component Analysis (PCA) was performed using SIMCA software.

RNA-seq analysis

According to the instructions of the kit manual, total RNA was extracted from the seed coats of five peanut cultivars using the Plant RNA Kit (Vazyme). The total RNA extracted from the seed coats of different peanut cultivars was used to construct RNA-Seq libraries, with each peanut variety analyzed in triplicate. The cDNA libraries were sequenced using the Illumina sequencing platform by Novogene (Novogene Co., Ltd., Beijing, China).

Integrative analysis of metabolome and transcriptome

Weighted gene co-expression network analysis (WGCNA) was conducted in R with default parameters to group genes into co-expression modules. Co-expression network modules were derived from raw gene counts obtained from three biological replicates of five peanut cultivars. Raw gene counts obtained from HTSeq analysis were log-transformed and used as input into the WGCNA package (version 1.51) in R [24]. Genes exhibiting low counts (< 1.00) and low correlation coefficients (< 1.00) post-log transformation were filtered out, and the remaining genes were employed to construct an adjacency matrix. The co-expression network modules were generated using the blockwiseModules function along with the default parameters specified in the package. A soft threshold power = 16; TOMtype = signed; mergeCutHeight = 0.25; and minModuleSize = 30 were chosen to indicate significance. Subsequently, metabolome data were incorporated into the WGCNA package to analyze the correlations between gene modules and metabolites. Pearson correlation analysis was conducted between module eigengenes and individual metabolites across matched samples, with gene–metabolite associations deemed significant when the absolute correlation coefficient |r| exceeded 0.8 and p-values were less than 0.05. The joint analysis of the metabolome and transcriptome was conducted using the cor program from R, with a screening criterion of a Pearson correlation coefficient (PCC) greater than 0.8. The relationships between the metabolome and transcriptome data were visualized using Cytoscape (The Cytoscape Consortium, San Diego, CA, United States, version 3.7.0).

Chemicals and reagents

Acetonitrile, methanol, and formic acid were bought from Sigma-Aldrich Co., Ltd. (St. Louis, MO, USA). Epicatechin and cyanidin-3-O-sophoroside of HPLC grade were obtained from Shyuanye Biotechnology Co., Ltd., located in Shanghai, China.

Statistical analysis

Statistical analysis was performed utilizing RStudio (RStudio Team, 2020, Integrated Development for R, RStudio, PBC, Boston, MA). Results are reported as means ± standard deviation (SD) based on three biological replicates. Statistical significance was evaluated using Student’s t-test (asterisks represent significant differences, with *P < 0.05 and P < 0.01) or one-way ANOVA followed by Duncan’s multiple range tests (significance denoted by different letters at P < 0.05).

Result

Characterization of antioxidant properties and resistance to Aspergillus flavus in peanut seed coat metabolites

Five peanut cultivars were cultivated under identical conditions, yet they exhibited distinct phenotypic differences, particularly in seed coat color. These variations included Chinese cultivars: KaiNongBai with a white seed coat (WP), NC92044 with a yellow seed coat (YP), Yuanza9102 characterized by a pink seed coat (PP), DaYu with a red seed coat (RP), and PuYangHeiZhenZhu featuring a black seed coat (BP), as illustrated in Fig. 1A. The assessment of the L* value, a color parameter measured using a colorimeter, indicated that the darkest seed coat color belonged to BP, followed by RP, PP, and YP, with WP being the brightest (Fig. S1). Further evaluation of the a* values, another color parameter, revealed that RP exhibits the strongest red hue, whereas WP tended towards green. These measurements closely aligned with the visually observed color characteristics of the peanut seed coats. Notably, the darker-seeded cultivars are rich in bioactive substances, particularly higher levels of total phenolics and flavonoids compared to the lighter-seeded varieties [25]. To evaluate the resistance of peanut seed coat metabolites to A. flavus, A. flavus was inoculated on DG-18 medium infused with extracts of peanut seed coat and cultured at 30 °C for 7 days. Observations indicated that the growth of A. flavus was not inhibited in plates containing WP, PP, and YP seed coat extracts. Conversely, the RP seed coat extract showed slight inhibition of A. flavus growth, with an inhibition rate of 28.1%. Most notably, the BP seed coat extract exhibited a significant inhibitory effect on A. flavus growth, achieving an inhibition rate of 72.9% (Fig. 1B and C). These findings suggest that darker-colored peanut seed coat extracts exhibit greater antifungal activity against A. flavus in vitro, which may be associated with higher levels of bioactive compounds.

Fig. 1.

Resistance to Aspergillus flavus and antioxidant activity of extracts from seed coat of different peanut cultivars. A Photographs displaying peanut from different cultivars. BP, black peanut; RP, red peanut; YP, yellow peanut; PP, pink peanut; WP, white peanut; (B) Aspergillus flavus was inoculated on DG-18 medium supplied with seed coat extracts from five peanut cultivars and DMSO was the control group; (C) Inhibition rates of seed coat extracts from five peanut cultivars against Aspergillus flavus; (D-F) Determination of DPPH, ABTS and reducing power of extracts from seed coat of five peanut cultivars. Error bars are represented in the standard format, and distinct letters signify significant differences determined through one-way ANOVA, p < 0.05

To evaluate the free radical scavenging capacity of peanut seed coat extracts, DPPH and ABTS assays were conducted along with a total reducing power assay, thereby providing a comprehensive assessment of antioxidant capacity (Fig. 1D and F). Extracts from the seed coats of five peanut cultivars (WP, YP, PP, RP, and BP) exhibited DPPH radical scavenging activities of 1.425, 10.716, 9.358, 17.594, and 27.692 µmol Vc/g, respectively (Fig. 1D). Notably, the BP seed coat extracts showed the strongest antioxidant activity, while the WP extracts demonstrated the lowest, highlighting significant variability in antioxidant potential among the cultivars. The results from the ABTS assay indicated that seed coat extracts from the five peanut cultivars displayed varying degrees of radical scavenging activity, quantified as 2.72, 51.71, 44.47, 61.25, and 114.28 µmol Vc/g for WP, YP, PP, RP, and BP, respectively (Fig. 1E). Importantly, WP extracts exhibited significantly lower ABTS radical scavenging values compared to the other cultivars, whereas the BP extracts recorded the highest values among all tested. Furthermore, BP seed coat extracts not only demonstrated superior ABTS scavenging efficacy but also exhibited the highest total reducing power, indicating a robust antioxidant capacity. This was followed by RP, PP, and YP, with WP again showing the least reducing power, as illustrated in Fig. 1F. The enhanced antioxidant and antifungal activities observed in vitro may be attributed to the darker pigmentation of peanut seed coats.

Metabolomic profiling of peanuts with different seed coat color

To explore metabolite differences potentially associated with the inhibition of A. flavus growth and enhanced antioxidant activity, a metabolomic analysis was performed on the seed coats of five peanut cultivars. An untargeted metabolite analysis was conducted to achieve a comprehensive metabolic profiling of BP, RP, PP, YP and WP using UPLC–MS/MS. Qualitative mass spectrometry analyses, referencing both a local metabolite database and public databases, were performed on the metabolites present in the samples, resulting in the identification of a total of 1,314 metabolites (Table S1). The total ion current (TIC) plots, along with the multi-peak detection plots for the quality control (QC) samples, are presented in Fig. S2. The TIC plot provides a comprehensive representation of the cumulative all ion intensities captured in the mass spectrum over various time intervals. The multi-peak detection plot obtained in the Multiple Reaction Monitoring (MRM) mode demonstrated the presence of multiple substances within the samples.

A comprehensive multivariate statistical analysis was conducted to evaluate the differences in metabolite profiles across five distinct peanut cultivars. Utilizing datasets generated from HPLC-Q-Orbitrap HRMS in both ESI + and ESI- modes, principal component analysis (PCA) was employed to compare the metabolite compositions. The PCA score plot (Fig. 2A) revealed two principal components: PC1, which explained 41.30% of the variance, and PC2, which contributed 17.66%. These results demonstrated clear differentiation among the peanut cultivars, with biological replicates from each variety clustering closely together, thus confirming the high reproducibility and reliability of the data. Furthermore, a heatmap (Fig. 2B) illustrated distinct groupings into five separate classes, highlighting significant variances in the metabolite contents of each variety. These observations suggest that the genetic background may contribute to the differences in metabolite profiles observed among the studied peanut cultivars.

Fig. 2.

Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) of metabolic profiles in five peanut cultivars. A PCA score plot showing sample distribution; (B) HCA heatmap of metabolites, where columns represent samples and rows correspond to metabolites. Red indicates higher metabolite abundance, while blue represents lower abundance. The sampling groups were color-coded as follows: purple, BP; red, RP; pink, PP; yellow, YP; black, WP; green, Mix

Key metabolites in A. flavus resistance and antioxidant activity in peanut seed coats

To investigate the metabolites in peanut seed coats that are associated with A. flavus resistance and antioxidant activities, we categorized the 1,314 identified substances into 12 clusters using the Mfuzz tool, based on their abundance levels in different peanut cultivars. Notably, metabolites in cluster 7 showed abundance patterns correlated with A. flavus resistance and antioxidant activity across various cultivars, in contrast to the other clusters which did not demonstrate distinct patterns (Fig. 3A). Cluster 7 comprises 70 metabolites, including amino acids and derivatives (10.00%), phenolic acids (8.57%), nucleotides and derivatives (1.43%), flavonoids (44.29%), quinones (7.14%), lignans and coumarins (1.43%), tannins (2.86%), alkaloids (5.71%), organic acids (5.71%), and others (17.14%) (Fig. 3B; Table S2). We utilized the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, a comprehensive public resource for metabolic pathways, to classify compounds from cluster 7 into various biochemical pathways. These pathways include flavonoid biosynthesis, phenylpropanoid biosynthesis, isoflavonoid biosynthesis, degradation of aromatic compounds, and anthocyanin biosynthesis (Fig. S3; Table S3). This analysis indicates that flavonoids, the most abundant subclass, are likely involved in the observed differences in the observed variances in A. flavus resistance and antioxidant activities [8, 9]. These flavonoids are categorized into nine classes: anthocyanins (22.58%), aurones (6.90%), chalcones (12.90%), flavanols (12.90%), flavanones (6.45%), flavones (19.35%), flavonols (6.45%), proanthocyanins (6.45%), and others (6.45%) (Fig. 3C).

Fig. 3.

Mfuzz cluster of all metabolites. A The proximity of metabolites to the cluster center is visualized: red indicates the closest proximity, green represents intermediate proximity, and yellow shows the farthest proximity; (B) Classification of metabolites of the subclass 7 in Mfuzz; (C) Classification of the flavonoids compound; (D) Mantel test between different types of flavonoids and color difference values, antioxidant activity and Aspergillus flavus inhibition ratio. The heatmap displays Pearson correlations between different types of flavonoids and color difference values, antioxidant activity and Aspergillus flavus inhibition ratio. The line thickness represents the Mantel test correlation coefficient, while the line color indicates the p-value. Significance levels are denoted as * P < 0.05, ** P < 0.01, *** P < 0.001

The Mantel test was employed to validate the correlations among seed coat color, antioxidant activity, resistance to A. flavus, and the presence of flavonoids (Fig. 3D). Pearson correlation analysis revealed that the brightness value (L*) of the peanut seed coat exhibited a significant negative correlation (P < 0.001) with antioxidant activity and inhibition ratio. This finding indicates that darker seed coat colors are associated with increased antioxidant activity and enhanced resistance to A. flavus, consistent with observed phenotypic variations. Additionally, the red-green value (a*) of the seed coat showed a significant positive correlation (P < 0.05) with both antioxidant activity and inhibition ratio. Furthermore, assays for DPPH (P < 0.01), ABTS (P < 0.001), and total reducing power (P < 0.001) each demonstrated significant positive correlations with the inhibition ratio, suggesting that darker and redder seed coat colors are correlated with higher antioxidant activity and greater resistance to A. flavus. Mantel test analysis identified significant correlations between 9 types of secondary metabolites and both the brightness and antioxidant activity of the seed coat (P < 0.01). Notably, key metabolites such as aurones (maritimetin, sulfurein), anthocyanins (cyanidin-3-O-rutinoside, cyanidin-3-O-sambubioside, cyanidin-3-O-sophoroside, cyanidin-3-O-xylosylrutinoside, petunidin, patuletin-3-rutinoside, petunidin-3-O-5-O-(6-O-coumaroyl)-diglucoside), chalcones (phloretin 3',5 '-Di-C-glucoside, 3,4,4',α-tetrahydroxy-2'-methoxydihydrochalcone, galiposin, monospermoside), and other flavonoids (Schaftoside 6'-O-glucoside, 2'-Hydroxydaidzein) were significantly associated with the inhibition ratio of A. flavus growth, suggesting their potential functional involvement (Fig. 3D).

The role of anthocyanins in inhibiting A. flavus in peanut seed coat

The analysis of 70 metabolites in module 7 identified epicatechin, a flavanol, as the most abundant compound. Following epicatechin, the anthocyanins cyanidin-3-O-sophoroside, cyanidin-3-O-sambubioside, and cyanidin-3-O-xylosylrutinoside ranked second, third, and fifth, respectively. Proanthocyanidin A3, a member of the proanthocyanidins, ranked fourth (Fig. S4). To evaluate their inhibitory effects on A. flavus, various concentrations of epicatechin and cyanidin-3-O-sophoroside were tested using DG-18 medium (Fig. 4A). The results indicated that epicatechin had no significant impact on A. flavus growth, whereas cyanidin-3-O-sophoroside showed significant antifungal activity. The minimum inhibitory concentration (MIC) for cyanidin-3-O-sophoroside was determined to be 50 µg/mL (Fig. 4B and C).

Fig. 4.

Inhibitory effects of different flavonoids concentrations on Aspergillus flavus. A Aspergillus flavus was inoculated on DG-18 medium supplied with different concentrations of epicatechin and anthocyanidin; (B) Inhibition rates of Aspergillus flavus by epicatechin at varying concentrations; (C) Inhibition rates of Aspergillus flavus by anthocyanins at varying concentrations. Error bars are represented in the standard format through one-way ANOVA, * P < 0.05, ** P < 0.01, *** P < 0.001

To further investigate whether the accumulation of key antifungal compounds associated with resistance to A. flavus was exclusive to black-colored seed coats, we expanded the sampling within each color group for anthocyanin quantification. The extracts of peanut seed coats with varying colors were analyzed in detail using HPLC. It was found that only the black cultivars (PYH, YH0215 and NYC) contained anthocyanins, and there were species differences. A total of three major anthocyanins (cyanidin-3-O-sophoroside, cyanidin-3-O-sambubioside and cyanidin-3-O-xylosylrutinoside in reference with peak 1, 2, and 3 in Fig. S5, respectively) were identified in the black seed coat of PYH. Similarly, three major anthocyanins were identified in the purple seed coat of YH0215, while only two major anthocyanins (cyanidin-3-O-sambubioside and cyanidin-3-O-xylosylrutinoside) were detected in the black-striped seed coat of NYC. Notably, no accumulation of anthocyanins was observed in the seed coats of other peanut cultivars. Furthermore, the anthocyanins identified in PYH, YH0215 and NYC were all glycosylated forms of cyanidin, specifically located at the C3 position on the B ring of the molecular structure. These findings suggest that various black peanut cultivars likely share similar anthocyanidin biosynthetic routes, with different glycosylation modifications. The total anthocyanin content in the PYH seed coat was the highest at 20.19 mg/g dry weight, followed by 15.7 mg/g dry weight in the YH0215 seed coat, and 13.42 mg/g dry weight in the NYC seed coa. In the black seed coat of PYH, cyanidin-3-O-sophoroside exhibited the highest concentration at 10.83 mg/g dry weight significantly greater than that of cyanidin-3-O-sambubioside at 7.37 mg/g dry weight and cyanidin-3-O-xylosylrutinoside at 1.99 mg/g dry weight (Table S4). These results indicate that anthocyanins, in various modified forms, are exclusively accumulated in black peanut seed coats, while no anthocyanin accumulation is observed in seed coats of other colors. These anthocyanins are likely the key factors contributing to the ability of black peanut seed coat extracts to inhibit the growth of A. flavus.

Combined transcriptomic and metabolic analysis of flavonoid pathway in peanut seed coat

To systematically investigate the molecular mechanisms underlying the differences in anthocyanin components among five cultivars of peanuts, RNA sequencing (RNA-Seq) was employed for transcriptome analysis. The RNA-Seq raw data were confirmed to be of high quality, and the results from sequencing and assembly proved to be reliable for subsequent analyses (Table S5). The application of Principal Component Analysis (PCA) demonstrated an acceptable level of reproducibility for the methodologies employed in transcriptome analysis. Notably, in the PCA score plot, two principal components were identified: PC1, which accounted for 33.22% of the variance, and PC2, which contributed 26.24% (Fig. S6A). A total of 11,350 differentially expressed genes (DEGs) were identified in the comparisons of ‘BP’ vs. ‘RP’, 6,498 in ‘BP’ vs. ‘PP’, 14,430 in ‘BP’ vs. ‘YP’, and 12,169 in ‘BP’ vs. ‘WP’, using the criteria of false discovery rate (FDR) ≤ 0.05 and absolute value of log fold-change (logFC) ≥ 1. In these comparison groups, 4,517, 2,673, 6,375 and 6,002 genes were found to be up-regulated, while 6,833, 3,825, 8,055 and 6,167 genes were down-regulated, respectively (Fig. S6B). A total of 3,398 DEGs were shared among all four comparison groups (Fig. S6C). The significantly enriched KEGG categories identified from the DEGs include Phenylalanine metabolism and Flavonoid biosynthesis, both of which are pathways associated with anthocyanin biosynthesis (Fig. S6D). The results of the KEGG pathway analysis indicated that the mRNA levels of genes related to Phenylalanine metabolism and Flavonoid biosynthesis exhibited significant changes across the various colors of peanut seed coats.

To investigate the regulatory mechanisms governing anthocyanin biosynthesis in peanut seed coats, weighted gene co-expression network analysis (WGCNA) was performed using a dataset comprising 34,453 unique genes (Table S6). A soft-thresholding power (β) was selected to construct a weighted gene co-expression network, achieving a scale-free topology. Testing β values ranging from 1 to 20 led to the identification of a soft-thresholding power of 12, ensuring an R² ≥ 0.8 and meeting the criteria for a scale-free network (Fig. S7). The genes were categorized into 25 distinct modules, each represented as a major branch of the hierarchical cluster tree and identified by a unique color (Fig. 5A). These modules consist of tightly co-expressed genes, indicating strong interrelationships within each group. Correlation analysis between modules and anthocyanin accumulation revealed that the pink and yellow modules were highly positively associated with the majority of anthocyanins (correlation coefficient > 0.8, 0 < P < 0.05). These modules may include candidate genes potentially associated with anthocyanin biosynthesis in peanut seed coats (Fig. 5B). To gain insight into the functional roles of genes within these modules, KEGG pathway enrichment analysis was conducted, revealing that the genes in the pink and yellow modules were primarily enriched in pathways related to Metabolism, Genetic Information Processing, and Environmental Information Processing. In the pink module, the pathway associated with anthocyanin accumulation is flavonoid biosynthesis, which comprises five genes (Fig. 5C). In the yellow module, the pathway linked to anthocyanin accumulation is phenylpropanoid biosynthesis, which includes fourteen genes (Fig. 5D). Correlation analysis revealed that ten structural genes within these two KEGG pathways showed a significant positive correlation with anthocyanin accumulation, suggesting that genes such as 4CL (AhSRT95Q and AhBDR360), CHS (Ah6J3HHE), and F3'H (Ah8F7PE4 and AhK8H9R8), with correlation coefficients exceeding 0.8, may play potential roles in regulating anthocyanin biosynthesis (Fig. 5E).

Fig. 5.

Weighted gene co-expression network analysis (WGCNA) revealing co-expressed gene modules within five peanut samples. A Hierarchical cluster dendrogram of 25 gene co-expression modules; (B) Associations between modules and anthocyanins; (C) KEGG enrichment analysis of genes in the pink module; (D) KEGG enrichment analysis of genes in the yellow module; (E) Correlation network diagram between anthocyanins and candidate genes. Each row represents a module. Each row corresponds to a module, and each cell, according to the color scale, represents the correlation coefficient between anthocyanin compounds and the module, with the p-value provided in parentheses

Differences in genes and metabolites involved in flavonoid synthesis in colored peanuts

A model of the flavonoid biosynthesis pathway in the colored peanut seed coat was constructed based on the expression profiles of critical genes and metabolites (Fig. 6). In peanut seed coats of varying colors, it was observed that not only do anthocyanins differ significantly among different cultivars, but the accumulation levels of chalcones, dihydroflavonols, flavonols, flavones, isoflavones, flavanones, and proanthocyanidins also vary considerably. In the yellow peanut seed coat, the accumulation of kaempferol-based flavonols may contribute substantially to the yellow coloration. In the seed coat of red peanuts, the presence of quercetin-based, rhamnetin-based, and isorhamnetin-based flavonols, along with a general accumulation of flavones and isoflavones, contributes to the red phenotype. In the seed coat of pink peanuts, the coloration may be attributed to the accumulation of chalcones, dihydroflavonols, and certain flavonols. Finally, the black seed coat phenotype is attributed to the accumulation of anthocyanins and proanthocyanidins.

Fig. 6.

Transcriptomic and metabolomic profiling of the anthocyanin and flavonoid biosynthetic pathways across different peanut cultivars. The heatmap indicates metabolite abundance and transcript expression, The circles represent genes and the squares represent metabolites

In black peanuts, the transcript levels of key genes involved in anthocyanin biosynthesis, including CHS (AhUDJX6I, AhCM90T6, Ah0FI6RG, Ah6J3HHE), F3H (Ah79B99S, AhHE8J5U), F3'H (Ah8F7PE4, AhK8H9R8), DFR (AhX8EVF3), ANS (AhAQ6B1J, AhUQ0Z3E), and UFGT (AhENIS4M), were significantly elevated. This upregulation coincided with an increased accumulation of anthocyanins, suggesting a potential enhancement of metabolic flux through the anthocyanin biosynthetic pathway. The structural genes in the black peanut seed coats exhibited markedly higher FPKM values, which drove increased flux through the anthocyanin biosynthetic pathway compared to other seed coat colors. Notably, the modulation of secondary metabolites in the black peanut seed coat was primarily driven by the substantial upregulation of structural genes associated with the anthocyanin biosynthetic pathways (Fig. 6). Enhanced catalytic efficiency of F3'H (Ah8F7PE4, AhK8H9R8) and UFGT (AhENIS4M) facilitated the biosynthesis of a diverse array of anthocyanins, incorporating diverse structural modifications such as hydroxylation and glycosylation. Furthermore, TT2 (AhA2IWKV), bHLH (AhMP3D3D and AhHM1IVV), WD40 (AhL6JJW9), and WRKY (AhB1IT3E and Ah431KH3) were identified as potential regulators due to their high transcript abundance in black peanut seed coats (Figs. 5E and 6).

Discussion

Traditional peanut cultivars typically feature pink or red seed coats; however, recent attention has shifted towards purple seed coat cultivars due to their promising nutraceutical properties. Despite these beneficial characteristics, the utilization of peanuts remains limited due to insufficient exploration of their nutritional values and active functional ingredients. Previous studies have demonstrated that dark-colored peanut seed coats contain significantly higher levels of total phenolics and flavonoids compared to their light-colored counterparts, with anthocyanin content showing a notable correlation with seed coat color [26]. Flavonoid colors range from white to yellow (flavonols) and reddish to blue (anthocyanins), contributing to the vibrant hues of many fruits and vegetables. In this study, dark-colored peanut seed coats were found to contain a variety of flavonoids, including anthocyanins (22.58%), aurones (6.90%), chalcones (12.90%), flavanols (12.90%), flavanones (6.45%), flavones (19.35%), flavonols (6.45%), proanthocyanins (6.45%), and others (6.45%) (Fig. 3C). These compounds are not only strongly correlated with the brightness of peanut seed coats but also exhibit significant relationships with the a* and b* values, as shown in Fig. 5. This underscores the pivotal role that flavonoids play in influencing the pigmentation of peanut seed coats. With the exception of aurones (maritimetin, sulfurein), anthocyanins (cyanidin-3-O-rutinoside, cyanidin-3-O-sambubioside, cyanidin-3-O-sophoroside, cyanidin-3-O-xylosylrutinoside, petunidin, patuletin-3-rutinoside, petunidin-3-O-5-O-(6-O-coumaroyl)-diglucoside), and others (Schaftoside 6'-O-glucoside, 2'-Hydroxydaidzein), other flavonoids are significantly correlated with the a* value. This indicates that an increase in these substances corresponds to a redder hue in the peanut seed coat (Fig. 3D).

The commercial development of plants as sources of antioxidants presents significant potential for enhancing the properties of foods. Flavonoids, known for their health-promoting properties as dietary antioxidants, consist of two benzene rings (A and B rings) connected by a three-carbon heterocyclic pyran ring (C ring). The chemical activities of flavonoids, particularly their reducing properties as hydrogen or electron-donating agents, suggest their potential role as free-radical scavengers (antioxidants). Flavonoids such as quercetin and epicatechin have been demonstrated to protect cells from oxidative damage by upregulating antioxidant enzymes like superoxide dismutase and catalase [27]. Notably, the antioxidant capacities of catechin and catechin-gallate esters derived from green tea are more effective on a molar basis than vitamin C, which is widely recognized as an antioxidant in plants [28]. DPPH, ABTS, and total reducing power tests further illustrate that the antioxidant capabilities of the purple seed coats are significantly superior to those of other cultivars (Fig. 1D and F), underscoring its extensive antioxidant potential. Our analyses, employing methods such as Mfuzz and Mantel, identified flavonoids as crucial contributors to both the antioxidant activity and pigmentation of peanut seed coats. The presence of both anthocyanins and flavonoids in dark seed coats may contribute collectively to the enhanced antioxidant activity observed. The interaction between these compounds can produce additive or synergistic effects, enhancing their ability to neutralize reactive oxygen species (ROS). This finding is consistent with previous research, which reported that mixtures of anthocyanins and flavonoids exhibited greater antioxidant capacity than individual compounds in vitro [29]. Such interactions may arise from the stabilization of free radicals by multiple phenolic groups, which leads to a more effective neutralization of oxidative species. Flavonoids not only enhance the commercial value of peanut seed coats by imparting vibrant colors, but they also serve as antioxidant dietary supplements that promote health, thereby maximizing the utilization of peanut byproducts.

A. flavus infection poses a significant threat to peanut production. Natural compounds derived from plants exhibit excellent antibacterial, insecticidal, and antioxidant properties. Notably, the purple peanut seed coat extracts demonstrate the most significant inhibitory effect on A. flavus, surpassing that of the red seed coat, while the pink, yellow, and white seed coats exhibit markedly weaker effects (Fig. 1B). In the present study, we employed an untargeted metabolomics approach to investigate the metabolite profiles of colored peanut seed coats. The analysis identified 70 metabolites consistent with the aforementioned trend, including amino acids and derivatives (10.00%), phenolic acids (8.57%), nucleotides and derivatives (1.43%), flavonoids (44.29%), quinones (7.14%), lignans and coumarins (1.43%), tannins (2.86%), alkaloids (5.71%), organic acids (5.71%), and others (17.14%) (Fig. 3B) (Table S2). Flavonoids can exert antibacterial activity by disrupting energy metabolism, inhibiting nucleic acid synthesis,, and damaging the cytoplasmic membrane, thus categorizing them as constitutive antibacterial substances [30]. Among these, quercitrin, isolated from the aerial parts of Tamarix balansae, exhibits broad-spectrum antibacterial activity against Staphylococcus aureus (S. aureus), methicillin-resistant S. aureus (MRSA), Enterococcus faecalis, Staphylococcus epidermidis, Pseudomonas aeruginosa, and Escherichia coli [31]. Mechanistic studies have revealed that quercitrin may exert its anti-MRSA effects through the modulation of penicillin-binding protein 2a [32]. Flavonoids, including naringin, hesperidin, and neohesperidin found in Citrus species, exhibit various inhibitory effects against four common food contaminant fungi: Aspergillus flavus, Aspergillus parasiticus, Fusarium semitectum, and Penicillium expansum [33]. The production of aflatoxin B1 is inhibited by anthocyanins, with 3-hydroxyl compounds demonstrating greater activity than their 3-deoxy counterparts [10]. To assess the antimicrobial efficacy of compounds in peanut seed coats, epicatechin and anthocyanin were tested. Interestingly, epicatechin did not inhibit A. flavus growth, the minimum inhibitory concentration (MIC) for anthocyanin was determined to be 50 µg/mL, as depicted in Fig. 4B. This finding suggests that anthocyanins may serve as promising antimicrobial compounds against A. flavus, warranting further investigation into their potential application in peanut preservation or breeding. The potential use of these flavonoids as natural fungicidal agents presents an intriguing opportunity for peanut by-products. In summary, this study presents a comprehensive metabolomic analysis of the mechanisms underlying peanut seed coat resistance to A. flavus infection. The identified metabolites provide a valuable foundation for advancing our understanding of plant-fungus interactions and may offer potential applications in controlling peanut pathogens, including A. flavus. Furthermore, these findings support the development of peanut cultivars with enhanced resistance to this pathogen.

Despite the valuable insights provided by our integrated metabolomic and transcriptomic analyses into the flavonoid-mediated defense mechanisms in peanut seed coats, several limitations should be acknowledged. First, the current study focused exclusively on mature seeds, thereby overlooking the dynamic regulatory events that occur during the early developmental and infection stages. Second, although strong correlations were observed between gene expression and metabolite accumulation, functional validation of candidate genes is necessary to confirm their roles in anthocyanin biosynthesis. Additionally, genetic variation among the analyzed cultivars may also influence flavonoid accumulation. Investigating sequence polymorphisms or structural variants in key biosynthetic and regulatory genes using the peanut reference genome could further elucidate the genetic basis underlying pigment diversity. Future studies should incorporate multi-omics time-series analyses, expand cultivar diversity, and integrate whole-genome resequencing with transcriptome and metabolite profiling to comprehensively dissect the regulatory networks controlling seed coat pigmentation and disease resistance in peanuts.

Conclusion

Peanut seed coat extracts with varying colors exhibit significant differences in antioxidant capacity and susceptibility to Aspergillus flavus infection. Utilizing an untargeted metabolomics approach, this study analyzed five peanut cultivars distinguished by their distinct seed coat colors. The results indicated that flavonoids play a crucial role in both the antioxidant activity and pigmentation of the seed coat. Notably, compounds such as anthocyanins, aurones, and chalcones have been associated with resistance to A. flavus infection, indicating their potential role in antifungal defense. Furthermore, transcriptome analysis provided a molecular framework for understanding the observed metabolic changes, suggesting that transcription factors such as bHLH, MYB, and WRKY may regulate flavonoid biosynthesis. This study not only deepens our understanding of the mechanisms underlying flavonoid synthesis in peanut seed coats but also offers molecular insights for developing high-quality peanut cultivars with improved resistance to fungal pathogens.

Abbreviations

A. flavus

Aspergillus flavus

MIC

Minimum inhibitory concentration

PAL

Phenylalanine ammonia-lyase

C4H

Cinnamate 4-hydroxylase

4CL

4-coumarate-CoA ligase

CHS

Chalcone synthase

CHI

Chalcone isomerase

F3H

Flavanone 3-hydroxylase

DFR

Dihydroflavonol 4-reductase

ANS

Anthocyanidin synthase

FLS

Flavonol synthase

LAR

Leucoanthocyanidin reductase

ANR

Anthocyanidin reductase

TFs

Transcription factors

UHPLC-Q-Orbitrap HRMS

Ultra-High Performance Liquid Chromatography coupled with quadrupole Orbitrap high-resolution mass spectrometry

BP

Black peanut

RP

Red peanut

PP

Pink peanut

YP

Yellow peanut

WP

White peanut

QC

Quality control

TIC

Total ion current

MRM

Multiple Reaction Monitoring

Authors’ contributions

Xuejie Zhao: Investigation, Data curation, Writing – original draft. Minghui Xing: Formal analysis. Lijing Zhang: Software. Suoyi Han: Formal analysis. Xinyou Zhang: Conceptualization, Supervision. Jinyong Huang: Writing – review & editing, Project administration.

Funding

This work was supported by the National Supercomputing Innovation Ecosystem Construction Science and Technology Project (201400210600).

Data availability

All data generated and analyzed during this study are included in this published article and its supplementary information files. The datasets generated and analysed during the current study are available in the Sequence Read Archive (SRA) repository in National Center for Biotechnology Information (NCBI) under the accession number PRJNA1243566.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Completing interests

The authors declare that they have no competing interests.