Metabolites of epigallocatechin gallate and changes in antioxidant activity through biotransformation with Eurotium cristatum during liquid-state fermentation

Abstract

Eurotium cristatum, the dominant fungus in Fu brick tea (FBT) production, critically determines its characteristic quality. Epigallocatechin gallate (EGCG)—the predominant and bioactive tea catechins—undergoes substantial modification during FBT processing, though its microbial-driven biotransformation pathways remain poorly understood. This study comprehensively analyzed E. cristatum-mediated EGCG metabolism during liquid-state fermentation, characterizing the metabolite profiles and their functional implications. Fermentation greatly increased total phenolics (40.16%) and flavonoids (3.2-fold) contents through fungal enzymatic activities (tannase, polyphenol oxidase, catalase, cellulase, and α-amylase). These enzymes catalyzed EGCG's sequential conversion into primary intermediates (gallic acid and epigallocatechin), followed by structural diversification via sulfation, glycosylation, methylation, and polymerization, significantly enhancing antioxidant capacity. Untargeted metabolomics revealed 128 differential metabolites, with nine major EGCG derivatives characterized and demonstrating potent antioxidant activity. Notably, EGCG displayed dual roles: as a bioconversion substrate and a growth stimulant, increasing E. cristatum biomass by 61.54% through pH and carbon-to‑nitrogen (C/N) ratio modulation. Our findings elucidate the enzymatic mechanisms and metabolic network of EGCG biotransformation by E. cristatum, providing insights for potential FBT quality enhancement.

Graphical abstract

Highlights

• •EGCG undergoes multiple biochemical transformations during fermentation.
• •Untargeted metabolomics identified 128 EGCG-derived metabolites after fermentation.
• •EGCG promoted the growth of E. cristatum during fermentation.
• •Abundant hydrolytic enzymes secreted by E. cristatum facilitated EGCG transformation.
• •EGCG fermented with E. cristatum significantly enhanced the antioxidant activity.

1. Introduction

Tea catechins, the primary bioactive constituents responsible for both sensory attributes and health-promoting effects, have been extensively studied in tea chemistry research (Cadoná et al., 2022). FBT, a distinctive Chinese post-fermented dark tea variety, contains characteristic catechins profiles dominated by epigallocatechin gallate (EGCG), epicatechin gallate (ECG), epigallocatechin (EGC) and epicatechin (EC), with smaller amounts of gallocatechin gallate (GCG), gallocatechin (GC), catechin gallate (CG) and catechin (C) also present (Xiao, Yang, et al., 2023; Zhu et al., 2020). EGCG, the most abundant catechins, has attracted considerable interest due to its potent antioxidant, anticancer, and cardiovascular protection (Ma et al., 2022; Zhao et al., 2024). However, the bioactivity of EGCG is limited by its chemical instability under physiological and processing conditions, including pH sensitivity, thermal degradation, and enzymatic hydrolysis (Dai et al., 2020; Krupkova et al., 2016). To address these challenges, microbial biotransformation—an environmentally friendly approach—has emerged as a viable strategy to elevate the stability and bioactivity of catechins. Recent studies have revealed significant changes in the catechins profile during FBT microorganism production, with EGCG undergoing the most pronounced structural modifications (Han et al., 2024; Huang et al., 2024). These transformations are key contributors to FBT's unique sensory properties and health-promoting effects (i.e., antioxidant activity) (Huang et al., 2023; Xiao, He, et al., 2022). Furthermore, structural modifications of metabolites have been demonstrated to enhance their biological activity (Amor et al., 2024; Chen et al., 2018; Dai et al., 2020), and green analytical techniques have been increasingly applied in tea-related research (Chen et al., 2015; Ridha et al., 2024; Zhang et al., 2024). However, most existing studies focus on total catechins change rather than EGCG-specific metabolism, leaving the precise biotransformation pathways of EGCG during fermentation poorly understood.

Eurotium cristatum, the dominant fungus in FBT fermentation, plays a pivotal role in molding the tea's sensory and functional properties (Chen, Peng, et al., 2023; Xiao et al., 2024; Xiao, He, et al., 2022). This filamentous fungus secretes a diverse array of hydrolytic enzymes (cellulase, tannase, pectinase, and β-glucosidase) which collectively drive the breakdown of complex tea constituents (polyphenols, polysaccharides, and proteins) into smaller bioactive molecules (Wang et al., 2021). These enzymatic modifications not only alter catechins composition but also influence their antioxidant capacity (Du et al., 2022). Recent studies have systematically characterized metabolic changes induced by E. cristatum in various tea matrices (Song et al., 2025). For instance, Wang, Shan, et al. (2023) reported significant reductions in ECG, C, and EGCG levels during black tea fermentation, alongside increased production of caffeine and GCG. Similarly, An et al. (2021) observed a decline in galloylated catechins and free amino acids but a rise in non-galloylated catechins, theabrownins, and alkaloids after fermenting green tea powder with E. cristatum. Solid-state fermentation of Pu-erh tea with E. cristatum further demonstrated marked changes in polyphenol profiles, accompanied by darker coloration, reduced astringency, and a distinctive moldy aroma (Wang et al., 2025). These findings collectively highlight E. cristatum's critical role in modulating tea color, flavor, and bioactivity through three key biochemical pathways: (1) pigment formation (e.g., thearubigins) influencing color (Lin et al., 2021), (2) amino acid and phenolic compound alterations affecting taste (Xiao, He, et al., 2022), and (3) volatile compound (ketones, aldehydes, esters) modifications shaping aroma (Xiao, Huang, et al., 2022).

Despite these advances, the specific mechanisms of E. cristatum-mediated EGCG biotransformation—particularly the structural modifications, metabolic intermediates, and their impact on antioxidant activity—remain unclear. To address this gap, we conducted liquid-state fermentation of EGCG with E. cristatum and systematically analyzed: (1) dynamic changes in total phenolics content (TPC), total flavonoids content (TFC), EGC, and gallic acid (GA); (2) key enzymatic activities; and (3) antioxidant capacity. Using ultra performance liquid chromatography-time of flight-tandem mass spectrometry (UPLC-TOF-MS/MS)-based metabolomics, we identified EGCG-derived metabolites and employed correlation analysis to elucidate relationships between metabolic shifts, enzyme activity, and antioxidant effects. This study provides brand-new mechanistic insights into E. cristatum's role in EGCG biotransformation and its contribution to FBT quality development.

2. Materials and procedures

2.1. Chemicals and reagents

Standard compounds EGCG, EGC, GA, rutin, glucose, along with the reagents ABTS (2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid), Folin-Ciocalteu reagent and DPPH (2,2,2-diphenyl-1-picrylhydrazyl) were acquired from Sigma-Aldrich company in St. Louis, Missouri from USA. All further reagents were of analytical purity. E. cristatum (CCTCC NO: M2023397) was previously isolated from FBT.

2.2. Sample preparation

2.2.1. E. cristatum strain activation and preparation of spore suspensions

The E. cristatum strain was retrieved from storage and inoculated onto M40Y solid medium. Following 7 days of incubation at 28 °C, colonies were fully developed. The spores were washed off the plate with sterile water, then dispersed uniformly by vortexing and oscillation. Spore concentration was measured using a hemocytometer under an optical microscope and adjusted to 10⁸ spores/mL with sterile water before being stored for subsequent use.

2.2.2. Preparation of EGCG solution and liquid-state fermentation with E. Cristatium

EGCG was accurately weighed and dissolved in ultrapure water to prepare a stock solution, which was then sterilized by filtration through a 0.22 μm membrane filter. A 2 mL aliquot of E. cristatum spores was inoculated into 100 mL of liquid Potato Dextrose Agar (PDA) medium. The EGCG stock solution was added to obtain a final concentration of 1.2 mg/mL (group JE). Additionally, a control group (group J) was also prepared as follows: 100 mL of liquid PDA medium was inoculated with 2 mL E. cristatum spore suspension and sterilized water. The cultures were maintained at 28 °C, 150 rpm continuous agitation for 24 days, with samples collected every 6 days to isolate mycelium and filtrate via filtration. Sampling intervals were determined based on significant EGCG, EGC, and GA conversion points observed in preliminary experiments. Additionally, a control medium containing EGCG (1.2 mg/mL final concentration) in liquid PDA was prepared under the same culture conditions (28 °C, 150 rpm shaking for 24 days), but without E. cristatum inoculation.

2.3. Determination of biochemical indicators

2.3.1. Determination of mycelial biomass, pH, soluble protein and soluble sugar

The mycelial biomass of E. cristatum was determined by filtering the fermentation broth through pre-weighed and numbered quantitative filter paper to collect the mycelium. After washing with distilled water, the mycelium was dehydrated at 105 ± 1 °C until a constant weight was achieved. The dry weight of the filter paper was subtracted to obtain the mycelium dry weight. The pH was determined with a high-precision pH meter. Soluble protein content was analyzed by reacting 1 mL sample with 5 mL Coomassie Blue reagent for 2 min before measurement at 595 nm (Huang et al., 2022). A bovine serum albumin (BSA) calibration curve was used to report protein concentration in μg/mL. Soluble sugar content was quantified by the procedure of Li et al. (2014). Aliquot 0.5 mL of the sample into 2 mL of anthrone solution, and heat in boiling water for 10 min. Cooled to 25 °C for 10 min before reading absorbance at 620 nm. A standard curve with glucose standard was plotted, with concentrations expressed as glucose equivalents (mg/mL).

2.3.2. Evaluation of total phenolics content (TPC) and total flavonoids content (TFC)

TPC was measured via the Folin-Ciocalteu method at 720 nm (Paiva et al., 2020), with a standard curve for GA (y = 0.0026x + 0.0016, R² = 0.9996). TFC was assessed using the aluminum chloride assay at 510 nm (Xiao, He, et al., 2022), employing a rutin standard curve (y = 0.783x + 0.0031, R² = 0.9995). Results are expressed in GA equivalents (μg/mL) for TPC and rutin equivalents (μg/mL) for TFC.

2.4. Determination of EGCG, GA and EGC via high-performance liquid chromatography (HPLC)

EGCG, EGC and GA were quantitatively analyzed on an Agilent 1260 HPLC system (USA). Prior to analysis, samples were membrane-filtered (0.22 μm PVDF) and 10 μL aliquots were chromatographed on an Agilen-C18 reversed-phase column (4.6 × 250 mm, 5 μm) at 30 °C. 0.1 % aqueous formic acid (A) and acetonitrile (B) was delivered at 0.8 mL/min with 10–35 % B (0–40 min), 35–10 % B (40–42 min). Detection was carried out at 280 nm using a G7114A VWD detector. Analytes was identified by matching retention times and spectra with standards.

2.5. Determination of microbial hydrolases activity

Tannase activity was determined according to An et al. (2021), with one unit (U) representing 1 μmol GA produced per minute from propyl gallate at 30 °C and pH 5.5. Polyphenol oxidase activity was determined according to de Oliveira Carvalho and Orlanda (2017), measuring the increase in absorbance at 420 nm due to substrate oxidation. The reaction mixture (0.2 mL enzyme solution, 50 mmol/L catechol and 0.05 mol/L phosphate buffer) was incubated at 35 °C. Activity was quantified as an increase of 0.001 absorbance units per minute. Catalase activity was determined according to the ammonium molybdate colorimetric method described by Liu, Ji, et al. (2023) and Tian (2022), where enzyme activity was defined as the amount required to decompose 1 μmol of H₂O₂ per minute. Cellulase and α-amylase activities were determined based on the method described by Liu, Shi, et al. (2022) and Huang et al. (2025), where enzyme activities were defined based on their ability to release 1 μmol of glucose from soluble starch and carboxymethyl cellulose under conditions of pH 5.0 and 50 °C. Using inactivated enzyme as control, enzyme activity was expressed as units per milliliter (U/mL) of fermentation broth.

2.6. Analysis of EGCG metabolites using UPLC-TOF-MS/MS-based non-targeted metabolomics analysis

2.6.1. Time points selecting for UPLC-TOF-MS/MS analysis

The time points of EGCG liquid-state fermentation sample for UPLC-TOF-MS/MS analysis were selected based on the changes in EGCG, EGC, and GA concentrations observed through HPLC analysis. On day 0, the sample was in its original state without fermentation or components transformation. By 6 days of fermentation, most of the EGCG had been converted into EGC and GA. On day 12, EGCG was fully converted, with EGC largely degraded, and GA began to decrease. Finally, all EGCG and EGC had been transformed after 18 days of fermentation, and most of the GA was degraded. This structured timeline provided a comprehensive understanding of the metabolic changes throughout the fermentation process and used for UPLC-TOF-MS/MS-based non-targeted metabolomics analysis.

2.6.2. UPLC-TOF-MS/MS analysis parameter setting

Quality control samples were prepared by mixing equal volumes of each sample to match the test sample volumes. For UPLC-TOF-MS/MS analysis, all samples were analyzed using a Nexera X2 UPLC system (Shimadzu, JPN) coupled with an TripleTOF™ 5600+ System (AB SCIEX, USA). Chromatographic conditions were as follows: chromatographic column: Waters 2.1 mm × 100 mm, 1.8 μm HSS T3; column temperature: 45 °C; injection volume: 5.0 μL; flow rate: 0.3 mL/min; Mobile phases were 0.1 % formic acid in water (A) and 0.1 % formic acid in acetonitrile (B). The gradient elution procedure was: 0–5 min, 2 % B; 5–7 min, 2–13 % B; 7–21 min, 13–21 % B; 21–23 min, 21–30 % B; 23–28 min, 30–100 % B; 28–30 min, 100 % B. Mass spectrometry conditions: data collection was conducted using data independent acquisition (DIA). Electrospray ion source; TOF-MS scanning range: m/z 100–1200; TOF-MS/MS scanning range: m/z 50–1200; scanning modes: positive/negative ion modes. Spray voltage and cone bore voltage for positive ion mode were 5.5 kV and 80 V, respectively; for negative ion mode, they were − 4.5 kV and − 80, respectively. The nebulizing, curtain, and auxiliary gas flow rates were set to 50 L/min, 35 L/min, and 50 L/min, respectively. Collision energy was set to ±35 eV with a 15 eV extension for MS/MS. TOF-MS and TOF-MS/MS accumulation times were both 150 ms. Each analysis was preceded by accurate mass calibration using an automated system.

2.6.3. UPLC-TOF-MS/MS data preprocessing and metabolites identification

Raw data acquired from the UPLC-TOF-MS/MS system were processed by Progenesis QI v2.3 from Waters Corporation (USA), a widely recognized metabolomics analysis platform. The preprocessing workflow consisted of several critical steps: (1) baseline correction to eliminate instrumental noise and enhance spectral quality; (2) automated peak detection and integration to quantify metabolite abundances based on peak areas; (3) retention time alignment to compensate for minor chromatographic variations between samples; and (4) peak matching across all samples to enable reliable comparative analyses. To account for potential technical variations, the data were normalized using total ion current (TIC) normalization, which scales the intensity of each feature relative to the overall signal intensity of the sample.

For metabolites identification, we established stringent mass accuracy criteria: a precursor ion mass tolerance of 5 ppm and a product ion tolerance of 10 ppm, with a minimum product ion intensity threshold of 5 %. Compound annotation was performed by querying high-resolution mass spectra against major public metabolomics databases, such as the Human Metabolome Database (HMDB), METLIN, and LipidMaps (v2.3), and supplemented with a self-generated library of known tea polyphenol metabolites. To maximize identification confidence, we implemented a multi-dimensional validation strategy: (i) exact mass matching (EMM) with a mass error threshold of <10 ppm; (ii) fragment ions matching (FIM), where experimental MS/MS spectra were compared to reference spectra to confirm structural assignments; and (iii) isotopic distribution analysis (IDA), which verified the observed isotopic patterns against theoretical simulations to distinguish isobaric compounds (Peng et al., 2022). This integrated approach significantly improved the reliability of metabolites identification.

Following data preprocessing, the positive and negative ionization mode datasets were systematically integrated into a comprehensive data matrix, and then subjected to multivariate statistical analysis using SIMCA 14.1 (Sartorius Stedim Data Analytics, Umeå, Sweden), including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) (Ren et al., 2023). Differential metabolite screening employed a dual-threshold approach, selecting features that simultaneously exhibited: (i) variable importance in projection (VIP) scores >1.5, indicating substantial contribution to group discrimination in the PLS-DA model, and (ii) statistically significant differences (p < 0.05) as determined by t-test. For EGCG-derived different metabolites, identification followed a rigorous workflow: a. Differential screening: Only metabolites with ≥100-fold higher abundance in the group JE (EGCG + E. cristatum) compared to the group J (no EGCG) (VIP > 1.5, p < 0.05) were retained. b. Exclusion of medium interference: Metabolites detected in the group J (without EGCG) were removed to eliminate background signals unrelated to EGCG metabolism. c. Structural annotation: Metabolites were annotated using public databases (HMDB, METLIN) and in-house libraries, focusing on structural features matching EGCG (e.g., galloyl groups, flavan-3-ol backbone) or known biotransformation products (e.g., hydrolysis, methylation, sulfation). d. Quantification of key intermediates: Critical metabolites (e.g., EGC, GA) were quantified using HPLC.

2.7. Determination of antioxidant activity

Antioxidant activity was evaluated using multiple assays. Reducing power (RP), DPPH and ABTS radical scavenging activity were measured following our previous procedure of Chen et al. (2020), using vitamin C (VC) for calibration and reporting results as μg VC equivalents/mL fermentation broth (μg VCE/mL). Additionally, the ferric reducing antioxidant power (FRAP) assay was performed as described by our earlier reported method (Huang et al., 2023), with results quantified as μmol Fe (II) equivalents/mL fermentation broth (μmol Fe (II)/mL).

2.8. Statistical analysis

All data were represented as the mean ± standard deviation of three repeated measurements. Statistical analysis was performed using SPSS 23.0 from SPSS Inc. (USA) and other relevant software. Significance was defined at p < 0.05. Data graphs were created using Origin 2021 from OriginLab (USA) and GraphPad Prism from GraphPad Software (USA).

3. Results and discussion

3.1. Fermentation of EGCG by E. cristatum alters the content of TPC and TFC

Polyphenols and flavonoids are key components contributing to the color, aroma, taste, and health benefits of tea (Chen, Peng, Xie, et al., 2023). To accurately assess changes in TPC and TFC during EGCG transformation, interference from the culture medium must be eliminated. The yellow line in the figure shows the trend of TPC and TFC after eliminating the medium's interference. As illustrated in Fig. 2A, TPC reached its maximum concentration of 1.71 mg GAE/mL at day 6, demonstrating a 40.16 % increase relative to baseline levels (day 0). Following this peak, TPC decreased to 1.24 mg GAE/mL by day 12, reflecting a 27.49 % drop from the peak level. However, TPC greatly increased again to 1.53 mg GAE/mL (a 23.39 % increase) by day 24. Meanwhile, as shown in Fig. 2B, TFC consistently increased throughout fermentation process, reaching 1.05 mg RT/mL by day 24, a 3.2-fold increase than day 0. These results demonstrated that liquid-state fermentation of EGCG with E. cristatum significantly enhances both TPC and TFC levels.

Fig. 2.

Temporal variations in (A) total phenolics content (TPC) and (B) total flavonoids content (TFC) during biotransformation of EGCG by E. cristatum.

The increase in TPC during the initial 0–6 days of fermentation is attributed to enzymes like tannase, lipase, and catalase secreted by E. cristatum. These enzymes hydrolyze the ester bonds of ester-type catechins (e.g., EGCG), gallates, and complex tannins in FBT, yielding structurally simplified catechins, GA, and more hydroxylated phenolic compounds (Xiao, He, et al., 2022; Guo et al., 2024). The decrease in TPC from the 6th to the 12th day of fermentation may be due to relatively insufficient polysaccharide and protein content. E. cristatum utilizes certain polyphenols as carbon and nitrogen sources for growth and proliferation (Chen, Wang, Zheng, et al., 2023). Extracellular enzymes from E. cristatium mediate the degradation, transformation, and oxidation of EGCG (Xiao et al., 2021). The increase in TPC from day 12 to 24 is likely due to the production of phenolic hydroxyl-containing compounds by E. cristatum. Throughout the fermentation process, TFC consistently increased, which might be due to the production of flavonoids from EGCG biotransformation by E. cristatum (Huang et al., 2023). However, further UPLC-TOF-MS/MS analysis is needed to identify the specific compounds.

3.2. Enhancement of E. cristatum growth by EGCG addition through fermentation system modification

Previous study also reported that phenolic compounds such as EGCG could influence the growth of filamentous fungus. To evaluate the effect of EGCG on the growth of E. cristatum, key parameters including mycelial biomass, pH of the fermentation broth, soluble sugar and protein concentrations were studied during fermentation. As shown in Fig. 1A, the mycelial biomass increased consistently over the fermentation period, both with and without the addition of EGCG. Notably, the mycelial biomass in group JE (supplemented with EGCG) was significantly higher compared to the respective fermentation time of group J (without EGCG). After 24 days, the mycelial biomass in group JE increased by approximately 61.54 % compared to group J. This finding suggests that the addition of EGCG enhances the growth of E. cristatum.

Fig. 1.

Dynamic profiling of fermentation parameters during biotransformation of EGCG by E. cristatum. (A) Mycelial biomass, (B) pH value, (C) Soluble protein content, and (D) Soluble sugar content. Values represent mean ± SD (n = 3). Note: Group J (control without EGCG), Group JE (with EGCG).

It is well illustrated that pH is a crucial parameter in fermentation, significantly influencing microbial growth and metabolic activity. Moreover, its dynamic variation provides valuable insights into metabolic changes within the fermentation system (García Mendez et al., 2023). As shown in Fig. 1B, the pH of group J increased initially and remained stable throughout the fermentation period, while the pH of group JE decreased initially and then gradually increased. After 6 days of fermentation, the pH of group JE dropped to 5.01. This was 0.45 lower than at day 0 and 0.69 lower than the group J at the same time. Since pH 5.0 is the optimal growth condition for E. cristatum, this favorable acidity level might boost both its enzymatic activity and biomass production. The decrease in pH is likely attributed to the biotransformation of EGCG into GA and other acidic metabolites by E. cristatum. After 6 days of fermentation, the pH of both groups continued to rise, potentially due to E. cristatum producing alkaline substances, consistent with findings by Xiao, He, et al. (2022).

The ratio of carbon and nitrogen (C/N) sources is crucial nutrients for microbial growth and significantly influences microbial growth (Fallahi et al., 2021). As shown in Fig. 1C and Fig. 1D, soluble protein and sugar levels progressively decreased during fermentation in both groups J and JE. However, group JE consistently exhibited markedly lower soluble protein and sugar levels compared to group J. The addition of EGCG likely modulated the C/N ratio in the fermentation system, establishing an optimized microenvironment that enhanced substrate utilization efficiency and consequently promoted the growth and proliferation of E. cristatum. Additionally, previous studies indicate that nitrogen supplementation promotes GA and non-ester catechins degradation through upregulated monooxygenase activity (Fang et al., 2019).

Liu, Wang, et al. (2022) examined high EGCG concentrations' effects on the growth of Aspergillus niger and also found that EGCG stimulates colony growth, mycelial differentiation, and spore germination for A. niger RAF106. They reported that EGCG promotes fungal growth by inhibiting the aggregation of conidia, affecting the ultrastructure of hyphae, and regulating the Slt2 MAPK pathway and Fus3 MAPK pathway in A. niger RAF106. These effects may be attributed to EGCG-induced activation of the Cdc42-Ste11-Ste50-Ste7-Fus3 signaling pathway in A. niger (Liu, Zhou, et al., 2023). Additionally, E. cristatum can convert EGCG into metabolites such as GA, which exhibit improved bioavailability or superior chemical stability. This conversion may potentially confer a competitive edge in fungal growth (Du, Fang, Liao, Fang, & Wang, 2020). These findings confirmed that EGCG's ability to enhance fungal growth to a certain extent.

3.3. Biotransformation of EGCG into EGC and GA by E. cristatum

Catechins are a major component of tea polyphenols, contributing approximately 70 % to the TPC (Liu, Gan, et al., 2023). The decline in tea catechins content during the late stages of dark tea fermentation is primarily due to catechins oxidation (Shi et al., 2023). As shown in Fig. 3, EGCG concentration experienced a sharp decline during the initial 6-day fermentation period. The residual EGCG was then continuously metabolized and degraded by E. cristatum throughout subsequent fermentation. In contrast, the EGCG metabolites EGC and GA first increased to peak levels by day 6, then gradually decreased. This dynamic profile aligns well with established metabolic patterns reported in previous study (Xiao, He, et al., 2022). Furthermore, our earlier study have found that EGCG remains stable in PDA medium after 24 days of incubation under identical culture conditions, with no detectable EGC or GA formation. This confirms the relative stability of EGCG under this experimental condition. These findings indicate that E. cristatum facilitates the biotransformation of EGCG into EGC, GA, and other small molecules.

Fig. 3.

Metabolic fate of EGCG during E. cristatum fermentation, tracking the concentrations of EGCG and its degradation products EGC and GA. Phase I (0–6 days): Rapid EGCG degradation accompanied by EGC and GA accumulation. Phase II (6–18 d): Enzymatic processing of residual EGCG and sequential catabolism of primary metabolites via extracellular enzyme systems.

The biotransformation of catechins during EGCG fermentation proceeded in two distinct phases. In the initial phase (0–6 days), rapid EGCG degradation occurred, yielding non-esterified EGC and GA. This process was primarily driven by extracellular enzymes (e.g., tannase, catalase, polyphenol oxidase, and cellulase) secreted by E. cristatum, which catalyze the hydrolysis of EGCG's ester bonds (Huang et al., 2023; Liu, Shi, et al., 2022). Notably, the breakdown of astringent esterified catechins during this phase plays a key role in modulating the sensory profile of fermented tea (Cheng et al., 2021). During the subsequent phase (6–18 days), residual EGCG, along with the accumulated EGC and GA, underwent further metabolism via hydrolysis, cyclolysis, dehydroxylation, demethylation, decarboxylation, and reduction reactions (Xiao, He, et al., 2022), generating small phenolic acids, B-ring fission products, tea pigments, and other derivatives (Huang et al., 2023; Zhu et al., 2020). Polyphenol oxidase and peroxidase mediated the oxidation of catechins to quinones, which subsequently polymerized with polysaccharides, proteins, and caffeine to form pigments (Long et al., 2023). Furthermore, catechol dioxygenase activity facilitated A-ring modification and B/C-ring oxidation, producing characteristic metabolites such as fuzhuanins, teadenols, and xanthocerinins (Chen, Wang, Ye, et al., 2023). Intriguingly, some of these metabolites exhibit enhanced bioactivity compared to their native catechins precursors (Ma et al., 2023; Xiao, He, et al., 2022; Zhu et al., 2020). However, the metabolites derived from catechins biotransformation by E. cristatum in this study require further characterization by UPLC-TOF-MS/MS.

3.4. Changes in hydrolytic enzymes activity during the fermentation of EGCG by E. cristatum

During fermentation, microorganisms release various enzymes that break down nutrients in their environment into usable compounds. These compounds then support the microbes' growth and reproduction (Zhang et al., 2023). E. cristatum was previously reported that secretes enzymes like tannase, polyphenol oxidase, catalase, cellulase, and α-amylase, which are linked to changes in polyphenols such as catechins during FBT fermentation (Du et al., 2022).

Tannases, also known as tannin acyl hydrolases, catalyze the breakdown of gallate ester bonds, gallotannins, and complex tannins (Yang et al., 2023). As shown in Fig. 4A, E. cristatum consistently secreted tannase throughout fermentation, regardless of EGCG addition. Tannase activity showed an initial increase, reaching its maximum level (27.12 U/mL in group JE vs 25.03 U/mL in group J) on day 18 of fermentation, followed by a gradual decline. This reduction in enzymatic activity may be attributed to the progressive aging of the fungal culture in the fermentation medium. Long term excessive fermentation reduces the available carbon and nitrogen sources in the system, leading to inhibition of microbial growth and metabolism. Alternatively, substances formed during fermentation may inhibit E. cristatum's tannase secretion. However, tannase activity was higher in EGCG-containing fermentation broth at the same time point, suggesting that EGCG might stimulate tannase secretion. Further investigation is needed to confirm this mechanism. Liu, Wang, et al. (2022) demonstrated that tannase serves as the pivotal catalytic enzyme mediating EGCG biotransformation during A. niger RAF 106 fermentation. Moreover, Liu, Zhou, et al. (2023) discovered that EGCG also promoted the production of pectinases and cellulases by A. niger RAF 106.

Fig. 4.

Enzyme activity profiles during biotransformation of EGCG by E. cristatum. (A) Tannase, (B) Polyphenol oxidase, (C) Cellulase, (D) α-Amylase, and (E) Catalase.

Polyphenol oxidase is a class of copper-containing proteins, including laccase, tyrosinase, and catechol oxidase (Sui et al., 2023). In microorganisms, laccase is the predominant polyphenol oxidase, playing a vital role in FBT production by oxidizing catechins into theaflavins, thearubigins, and other compounds (Du et al., 2022; Xiao, He, et al., 2022). As illustrated in Fig. 4B, polyphenol oxidase activity followed a similar trend to tannase activity, increasing initially and then decreasing, with both peaking at day 18. At this peak, group JE exhibited an activity of 10.20 U/mL, while group J had 9.62 U/mL. In the later stage of fermentation, the decrease in polyphenol oxidase activity is related to the reduction in the number of E. cristatum spores. This result aligns with Liu, Shi, et al. (2022), who reported similar trends in polyphenol oxidase activity during tea fermentation with E. cristatum.

Cellulase, a crucial hydrolytic enzyme produced by E. cristatum (Deng et al., 2023), exhibited a characteristic activity pattern during fermentation, with an initial increase followed by gradual decline in both groups J and JE (Fig. 4C). Group JE exhibited higher cellulase activity than group J during the first 18 days of fermentation, but the underlying mechanism remains to be elucidated. Chiang et al. (2022) reported a dynamic pattern of cellulase activity during black tea fermentation, characterized by an initial increase followed by a gradual decline, which aligns well with our current observations. This biphasic trend could potentially be attributed to the progressive depletion of available substrates throughout the fermentation process.

α-Amylase and catalase are also important hydrolytic enzymes secreted by E. cristatum during fermentation (Deng et al., 2023). As shown in Fig. 4D and E, α-amylase and catalase activities in group JE were lower than in group J during early fermentation. This phenomenon can likely be caused by the incomplete degradation of EGCG during the early fermentation stage, where its presence suppresses the secretion of these two enzymes by E. cristatum. Previous studies have shown that EGCG inhibits α-amylase activity primarily by significantly decreasing its maximal reaction velocity (Vmax), while having minimal impact on the Michaelis-Menten constant (Km) (Xu et al., 2020). However, the mechanisms underlying EGCG-mediated inhibition of catalase in E. cristatum remain to be further elucidated.

Collectively, E. cristatum facilitates the biotransformation of EGCG during fermentation by secreting various extracellular enzymes, including tannase, polyphenol oxidase, peroxidase, cellulase, and α-amylase. Conversely, EGCG exhibits inhibitory effects on the secretion of certain extracellular enzymes by E. cristatum. However, the underlying mechanisms of these interactions remain poorly understood and warrant further investigation.

3.5. UPLC-TOF-MS/MS untargeted metabolomics analysis of EGCG metabolites via E. cristatum fermentation

3.5.1. Multivariate statistical analysis

Untargeted metabolomics techniques were used to investigate metabolites change in groups J and JE at various fermentation time points, aiming to elucidate metabolic patterns during EGCG fermentation by E. cristatum. The total ion chromatogram (TIC) of samples from groups J and JE were shown in Fig. S1. PCA plots for each time point revealed distinct separations among samples from different fermentation stages, with all data points within the 95% confidence ellipse. Additionally, the three parallel samples at each time point were closely clustered, demonstrating experimental stability and reproducibility. In the ESI+ mode, PC1 and PC2 captured 43.9% and 19.3% of the total variance, respectively (Fig. 5A). Comparable outcomes were noted in ESI- mode (Fig. 5B), indicating that the metabolic profile is highly responsive to the fermentation process. Samples within each group were tightly clustered, while those from different groups were distinctly separated. This suggests no significant differences among samples at the same time point but substantial variations across different time points. However, PCA alone cannot fully explain these differences. To further investigate metabolites transformation, PLS-DA was employed. As shown in Fig. 5C and D, the PLS-DA scoring plots were derived from positive and negative ion mode model parameters. In positive mode, principal component [1] accounted for 43.9% and principal component [2] for 19.3%. In negative mode, principal component [1] accounted for 40.0% and principal component [2] for 21.5%. These findings indicate that PLS-DA provides better efficacy and predictive power than PCA, highlighting substantial changes in secondary metabolite levels during EGCG fermentation.

Fig. 5.

Multivariate analysis results of metabolic profiles. Principal component analysis (PCA) score plots in (A) positive and (B) negative ion modes. Partial least squares-discriminant analysis (PLS-DA) models in (C) positive and (D) negative ion modes.

3.5.2. Screening for differential metabolites

PLS-DA was also applied to differentiate the metabolic profiles between groups J and JE during fermentation. Significant differential metabolites were identified using thresholds of VIP ≥ 1 and p < 0.05. Notably, metabolites displaying at least a 100-fold higher abundance in group JE compared to group J were selected for further analysis of EGCG-related metabolites. To reduce potential matrix interference, metabolites detected in the control group (without EGCG treatment) were systematically excluded. Subsequently, structural annotation was performed using HMDB, METLIN, and in-house libraries, with particular focus on characteristic EGCG-derived moieties (e.g., galloyl groups, flavan-3-ol backbone) and common biotransformation products (e.g., hydrolyzed, methylated, or sulfated derivatives). Preliminary screening yielded a total of 128 differential metabolites potentially associated with EGCG metabolism, and their detail information is shown in the Supplementary Table S1. For instance, the compound eluting at 22.4212 min (RT) exhibited a molecular formula of C15H14O9S, with an accurate mass of 739.0661674 in ESI-negative mode. MS/MS analysis revealed characteristic fragment ions at m/z 439.0347 and 299.0413. Based on the tandem mass spectral data and comparison with prior literature, this compound was unequivocally identified as catechin 3′-sulfate. Moreover, in the negative-ion mode, a compound with the molecular formula C44H34O22 exhibited an accurate mass of 915.1569478 and produced characteristic product ions at m/z 897.2349 and 416.9315 in MS/MS analysis. Based on its mass spectrometric fragmentation pattern, accurate mass data, and structural alignment with known compounds in its molecular family, this metabolite was tentatively identified as theasinensin A. These differential metabolites were normalized via z-score and visualized in a clustered heatmap (Fig. 6). The color gradient from red to blue indicates the relative abundance of metabolites, with red and blue representing high and low levels, respectively. Each row represents a single metabolite, and each column an individual sample. As shown in Fig. 6, these metabolites consistently exhibited low levels at all-time points in the fermentation broth without EGCG (group J). In contrast, their concentrations were significantly higher at one or more time points in the EGCG-supplemented group (group JE), suggesting a strong association with metabolites derived from E. cristatum-mediated EGCG transformation. For example, the galloyl moiety of EGCG is hydrolyzed by esterase, resulting in the cleavage of the ester bond and the release of GA and EGC. GA is then decarboxylated by galic acid decarboxylase to form pyrogallol, a trihydroxyphenol known for its potent antioxidant activity. Pyrogallol is subsequently oxidized to ortho-quinone by polyphenol oxidase. This unstable intermediate readily polymerizes to form high-molecular-weight theabrownins, which are complex polymeric compounds that contribute to the characteristic color and flavor profiles of fermented teas, such as dark tea (Liu, Boeren, et al., 2022). Fig. 7 illustrates the principal potential metabolic pathways involved in E. cristatum-mediated biotransformation of EGCG.

Fig. 6.

Cluster heat-map results of EGCG-derived metabolites. Color gradient (red to blue) indicates relative metabolite abundance. Rows represent individual metabolites; columns correspond to fermentation time points. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7.

Proposed biotransformation pathway of EGCG by E. cristatum. The schematic outlines the stepwise degradation of EGCG, involving hydrolysis, oxidation, decarboxylation, and polymerization, ultimately generating structurally diverse phenolic metabolites.

The production of EGCG metabolites is influenced by multiple factors, including molecular interactions, microbial metabolism, and genetic regulation. As demonstrated by Nassarawa et al. (2023), EGCG engages in both non-covalent and covalent interactions with proteins and other biomolecules, altering their functional properties. When supplemented into culture media, EGCG modifies the medium composition, leading to divergent metabolic conversion pathways in E. cristatum compared to unsupplemented control. Additionally, evidence indicates that EGCG may regulate gene expression in E. cristatum, further shaping its metabolic activity. Notably, higher EGCG concentrations exert great effect on the growth, sporulation, and extracellular enzyme secretion E. cristatum, ultimately resulting in distinct metabolite profiles between groups J (control) and JE (EGCG-supplemented). Previous study of Liu, Zhou, et al. (2023) also observed that EGCG co-culture not only enhanced cellulase and pectinase activities in A. niger but also differentially regulated 1394 genes, including 37 genes linked to fungal growth and carbohydrate metabolism. This reported finding aligns well with our experimental observations and establishes a theoretical foundation for further investigation.

Among 128 metabolites, 87 flavonoids were identified, including 62 flavonoids, 14 flavonoid glycosides, 3 isoflavones, 2 pyranoid flavonoids, 2 isoflavone glycosides, 2 biflavonoids and polyflavonoids, 1 flavanol, and 1 methoxy-flavonoid. The modification products identified as derivatives of EGCG included theasinensin A, epiafzelechin-(4β → 6)-epicatechin 3,3′-digallate, epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate, 4′-O-Methyl-(−)-epicatechin-5-O-sulphate, epicatechin 5-O-β-D-glucopyranoside-3-benzoate, catechin 3′-sulfate, (−)-epicatechin sulfate, and catechin 3,7,-di-O-galate. The changes in the levels of these metabolites are depicted in Fig. 8. Theasinensin A, a dimeric theaflavin derived from the oxidative coupling of two EGCG molecules, demonstrates multiple bioactive properties including antioxidant, anti-obesity, hypoglycemic, anti-inflammatory, and cardiovascular protective effects (Xu et al., 2023). During fermentation, its concentration peaked on day 18, showing a 3.99-fold increase compared to baseline levels (day 0). The enhanced formation of theasinensin A may be attributed to the robust extracellular enzymes secretion by E. cristatum, which facilitates the oxidative conversion process (Xiao et al., 2022, Xiao et al., 2023; Xiao, He, et al., 2022). These findings collectively demonstrate that E. cristatum mediates the biotransformation of catechins (e.g., EGCG) into theaflavin-type compounds. Thonningianin A, an ellagitannin derivative and one of the key metabolites derived from EGCG, demonstrates significant antioxidant activity through multiple synergistic mechanisms. The compound demonstrates potent free radical scavenging activity, particularly against DPPH radicals, and effectively suppresses superoxide anion production. Furthermore, thonningianin A exhibits strong transition metal chelating capacity, primarily for iron and copper ions, which contributes to its enhanced antioxidant efficacy and cellular protection against oxidative stress (Gyamfi & Aniya, 2002).

Fig. 8.

Comparative profiling of major EGCG transformation products generated by E. cristatum. (A)-(H) are epiafzelechin-(4β → 6)-epicatechin 3,3′-digallate, epicatechin 5-O-β-D-glucopyranoside-3-benzoate, catechin 3′-sulfate, (−)-epicatechin sulfate, 4′-O-methyl-(−)-epicatechin-5-O-sulphate, epiafzelechin-(4b → 8)-epicatechin 3,3′-digallate, catechin 3,7-di-O-galate, theasinensin A, and thonningianin A, respectively. Significance levels: *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

The metabolites epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate and epiafzelechin-(4β → 6)-epicatechin 3,3′-digallate are polymerization products derived from EGCG and other constituents. Both compounds reached their peak concentrations on day 18 of fermentation, demonstrating over 1000-fold increases compared to day 0. These substances exhibit potent antioxidant activity, suggesting that catechins polymerization during fermentation contributes significantly to the health-promoting properties of tea (De la Luz Cádiz-Gurrea et al., 2014). Simultaneously, sulfate-modified catechins including catechin 3′-sulfate, (−)-epicatechin sulfate, and 4′-O-methyl-(−)-epicatechin-5-O-sulfate were generated through the sulfation pathway mediated by E. cristatum. Notably, 4′-O-methyl-(−)-epicatechin-5-O-sulfate peaked on day 6 with a remarkable 10,000-fold increase from baseline, indicating E. cristatum's capacity to facilitate both methylation and sulfation of catechins. Catechin 3′-sulfate and (−)-epicatechin sulfate reached maximal levels on days 12 and 18, respectively, each showing >1000-fold increases. Furthermore, epicatechin 5-O-β-D-glucopyranoside-3-benzoate, which peaked on day 12 with a 1000-fold increase, demonstrated E. cristatum's ability to perform glycosylation modifications on EGCG. These findings collectively reveal that E. cristatum mediates multiple biotransformation pathways (sulfation, methylation, and glycosylation) of EGCG. The methylation modification enhances catechins stability and bioavailability (Duenas et al., 2010), while sulfation increases molecular polarity and water solubility, thereby improving radical scavenging efficiency (Winiarska-Mieczan et al., 2021). Glycosylation modifications improve metabolite solubility, stability, and bioavailability (Chen et al., 2022), with glycosidic bond formation playing a crucial role in developing the characteristic flavor profile and unique bioactivities of fermented tea (Plaza et al., 2014). Therefore, various metabolites products of EGCG by E. cristatum may contribute to the excellent bioactivity, bioavailability and unique quality properties of FBT.

3.6. Changes in antioxidant activity during the fermentation of EGCG by E. cristatum

During cultivation in liquid PDA medium, E. cristatum metabolically transforms medium components, leading to dynamic changes in antioxidant activity. To accurately evaluate the antioxidant contributions from EGCG and its metabolites during E. cristatum-mediated biotransformation, the baseline antioxidant activity of PDA medium must be subtracted from measured values. As shown in Fig. 9, all measured antioxidant indices — including ABTS·⁺ and DPPH radical scavenging activities, FRAP, and RP — demonstrated a characteristic fluctuation pattern during EGCG transformation: an initial increase followed by a decrease and subsequent rebound. Notably, ABTS·⁺ and DPPH radical scavenging activities peaked on fermentation day 12, showing significant increases of 31.95% and 40.20%, respectively, compared to initial levels (day 0). In contrast, FRAP and RP reached maximal values earlier, on day 6, with more pronounced enhancements of 77.09% and 44.84%, respectively. These observations are consistent with previous findings by Huang et al. (2023), who reported comparable increases in antioxidant capacity (32.71% for DPPH, 30.97% for ABTS, and 46.58% for FRAP) during E. cristatum-fermented dark tea production. The temporal variations in antioxidant indices reflect stage-dependent production of diverse metabolites with differing antioxidant capacities by E. cristatum during EGCG fermentation. Importantly, these antioxidant activity changes might closely correlate with the differential metabolites identified in prior analyses.

Fig. 9.

Temporal changes in antioxidant activity during EGCG biotransformation by E. cristatum. (A) ABTS radical scavenging capacity, (B) DPPH radical scavenging activity, (C) Ferric reducing antioxidant power (FRAP), and (D) Reducing power (RP) assay.

3.7. Relationship of antioxidant activity, hydrolase activity, and EGCG metabolites

3.7.1. Hydrolytic enzymes secreted by E. cristatum convert EGCG

Microbial hydrolytic enzymes play a crucial role in EGCG biotransformation (Huang et al., 2023). As shown in Fig. 10A, all transformed metabolites demonstrated significant positive correlations (p < 0.05) with hydrolase activity, with the exception of catechin 3,7-di-O-galate, which displayed a negative correlation. Moreover, the activities of tannase, polyphenol oxidase, catalase, and cellulase were positively associated with changes in both TPC and TFC. Notably, alterations in TFC showed particularly strong positive correlations with polyphenol oxidase and catalase activities (p < 0.05). Liu, Zhou, et al. (2023) provided compelling evidence that EGCG significantly up-regulated both pectinase and cellulase activities while modulating the expression of carbohydrate metabolism-related genes of A. niger. Genomic analysis by Chen, Wang, Zheng, et al. (2023) revealed that E. cristatum possesses an extensive repertoire of carbohydrate-active enzyme encoding genes, particularly gene clusters for cellulases, proteases, oxidases, and lipases, which directly correlate with its remarkable enzymatic production capacity during fermentation. Notably, its extensive oxidase gene repertoire facilitates polyphenol oxidation and may consequently augment antioxidant potential. These results establish that E. cristatum promotes EGCG biotransformation through secreted hydrolytic enzymes, mediating three key modifications — methylation, glycosylation, and sulfation — which collectively yield diverse structural derivatives (Zhang & Zhang, 2024).

Fig. 10.

Multidimensional analysis of EGCG biotransformation by E. cristatum. (A) Chord diagram illustrating interactions between EGCG-derived metabolites and hydrolase activities; (B) Correlation mapping between metabolites and antioxidant activity; (C) Principal component analysis integrating metabolite profiles, antioxidant indices, and enzyme activities.

3.7.2. Correlation analysis between antioxidant activity and EGCG metabolites

The observed changes in antioxidant activity during fermentation likely stem from the diverse transformation products of EGCG. As shown in Fig. 10B, one or more antioxidant indices showed positive correlations with EGC, GA, 4′-O-methyl-(−)-epicatechin-5-O-sulphate, catechin 3′-sulfate, epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate, epicatechin 5-O-β-D-glucopyranoside-3-benzoate, as well as TPC and TFC. Notably, FRAP and RP values exhibited significant positive correlations with ECG, whereas all four antioxidant indices demonstrated strong positive associations with GA. Huang et al. (2023) established that the enhanced antioxidant activities measured by DPPH, ABTS·⁺, and FRAP assays were attributable to increased production of non-ester catechins and GA during E. cristatum-mediated dark tea fermentation. Consistent with Yao et al. (2017), higher fermentation temperatures enhanced GA yield in E. cristatum-fermented loose-leaf tea, leading to greater DPPH and FRAP activities. From a structural perspective, EGC, GA, and epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate exhibit multiple phenolic hydroxyl and galloyl moieties that confer potent free radical scavenging capacity through both hydrogen atom transfer (HAT) and single electron transfer (SET) mechanisms. Specifically, GA, containing three phenolic hydroxyl groups, demonstrates remarkable antioxidant activity by neutralizing reactive oxygen species (ROS), terminating free radical chain reactions, and protecting biomolecules (lipids, DNA, proteins, and redox enzymes) from oxidative damage (Zahrani et al., 2020). Similarly, the non-ester catechins EGC (with five phenolic hydroxyls) exerts dual antioxidant effects: (1) HAT-mediated radical quenching through phenolic hydrogen donation, forming stable phenolic radicals, and (2) SET-driven radical reduction via direct electron transfer (Ambigaipalan et al., 2020; Liu et al., 2024).

Additionally, both ABTS·⁺ and DPPH were positively correlated with several structural derivatives of EGCG, particularly catechin 3′-sulfate, epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate, and epicatechin 5-O-β-D-glucopyranoside-3-benzoate. Although sulfation modifications in 4’-O-methyl-(−)-epicatechin-5-O-sulfate and catechin 3′-sulfate decreased their phenolic hydroxyl content and reaction activity, these compounds maintained partly antioxidant potential. These findings collectively account for the robust antioxidant properties observed among EGCG transformation products. Notably, both radical scavenging capacities showed significant positive correlations with epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate (p < 0.05), whereas ABTS·⁺ scavenging activity was specifically associated with epicatechin 5-O-β-D-glucopyranoside-3-benzoate (p < 0.05). Previous studies have demonstrated that enzymatic acylation of EGCG using Novozym 435 lipase enhances its DPPH radical scavenging capacity compared to native EGCG, suggesting that structural modifications can improve antioxidant efficacy (Mardani et al., 2024). Similarly, biotransformation of tea polyphenol mixtures has been reported to increase antioxidant activity, likely due to the conversion of catechins (e.g., EGCG and EGC) into novel bioactive derivatives (Wang, Li, et al., 2023). Collectively, these findings indicate that the enhanced antioxidant effects observed during fermentation may result from structural modifications of EGCG and the formation of new biotransformation products.

3.7.3. Principal component analysis of antioxidant activity, hydrolase activities and EGCG transformation products

To elucidate the interrelationships among hydrolase activities, EGCG metabolites, and their corresponding effects on antioxidant capacity, principal component analysis (PCA) was performed. As shown in Fig. 10C, the first two principal components accounted for 97.2% and 2.15% of the total variance, respectively, demonstrating their effectiveness in representing the original dataset. The PCA loading plot not only reveals the correlation between principal components and original variables, but also reflects the relationships among the investigated parameters. The spatial proximity of different indicators in the loading plot suggests strong intercorrelations. As illustrated in Fig. 10C, several EGCG metabolites (EGC, GA, epiafzelechin-(4β → 6)-epicatechin 3,3′-digallate, catechin 3,7-di-O-gallate, 4′-O-methyl-(−)-epicatechin-5-O-sulfate, (−)-epicatechin sulfate, catechin 3′-sulfate, theasinensin A, epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate, and epicatechin 5-O-β-D-glucopyranoside-3-benzoate) clustered closely with hydrolase activities (tannase, polyphenol oxidase, catalase, cellulase, and α-amylase) and FRAP values along PC1. This clustering pattern indicates that these hydrolases are strongly associated with the formation of EGCG metabolites, which collectively contribute significantly to FRAP variations. Along PC2, catechin 3′-sulfate, theasinensin A, epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate, and epicatechin 5-O-β-D-glucopyranoside-3-benzoate exhibited similar loading patterns with ABTS·⁺ and DPPH radical scavenging capacities, suggesting strong positive correlations between these specific metabolites and antioxidant activities. These PCA findings were consistent with the results obtained from Pearson correlation analysis (Fig. 10B).

Liu, Zhou, et al. (2023) investigated the biotransformation of EGCG through liquid-state fermentation using A. niger RAF106. Their findings revealed that EGCG could be metabolized into multiple derivatives, including GA, EGC, (−)-epigallocatechin 3,5-di-gallate, (−)-epigallocatechin 3-(3-methyl-gallate), (−)-catechin 3-O-gallate, 4′-methyl-(−)-epigallocatechin 3-(4-methyl-gallate), myricetin, prodelphinidin B, 7-galloylcatechin, and 3-hydroxyphenylacetic acid. Concurrently, a significant alteration in DPPH radical scavenging capacity was observed. These results demonstrate that microbial metabolism of EGCG modulates its antioxidant activity, though the metabolic profiles vary across microbial species. Thus, E. cristatum secretes hydrolytic enzymes such as tannase, polyphenol oxidase, catalase, cellulase, and α-amylase, which collectively catalyze the bioconversion of EGCG into diverse antioxidant compounds. Consequently, the antioxidant activity is influenced by the concentration and structure of these transformation metabolites. However, further research is needed on the biosynthetic pathways of key metabolites of EGCG and the differentially expressed genes encoding hydrolytic enzymes in E. cristatum.

4. Conclusion

This study elucidates the targeted biotransformation of EGCG by E. cristatum during liquid-state fermentation, establishing an innovative platform for tea polyphenol modification. The fermentation process remarkably enhanced the contents of total phenolics (40.16% increase) and flavonoids (3.2-fold increase), which was mediated by a fungal enzymatic system comprising tannase, polyphenol oxidase, catalase, cellulase, and α-amylase. These enzymes catalyzed the conversion of EGCG into gallic acid, epigallocatechin, and structurally diverse derivatives. Metabolomic analysis identified 128 differential metabolites, among which nine major EGCG-modified products were characterized (i.e., epiafzelechin-(4β → 6)-epicatechin 3,3′-digallate, epiafzelechin-(4β → 8)-epicatechin 3,3′-digallate, (−)-epicatechin sulfate, epicatechin 5-O-β-D-glucopyranoside-3-benzoate, 4′-O-methyl-(−)-epicatechin-5-O-sulfate, theasinensin A, thonningianin A, catechin 3′-sulfate, and catechin 3,7-di-O-gallate). These metabolites included sulfated/galloylated/glycosylated derivatives, which expand the known spectrum of EGCG metabolites and underscore their role in fermented tea quality. Notably, E. cristatum-mediated EGCG biotransformation significantly enhanced antioxidant capacity, which exhibited strong correlation with the generated EGCG metabolites. Intriguingly, EGCG served a dual role: as a substrate for bioconversion and as a growth regulator, increasing E. cristatum biomass by 61.54% via pH and C/N ratio modulation. These findings elucidate the enzymatic machinery and metabolic pathways underpinning EGCG biotransformation while highlighting a potential catechins-mediated feedback loop influencing fungal proliferation. To further advance this work, we propose a multi-omics approach combining transcriptomics (RNA-seq) and bioinformatics-driven pathway analysis to delineate stage-specific gene expression patterns, particularly enzymes governing EGCG derivative biosynthesis. While this study elucidates the metabolic outcomes of E. cristatum fermentation, future research should focus on molecular mechanisms—such as gene-regulatory networks and enzyme kinetics—to fully exploit the biotechnological potential of fungal-polyphenol interactions for fermented tea quality enhancement.

CRediT authorship contribution statement

Ziyi Fan: Writing – original draft, Validation, Methodology, Investigation, Data curation. Qing Qing: Writing – original draft, Investigation, Data curation, Software. Jiaquan Yin: Investigation. Cheng He: Methodology, Investigation. Yufei Hu: Investigation. Yulian Chen: Writing – original draft, Investigation, Formal analysis, Data curation, Methodology. Youhua Ren: Investigation. Mingzhi Zhu: Project administration, Formal analysis, Resources. Zhonghua Liu: Resources, Project administration, Conceptualization. Xiaozhen Peng: Writing – review & editing, Project administration, Formal analysis, Resources, Visualization. Yu Xiao: Writing – review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Formal analysis, Investigation, Resources, Writing – original draft.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability

Data will be made available on request.