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. 2024 Dec 16;89(12):10134–10151. doi: 10.1111/1750-3841.17609

Screening study of hydroxytyrosol metabolites from in vitro fecal fermentation and their interaction with intestinal barrier repair receptor AhR

Yuqing Song 1, Mengting Li 1, Jingle Liu 1, Juan Wang 1, Aimei Zhou 1, Yong Cao 1, Shan Duan 1, Qun Wang 1,✉
PMCID: PMC11673453  PMID: 39686652

Abstract

Olive oil polyphenol hydroxytyrosol (HT) significantly repairs intestinal barrier function, but its absorption in the stomach and small intestine is limited. The metabolites of unabsorbed HT that reach the colon are crucial, yet their effects on colonic microbiota and intestinal barrier repair remain unclear. This study utilized in vitro simulated digestion and colonic fecal fermentation to investigate HT's digestion and fermentation. Results indicated that 79.25% of HT potentially reached the colon intact. Further 16S rDNA, targeted, and untargeted metabolomics analyses showed that HT can be decomposed by colonic microbiota, producing aromatic hydrocarbon metabolites and regulating gut microbiota structure. It promotes the growth of gut microbiota, such as Bacteroides, Faecalibacterium, Klebsiella, and Lachnospira, which degrade HT. Additionally, HT's intervention conversely affected the production of tryptophan‐derived metabolites and short‐chain fatty acids (SCFAs). Subsequently, computer‐simulated molecular docking technology was used to simulate the binding affinity between HT metabolites and derived metabolites and the intestinal barrier repair‐related receptor aryl hydrocarbon receptor (AhR). Indole‐3‐acetic acid, indole‐3‐acetaldehyde, skatole, kynurenine, and homovanillic acid could tightly bind to the amino acid residues of the AhR receptor, with binding energies all ˂−6.0 kcal/mol, suggesting that these metabolites may enhance the intestinal barrier function through the AhR signaling pathway.

Keywords: Ahr, fermentation, hydroxytyrosol, intestinal microbiota, metabolites

1. INTRODUCTION

A damaged intestinal barrier can lead to increased gut permeability, disrupting the balance of the gut microbiota and negatively affecting immune defense mechanisms (Chelakkot et al., 2018). Therefore, maintaining intestinal barrier function is crucial for preventing diseases, maintaining immune system balance, and promoting overall health. Damage to the intestinal barrier, often caused by inflammatory factors, can lead to increased permeability and further invasion of pathogens into the intestinal lining, exacerbating immune responses and inflammation (El‐Akabawy & El‐Sherif, 2019; Gong et al., 2022; Neurath, 2014; Sharma et al., 2020). The aryl hydrocarbon receptor (AhR), a ligand‐dependent transcription factor, plays an important role in regulating various biological processes such as cell proliferation, apoptosis, and inflammatory responses (Mandavia, 2015; Marlowe et al., 2008; Qiu et al., 2012; Xie et al., 2012). Activation of AhR has been shown to protect the intestinal barrier by modulating key signaling pathways, including myosin light chain kinase‐phosphorylated myosin light chain (MLCK‐pMLC), which helps maintain epithelial integrity under inflammatory conditions. In a dextran sulfate sodium (DSS)‐induced colitis mouse model, administration of the AhR ligand 6‐formylindolo[3, 2‐b]carbazole (FICZ) improved gut permeability and enhanced intestinal barrier function by promoting the protective effects of AhR activation (Han et al., 2016). This suggests that AhR activation plays a key role in supporting intestinal barrier integrity, particularly in inflammatory contexts.

Dietary polyphenols play an important role in promoting the repair of intestinal barrier function and maintaining intestinal health. For example, supplementing four polyphenol compounds (curcumin, quercetin, naringenin, and hesperidin) to mice with DSS‐induced colitis resulted in varying degrees of improvement, but all restored the expression levels of tight junctions (TJs) proteins to some extent. Among them, quercetin protected the intestinal barrier by restoring the expression of occluding; hesperidin and curcumin inhibited DSS‐induced barrier disruption by restoring the expression of the adherens junction molecule‐A and claudin 3; and naringenin protected by the intestinal barrier by restoring the expression levels of occludin and junctional adhesion molecule‐A (JAM‐A) (Chen et al., 2023; Feng et al., 2023; R. Yu et al., 2023; Zhong et al., 2023). Recent research has found that treating colitis mice with urolithin A, a metabolite of pomegranate polyphenols, and its synthetic analogue UAS03, can activate a novel target AhR. Through the AhR‐nuclear factor erythroid 2‐related factor 2 (Nrf2) pathway, they promote the expression levels of TJ proteins, thus enhancing barrier function. AhR‐mediated activation of Nrf2 plays a role in antioxidant defense by protecting the intestinal integrity of patients with colitis (R. Singh et al., 2019). Additionally, a study evaluated the probiotic potential of bread enriched with olive polyphenol fiber using the multi‐unit colon gut model in vitro colon model, combined with microbiomics and metabolomics approaches. The results indicated that bread rich in olive polyphenol fiber can promote the growth of beneficial bacteria, inhibit harmful microbial communities, increase the production of short‐chain and medium‐chain fatty acids, and reduce harmful metabolites, demonstrating its potential to improve gut health (Nissen et al., 2021).

Hydroxytyrosol (HT) is a major phenolic compound in olive oil and the main active metabolite of oleuropein in the body. Previous studies have found that HT can repair intestinal barrier damage by promoting the expression levels of occludin, claudin‐1, and zonula occludens‐1 (ZO‐1). In an HT supplementation group, a significant decrease in the expression of pro‐inflammatory factors was observed, and Nrf2 and hemeoxygenase‐1 were significantly upregulated at the protein level (Wang et al., 2022). HT can effectively alleviate tissue and pathological damage and enhance the intestinal barrier function. However, the effective absorption of HT in the stomach and small intestine is limited, and unabsorbed HT may interact with the colon and intestinal microbiota. The intestinal microbiota converts HT into various metabolites and microbial metabolites, increasing its bioavailability. Currently, the molecular basis for the active effects of HT is not clear. Various metabolites and derived metabolites metabolized by intestinal bacteria are prerequisites for HT to be absorbed by the body and exert biological activity. Therefore, whether a series of metabolites and derived metabolites of HT, through binding to AhR, regulate the transcription and translation of TJ proteins, such as occludin and claudin‐1, thereby promoting their expression and repairing intestinal barrier function, is not yet clear. Clarifying the types and structural characteristics of the metabolites of HT metabolized by colonic bacteria and how the metabolites and microbial metabolites of HT enhance the intestinal barrier function through the AhR signaling pathway is crucial. They are key issues in elucidating the molecular mechanism of HT in repairing intestinal barrier function.

This study first verified the digestion of HT through in vitro simulated gastrointestinal digestion. Furthermore, by simulating colonic fecal fermentation of HT, substances in the fermentation fluid were detected at different fermentation times. QExactive liquid chromatography–tandem mass spectrometry (LC–MS) was used to analyze the metabolic spectrum of HT to clarify the structural characteristics of the metabolites produced during fermentation. Moreover, gas chromatography–mass spectrometry (GC–MS) is used to precisely analyze the content of volatile short‐chain fatty acids (SCFAs) in the fermentation fluid to clarify the interaction between the HT and the intestinal microbiota and the production of metabolites. Additionally, partial least square discriminant analysis (PLS‐DA) and computer simulation molecular docking technology were used to efficiently screen AhR ligands, exploring the possibility that metabolites and derived metabolites of HT enhance the intestinal barrier function through the AhR pathway. This study systematically evaluated the metabolic processes of HT in in vitro simulated gastrointestinal digestion and colonic fecal fermentation. The microbial composition changed during the fermentation of HT, promoting the growth of HT‐degrading bacteria and SCFA‐producing bacteria. Moreover, a positive correlation was observed between the intestinal microbiota and the generation of specific metabolites during HT fermentation. This finding reveals the regulatory effect of HT fermentation on the structure and metabolic function of the intestinal microbiota and indicates that HT metabolites may enhance intestinal barrier function through AhR, providing a theoretical basis for an in‐depth understanding of the role and potential mechanism of HT metabolites in intestinal health.

2. MATERIALS AND METHODS

2.1. Materials and reagents

HT (purity = 98%) was provided by Shanghai Macklin Biochemical Co., Ltd. The high‐performance liquid chromatography (HPLC)‐grade methanol and acetonitrile used for extraction or analysis were purchased from Yongda Chemical Reagent Co., Ltd. HPLC‐grade ethyl acetate was purchased from Pishiluo Supply Chain Co., Ltd. Ethyl acetate was purchased from China National Pharmaceutical Group Corporation. Ultrapure water was prepared using a Milli‐Q water purification system (Millipore). K2HPO4 and KH2PO4 were purchased from Yongda Chemical Reagent Co., Ltd. Peptone was purchased from Ouboxing Biotechnology Co., Ltd. Cysteine was purchased from Shanghai Macklin Biochemical Co., Ltd. Sodium sulfide was purchased from Guangzhou Chemical Reagent Co., Ltd. Brilliant Blue was purchased from Anhui Kuer Biotechnology Co., Ltd. Glycerol was purchased from Huada Reagent. Sodium hydroxide was purchased from Tianjin Damao Chemical Reagent Factory. Hydrochloric acid was purchased from Shanghai Sui Test Company. α‐Amylase was purchased from Shanghai Yuanye Biotechnology Co., Ltd. Pepsin, trypsin, and lipase were purchased from Shanghai Macklin Biochemical Co., Ltd.

2.2. In vitro simulated oral‐gastrointestinal digestion

First, 10 mg of HT was weighed and added to 10 mL of phosphate buffer along with 0.2 mg of α‐amylase. The pH was adjusted to 6.8 by using 0.5 mol/L hydrochloric acid solution, and the mixture was incubated in a water bath (37°C, 160 rpm/min) for 5 min. After the water bath, 5 mL of the sample was taken, and the enzyme was inactivated in a boiling water bath for 5 min. For simulated gastric digestion, 0.2 mg of pepsin was added to the saliva‐digested sample, and the pH was immediately adjusted to 3.0. The mixture was subjected to simulated gastric digestion at 37°C on a constant temperature shaker (150 r/min) for 2 h. After 2 h, 5 mL of the mixture was taken out and immediately placed in a boiling water bath for 5 min to stop the reaction. The collected samples were centrifuged (10,000 rpm, 10 min). Then, 0.4 mg of pancreatin was added to the saliva‐gastric digest, and the pH was adjusted to 7.0. Intestinal fluid digestion was simulated at 37°C in a constant‐temperature shaker (150 r/min) for 4 h. After digestion, 5 mL of the mixture was taken out and immediately placed in a boiling water bath for 5 min to stop the reaction. The collected samples were centrifuged (10,000 rpm, 10 min). The control group was treated in the same manner, and each experiment was independently repeated three times. HPLC was used to detect the digested samples.

2.3. In vitro human fecal fermentation

Fresh fecal samples were collected from four healthy volunteers (two males and two females, aged 24–27 years, without gastrointestinal diseases, antibiotic‐free for at least 30 days, and no olive oil in their diet for the 3 days prior to sample collection). The fecal samples were collected within 1 h and immediately preserved in anaerobic conditions. The samples were diluted in phosphate buffer in an anaerobic environment. The feces were then homogenized to obtain a 32% (w/v) fecal bacterial suspension, which was centrifuged (550 rpm, 5 min at room temperature) to remove larger particles. The supernatant containing bacterial cells was retained (Pérez‐Burillo et al., 2021), serving as the fecal inoculum. The final fermentation experiment had a total volume of 10 mL (7 mL growth medium, 2 mL fecal bacterial suspension, and 1 mL of 0.1 mol/L HT). The mixture was placed in Eppendorf tubes in an anaerobic chamber and incubated at 37°C with continuous shaking (60 rpm). All in vitro fermentation processes were performed in triplicate. The samples were incubated at 37°C for 48 h, and 4 mL samples were collected at 0, 2, 4, 6, 8, 12, 24, and 48 h. The fermentation liquid was subjected to centrifugation (12,000 rpm, 10 min), and the supernatant was collected. LC–MS and GC–MS were employed to analyze the simulated fermentation liquid.

2.4. Microbiota analysis by 16S rRNA gene sequencing

Five milliliters of fermentation broth collected at 0, 2, 6, 12, and 48 h were subjected to high‐speed centrifugation (13,000 rpm, 5 min). The precipitate was retained and sent to Beijing Nuohuzhiyuan Technology Co., Ltd. for gene sequencing. Denoising was performed in QIIME 2 based on the DADA2 analysis. The deionized sequences were clustered at a 97% similarity level by using Vsearch (version 2.13.4 Linux x86 64) and Cutadapt (version 2.3) to calculate the number of operational taxonomic units contained in different samples at the classification level. Alpha and beta diversity indices were used to comprehensively evaluate the overall microbial diversity.

2.5. Structural changes in HT colonic metabolites

First, 500 µL of the fermentation broth was transferred to a centrifuge tube. Second, 5 mL of methanol was added two times for liquid–liquid extraction, and the tube was centrifuged at room temperature (8000 rpm, 10 min). The supernatants from the two centrifugations were combined and evaporated to dryness under a nitrogen stream at 30°C. Prior to instrumental analysis, 120 µL of reconstitution solvent (acetonitrile:water = 4:1, v/v) was added to each dried sample, vortexed for 5 min, and centrifuged at 14,000 rpm for 5 min at room temperature. Next, 100 µL of the supernatant was taken into a 2‐mL MS sample vial containing an insert for analysis.

Instrumental analysis: The LC method was established on a QExactive LC system (Thermo Fisher), with the liquid phase component using an Ultimate 3000 LC system (Thermo Fisher). Metabolomics LC–MS was utilized using an HSS Hilic column (100 × 2.1 mm, 1.9 µm; Waters) at a column temperature of 40°C, a flow rate of 0.4 mL/min, and an injection volume of 5 µL. Mobile phase A was acetonitrile/water = 95/5 with 10 mM ammonium acetate, and mobile phase B was acetonitrile/water = 50/50 with 10 mM ammonium acetate. For negative ion mode analysis, the gradient for the liquid‐phase positive and negative spectra was as follows: 0.00–13.00 min, 99% A, 1% B; 13.10–15.00 min, 0% A, 100% B; and 15.10–18.00 min, 99% A, 1% B.

The MS method was established on a QExactive system (Thermo Fisher) by using the full scan/data‐dependent MS2 Top10 analysis mode, with electrospray ionization mode for positive and negative spectral analysis. The spray voltage was set at +3500 V for positive mode and −3000 V for negative mode, sheath gas flow rate at 40 arb, auxiliary gas flow rate at 10 arb, ion transfer tube temperature at 320°C, and auxiliary gas heating temperature at 350°C. For full scan, the primary mass resolution was set to 70,000 with a mass range of 70–1050 m/z. For data‐dependent MS2, the secondary scan resolution was 17,500, with a top 10 acquisition mode, collision energies of 20 and 40, and a mass range of 70–1050 m/z.

2.6. Extraction and analysis of SCFAs

The determination of SCFAs content followed the published procedure (Fu et al., 2019). Solutions were prepared to final concentrations as follows: 1 mol/L hydrochloric acid solution, 0.04 mol/L 2‐ethylbutyric acid, and different gradient concentrations of SCFAs standard solution. GC–MS was used to measure the concentrations of SCFAs (acetic acid, propionic acid, butyric acid, valeric acid, isobutyric acid, and isovaleric acid) in the fermentation liquid at 0, 2, 6, 12, and 48 h. The fermentation liquid was centrifuged at 12,000 rpm and 4°C for 4 min, and the supernatant was filtered with a 0.22 µm membrane. Subsequently, 20 µL of 6 mol/L hydrochloric acid and 30 µL of 0.04 mol/L 2‐ethylbutyric acid were added to 0.45 mL of the filtered supernatant. Then, the SCFAs were extracted with 0.5 mL of ethyl ether, shaken for 3 min, and centrifuged at 3000 rpm for 5 min at room temperature. The upper layer was treated with anhydrous CaCl2 to remove residual water, and the upper liquid was filtered through a membrane by using a syringe and transferred to a GC–MS analysis tube. Afterwards, 1 µL of the supernatant was taken and injected into the Agilent 6890‐5973 system for analysis (Agilent Technologies). The system is equipped with an high polarity‐free fatty acid phase (HP‐FFAP) column (Agilent 122–7062, 60.0 m × 250 µm × 0.25 µm) and a flame ionization detector. Helium was used as the carrier gas at a flow rate of 14.4 mL/min. The solvent delay was 5 min. The initial oven temperature was 100°C, which was held for 0.5 min and then increased to 180°C at 8°C/min, held for 1.0 min, and then increased to 200°C at 20°C/min, and finally held at 200°C for 5 min.

2.7. Molecular docking

Molecular docking is a method used to predict the binding mechanism and affinity between small‐molecule ligands and large biomolecular receptors by simulating their interactions (Udrea et al., 2021). The 3D structure of AhR was obtained from the PDB database (https://www.rcsb.org) and converted to PDBQT format by using The Open Babel GUI 3.1.1 for further processing. The 3D structure of HT colonic metabolites was downloaded in SDF format from PubChem, and the protein was processed using AutoDockTools software (version 1.5.6). The protein was separated, non‐polar hydrogens were added, Gasteiger charges were calculated, AD4 types were assigned, and all flexible bonds of the small‐molecule ligand were set to be rotatable. The docking box was adjusted to include all protein structures on the basis of the original ligand coordinates. Docking results were obtained by running autogrid4 and autodock4, revealing the binding energy. Finally, a visualization of the molecular docking was generated using PyMol software.

2.8. Statistical analysis

All graphs were generated using GraphPad Prism 8.0 software (GraphPad Software). Statistical analyses were performed using Origin (version 9.0, OriginLab, Microcal), including ordinary one‐way analysis of variance and unpaired t‐tests for mean comparisons. The mean values and standard deviations reported are from at least three independent biological replicate runs. Different letters (a, b, c, and d) indicate statistical significance (p < 0.05).

3. RESULTS AND DISCUSSION

3.1. Simulation of the content changes in HT during digestion in saliva, gastric fluid, and small intestinal fluid

The effect of food nutrients on human health largely depends on the digestive and fermentation processes in the gastrointestinal tract. In vitro gastrointestinal digestion and fermentation models have advantages such as reproducibility, simplicity, and universality. They can comprehensively simulate in vivo conditions and the digestive processes of the mouth, stomach, small intestine, and large intestine (Nie et al., 2019; Song et al., 2019). In the present work, in vitro simulated digestion experiments were conducted to verify whether HT is digested and degraded in the gastrointestinal system. First, the digestion of HT was examined by simulating saliva. As shown in Figure 1b,c, no significant change was observed in the molecular peak of HT before and after saliva digestion, as indicated by retention time and reaction value. Under simulated gastric digestion conditions, the retention time and response values of HT remained almost constant, similar to those during simulated saliva digestion, with a slight change in HT content (Table 1). After simulated intestinal digestion, the HT content slightly decreased (15.62%), but the majority was consumed in the colon (69.28%). These data indicate that saliva and gastric juice cannot completely degrade HT, suggesting that 79.25% of ingested HT can reach the colon intact (Figure 1d,e; Table 1), where it is degraded by the colonic microbiota.

FIGURE 1.

FIGURE 1

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High‐performance liquid chromatography (HPLC) chromatograms of in vitro simulated gastrointestinal digestion. (a) HPLC chromatogram of hydroxytyrosol (HT) before simulated salivary digestion; (b) HPLC chromatogram of HT after simulated salivary digestion; (c) HPLC chromatogram of HT after simulated gastric digestion; (d) HPLC chromatogram of HT before and after simulated intestinal digestion; (e) HPLC chromatogram of HT before and after simulated intestinal digestion; and (f) HPLC chromatogram of the control group.

TABLE 1.

Hydroxytyrosol (HT) remaining amount and percentage of digestion after simulating saliva, stomach, and intestinal digestion.

Process Time Content of HT (µg/mL) Digestibility (%)
Undigested 0 min 75.93 ± 3.76 0
Saliva digestion 5 min 73.66 ± 7.32 3.02
Gastric juice digestion 2 h 71.31 ± 5.44 3.15
Intestinal juice digestion 4 h 60.17 ± 5.89 15.62
Colonic digestion 8 h 18.48 ± 1.43 69.28
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Note: Prepare an HT solution at the same concentration as the experimental group to serve as the undigested group. Measure its concentration using liquid chromatography and calculate it based on the standard curve. For the digestion group, perform liquid chromatography after digestion and calculate the concentration using the standard curve. The digestion rate is the ratio of the concentration change before and after digestion of the concentration before digestion.

3.2. Analysis of microbial diversity during HT fecal fermentation

3.2.1. Alpha diversity

High‐throughput sequencing technology was used to assess the dynamic changes in the gut bacterial community during fermentation to study the interaction between HT and microbes and the process of mediating microbial‐derived metabolites. The α diversity indices of the bacterial community, including the Chao1 index, Shannon index, Simpson index, and observed species, are presented in Figure 2a–e. Chao1 index and observed species indicate species richness. Meanwhile, the Shannon and Simpson indices reflect species diversity. The Chao1 value was positively correlated with the number of species present in the community (Dou et al., 2022). Within 0–6 h, all of the measured diversity indices decreased continuously, indicating that the growth of the microbial community was inhibited. However, at 12 h of fermentation, the measured diversity indices showed a decreasing trend followed by an increasing trend, which may be due to HT metabolites promoting the growth of the gut microbiota. Within 12–48 h, the species richness and species diversity decreased again, possibly due to nutrient depletion. In summary, HT may inhibit the growth of the intestinal microbiota during 0–6 h. As time progresses, the gut microbiota responds to HT, degrades HT, and weakens its inhibitory effect on the microbiota, with metabolites that promote microbial growth gradually becoming the dominant factor.

FIGURE 2.

FIGURE 2

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Effects of HT on gut microbiota diversity. (a) Chao1 index; (b) Shannon index; (c) Simpson index; (d) observed species; and (e) principal coordinate analysis (PCoA) diagram. Letters a, b, c, and d indicate significant differences between different groups as determined by one‐way analysis of variance (ANOVA) and Duncan's test (p < 0.05).

3.2.2. Beta diversity

Principal coordinate analysis (PCoA) was used to reflect the overall differences in the gut microbiota between different groups to study the diversity of the microbial community during fermentation. As shown in Figure 2e, two principal axes accounted for 85.67% of the variation. Samples from the same group are represented by the same color. Samples that are closer together indicate a more similar species composition and community structure. Therefore, samples with high similarity in community structure tend to cluster together, whereas samples with large community differences are far apart. The PCA results (Figure 2e) show that the gut microbiota structure in the HT48, HT2, and HT0 groups is completely different from that in the HT12 and HT6 groups, indicating that significant changes in microbial composition occurred between 0–6 and 12–48 h. The overlap between the HT12 and HT6 groups is similar in community structure and reflects dynamic microbiota shifts during this intermediate stage. This pattern suggests that HT metabolites may gradually achieve equilibrium in regulating intestinal balance during the 6‐ to 12‐h fermentation period. Overall, the PCoA results reveal distinct shifts in gut microbiota composition at different fermentation stages, highlighting dynamic community structure adjustments over time.

3.2.3. Analysis of fecal microbiota community structure

Changes in the major bacterial taxa during fermentation were analyzed to gain a deep understanding of the regulatory effects of HT metabolites on the gut microbiota. The analysis of the phylum and genus levels reveals that Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria accounted for more than 90% of the total gut bacteria, consistent with other studies (Ashaolu, 2020). These results confirmed that the interactions between HT and gut microbiota modulated the gut bacterial composition. The proportional histogram (Figure 3a,f) shows the relative abundances of top 15 dominant phyla, and top 30 dominant genera in gut microbiota, highlighting shifts in key bacteria across groups. Notably, the abundance of p. Firmicutes and g. Phascolarctobacterium significantly increased at 48 h of fermentation, likely reflecting HT metabolites' role in promoting microbial growth. Conversely, the relative abundance of p. Bacteroidota, p. Proteobacteria, p. Actinobacteriota, g. Bacteroides, and g. Escherichia‐Shigella initially rose, but declined over time (Figure 3c–e,g,h), while g. Faecalibacterium gradually declined (Figure 3j), possibly due to nutrient depletion in the fermentation medium, leading to microbial population decline. Firmicutes and Phascolarctobacterium play essential roles in the production of SCFAs, which are important for maintaining gut health. On the other hand, Bacteroides contribute to gut homeostasis by secreting vesicle hydrolases, which facilitate crucial metabolic functions in the human colon, such as nutrient acquisition and the biodegradation of dietary fiber, starch, and proteins. A reduction in the abundance of Firmicutes, however, is commonly associated with dysbiosis, and has been identified as a significant marker in patients with ulcerative colitis (Guo & Li, 2019; Henn et al., 2021; Wexler & Goodman, 2017). In summary, HT metabolites may promote the increase of p. Firmicutes and g. Phascolarctobacterium, while leading to a decrease in the relative abundance of p. Bacteroidota, p. Proteobacteria, p. Actinobacteriota, g. Bacteroides, g. Escherichia‐Shigella, and g. Faecalibacterium. This may be related to the promotion effect of HT metabolites on microbial growth and the depletion of nutrients in fermentation broth leading to the reduction in bacterial microbiota.

FIGURE 3.

FIGURE 3

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Relative abundance of the gut microbiota at the phylum and genus level. (a) The relative abundance of the gut microbiota at the phylum level; (b–e) the relative abundance of p. Firmicutes, p. Bacteroidota, p. Proteobacteria, and p. Actinobacteriota; (f) the relative abundance of the gut microbiota at the genus level; and (g–j) the relative abundance of g. Bacteroides, g. Escherichia‐Shigella, g. Phascolarctobacterium, and g. Faecalibacterium. Letters a, b, c, and d indicate significant differences between different groups as determined by one‐way analysis of variance (ANOVA) and Duncan's test (p < 0.05).

3.3. Effects of SCFAs’ production

SCFAs are one of the main metabolites produced by microorganisms. To evaluate the impact of HT on the gut environment and detect the levels of volatile microbial‐derived metabolites. Changes in SCFAs (acetic acid, propionic acid, isobutyric acid, butyric acid, isovaleric acid, and valeric acid) concentrations during the fermentation process were measured (Figure 4). The total SCFAs content increased over time, from 273.84 ± 76.56 µg/mL at 0 h to 1110.6 ± 166.5 µg/mL at 48 h (Figure 4g). The concentrations of acetic acid, butyric acid, valeric acid, and isovaleric acid at 48 h were significantly different from their levels at 0 h, with notably higher levels at 48 h (Figure 4a,c,d,f). SCFAs produced by the gut microbiota can serve as energy substrates for intestinal cells and participate in regulating gut homeostasis. For instance, acetate prevents intestinal oxidative stress and inflammation by activating the G‐protein‐coupled receptor 43. Propionate plays a crucial role in maintaining metabolic homeostasis by inhibiting apoptosis (Koh et al., 2016), while butyrate, as the preferred metabolic substrate for intestinal epithelial cells (IECs), mediates mucin synthesis (Willemsen et al., 2003), TJ reassembly, and upregulation of occludin and ZO‐1, thereby improving intestinal barrier permeability (Ma et al., 2012; Miao et al., 2016). Therefore, our study suggests that HT may promote the growth of SCFA‐producing microbes, indicating that HT fermentation could modulate the gut microenvironment by regulating gut microbiota structure and promoting SCFAs production. In turn, these SCFAs play essential roles in maintaining intestinal health by serving as energy substrates, reducing inflammation, and enhancing barrier function.

FIGURE 4.

FIGURE 4

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Concentrations of short‐chain fatty acids (SCFAs) in the fermentation broth after different fermentation times. (a) Acetic acid; (b) propionic acid; (c) isobutyric acid; (d) n‐butyric acid; (e) valeric acid; (f) isovaleric acid; and (g) total acid. Data are presented as mean ± standard deviation. Different letters within the same test indicate significant differences at p < 0.05 (n = 3 per group).

3.4. Major differential metabolites during the fermentation process of HT fecal bacteria

Using ultra‐performance liquid chromatography‐MS technology, the primary differential metabolites in HT were identified. Through accurate mass and MS/MS data in the MS2 database, a total of 3399 metabolites was identified in the positive ion mode, and 808 metabolites were identified in the negative ion mode. We performed PLS‐DA to investigate the differences between different groups. In the scores plot (Figure 5a,b), samples from the group HT are distinctly separated from those in blank B, indicating significant differences in their metabolite profiles. The tight clustering within each group suggests consistency among replicates. Additionally, the clear separation between groups highlights the potential discriminatory power of the variables analyzed. Samples from the experimental group (red) are primarily located in the positive quadrant, while those of the blank group (blue) are clustered in the negative quadrant. This spatial separation indicates a significant shift in the metabolite after the addition of HT. Overall, the PLS‐DA scores plot effectively illustrates the distinct metabolite profiles between experimental group and blank group, and the impact of adding HT is underscored.

FIGURE 5.

FIGURE 5

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Metabolite profile score of hydroxytyrosol (HT) after fermentation by fecal bacteria. (a) Partial least square discriminant analysis (PLS‐DA) scores plot of metabolites identified in positive ion mode; (b) PLS‐DA scores plot of metabolites identified in negative ion mode. The experimental group was represented by HT, and blank groups are represented by B. Each point represents a sample colored by group: HT groups are shown in red, and B groups are shown in blue. The scores plot demonstrates the distribution of samples along the first two latent variables (t1 and t2).

Strict filtering criteria using PLS‐DA (p < 0.05; variable importance in projection (VIP) > 1) (Gromski et al., 2015) were applied to accurately identify the differential metabolites affected by fermentation. Sixty differential metabolites were screened out (Table 2). These metabolites include 56 non‐volatile substances, such as indole‐3‐acetic acid, indole‐3‐acetaldehyde (IAld), skatole, kynurenine, and homovanillic acid. Tryptophan‐derived metabolites, including indole‐3‐acetic acid, IAld, and kynurenine, play crucial roles in regulating gut immune responses and maintaining intestinal barrier function (Gargaro et al., 2021; Smith & Macfarlane, 1996; Tomii et al., 2023). Skatole, a heterocyclic compound produced by gut bacteria, can activate the AhR, further impacting gut health (Kurata et al., 2023). Homovanillic acid, known for its antioxidant and anti‐inflammatory properties, also contributes to maintaining gut health (A. Singh et al., 2020). Additionally, volatile compounds such as toluene, isobutyric acid, propionic acid, and valeric acid were identified. Propionic acid and valeric acid, both SCFAs, are essential for gut health, providing energy, regulating inflammation, and supporting intestinal barrier integrity (Martin‐Gallausiaux et al., 2021). Overall, this study highlights the wide range of metabolites and derived substances produced during HT fermentation, which play important roles in modulating gut health. These metabolites not only reflect shifts in the microbial community but also suggest that HT fermentation may influence gut health through various mechanisms. The findings provide valuable theoretical insights for further research into the potential pathways through which HT metabolites contribute to gut health.

TABLE 2.

Partial least square discriminant analysis (PLS‐DA) was used to screen out 60 different metabolites after fermentation of hydroxytyrosol (HT) fecal bacteria; A p‐value ˂0.05 indicates that the research results are statistically significant, and variables with VIP values >1 are considered to have significant importance in the model; [M+H]+, positively charged molecular ion; [M‐H]−, negatively charged molecular ion.

Sequence Name of the metabolite p‐value VIP Pattern
1 Norbelladine 0.021117083 1.226735295 [M+H]+1
2 Azobenzene 0.00195656 3.540476949 [M+H]+1
3 Dimethyltryptamine 0.003067789 4.875867406 [M+H]+1
4 Indole‐3‐acetic acid 0.016527303 1.385792254 [M+H]+1
5 Indole‐3‐acetaldehyde 0.003756054 1.295645476 [M+H]+1
6 Skatole 0.018008867 4.21815197 [M+H]+1
7 Kynurenine 0.013677808 6.784718349 [M+H]+1
8 1,3‐Dipropylxanthine 0.001597225 1.302065728 [M+H]+1
9 Aminohippuric acid 0.031908018 1.060172792 [M+H]+1
10 5‐Methyldeoxycytidine 0.039389149 1.53772191 [M+H]+1
11 2,4‐Diaminotoluene 0.004911202 1.204124372 [M+H]+1
12 Ectoine 0.010687549 1.653693498 [M+H]+1
13 N‐Methylisopelletierine 0.000191273 1.477535984 [M+H]+1
14 Trigonelline 0.001972701 2.194206531 [M+H]+1
15 N8‐Acetylspermidine 0.004486066 2.049568424 [M+H]+1
16 Hypoxanthine 0.049297446 10.58953129 [M+H]+1
17 Cytosine 0.000157013 3.077893999 [M+H]+1
18 Acetylcadaverine 0.004500052 2.034736733 [M+H]+1
19 2,5‐Dimethylpyrazine 0.018035317 1.371408388 [M+H]+1
20 (2E)‐2‐Pentenoic acid 0.00190723 10.35364469 [M+H]+1
21 DL‐Ornithine 0.001930332 1.277021166 [M+H]+1
22 Choline O‐Sulfate 0.021147761 1.068225717 [M+H]+1
23 N‐methylethanolamine phosphate 0.034431225 1.504065563 [M+H]+1
24 5‐Aminovaleric acid 0.00190723 10.35364469 [M+H]+1
25 Tetramethylurea 0.0096184 1.346057335 [M+H]+1
26 Glycerin 0.009247523 5.051318464 [M+H]+1
27 Indole‐3‐acetic acid 0.042602485 1.310255258 [M‐H]−1
28 3‐Hydroxybenzoic acid 0.00091028 1.201651688 [M‐H]−1
29 Homovanillic acid 0.020966207 1.2023318 [M‐H]−1
30 Pseudouridine 0.045629181 1.26438957 [M‐H]−1
31 L‐Phenylalanine 0.044563905 5.042608667 [M‐H]−1
32 3‐Carboxyindole 0.030537558 1.35153959 [M‐H]−1
33 Butylparaben 0.000772198 1.826650674 [M‐H]−1
34 4‐(Hydroxymethyl)benzoic acid 0.024878095 1.028086676 [M‐H]−1
35 4‐Hydroxybenzoic acid 0.039203279 1.005948773 [M‐H]−1
36 Vanillyl alcohol 0.016978296 5.657619165 [M‐H]−1
37 Cyclamic acid 0.007633232 7.303507359 [M‐H]−1
38 Phenylacetic acid 0.002274757 5.323852884 [M‐H]−1
39 4‐Indolecarbaldehyde 0.020741831 1.413404978 [M‐H]−1
40 Catechol 0.042904395 1.03019195 [M‐H]−1
41 10‐Hydroxydecanoic acid 0.015119737 1.235911635 [M‐H]−1
42 Allopurinol 0.039376659 5.650387502 [M‐H]−1
43 Imidazolelactic acid 0.014985742 1.405483574 [M‐H]−1
44 Toluene 0.004245146 3.418560255 [M‐H]−1
45 Dibutyl malate 0.025536654 1.080439198 [M‐H]−1
46 Mevalonic acid 0.015302429 1.628729953 [M‐H]−1
47 4‐Oxoproline 0.020046508 2.209354636 [M‐H]−1
48 2′‐Deoxycytidine 0.038292567 1.506033012 [M‐H]−1
49 Propyl hydrogen sulfate 0.043435107 1.609390347 [M‐H]−1
50 Palmitic acid 0.002302517 6.116638637 [M‐H]−1
51 1‐Methyl‐3,6‐(1H,2H)‐pyridazinedione 0.040669756 3.321657051 [M‐H]−1
52 4‐Hydroxybutyric acid (GHB) 0.012674043 1.513322233 [M‐H]−1
53 Hexanoic acid 0.003421582 1.026049552 [M‐H]−1
54 Isobutyric acid 0.003519377 8.861465802 [M‐H]−1
55 L‐(+)‐Lactic acid 0.046924873 5.245602167 [M‐H]−1
56 Pyruvic acid 0.012855037 1.516232703 [M‐H]−1
57 Propionic acid 0.003658242 2.32260535 [M‐H]−1
58 2‐Hydroxyvaleric acid 0.007206265 1.751628483 [M‐H]−1
59 3,4‐Dimethylbenzoic acid 0.001860645 3.803458773 [M‐H]−1
60 Valeric acid 0.002046953 4.525657273 [M‐H]−1
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3.5. Correlation analysis between gut microbiota and key metabolites

LDA effect size analysis to evaluate the diversity of gut microbiota. A histogram of linear discriminant analysis (LDA) scores was plotted to identify statistically significant biomarkers and reveal dominant microorganisms in each group (Segata et al., 2011). Figure 6a shows eight dominant taxa in the HT0 group (red), 17 in the HT2 group (blue), six in the HT6 group (purple), five in the HT12 group (green), and 15 in the HT48 group (green). o_Oscillospirales, f_Ruminococcaceae, and g_Faecalibacterium were significantly enriched in the HT0 group; o_Bacteroidales, c_Bacteroidia, p_Bacteroidota, g_Bacteroides, and f_Bacteroidaceae were significantly enriched in the HT2 group; and o_Lachnospirales, f_Lachnospiraceae, p_Firmicutes, g_Dorea, and c_Clostridia showed significant differences among groups, and they were significantly enriched in the HT48 group, with the highest abundance. Lachnospiraceae, reported as a probiotic that induces the accumulation of regulatory Treg cells in the colon, contributes to butyrate production, and reduces inflammation (Atarashi et al., 2013). As shown in Figure 6a, the abundance of Lachnospirales in the HT48 group was significantly higher than in other groups, with Lachnospiraceae displaying higher LDA scores than other taxonomic units, indicating a greater impact on intergroup differences. In summary, the dominant microbiota in each group changed with fermentation time.

FIGURE 6.

FIGURE 6

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The LDA effect size (LEfSe) analysis of gut microbiota and correlation heatmap analysis based on Pearson correlation coefficients of differential abundance microorganisms with differential metabolites and short‐chain fatty acids (SCFAs). (a) Linear discriminant analysis (LDA) scores of the differentially abundant taxa; The X‐axis represents the LDA score (Log10), and the Y‐axis represents significantly different genera (LDA score > 4). (b) Taxonomic cladogram obtained from LEfSe analysis of 16S sequences; The circles radiating from the inside to the outside represent the classification levels from phylum to genus, and the size of the nodes corresponds to the average relative abundance of different genera. Data are presented as mean ± standard deviation (n = 4).

On the basis of the LDA scores, a phylogenetic clustering analysis was performed to identify important microbial taxa based on taxonomy. As shown in Figure 6b, in the HT2 group, Bacteroidetesand Proteobacteria played important roles during fermentation, and Actinobacteriota, Fusobacteriota, and Firmicutes were prominent in the HT6, HT12, and HT48 groups, respectively. The majority of microorganisms constituting the human microbiota can be classified into four major phyla: Bacteroidota, Firmicutes, Proteobacteria, and Actinobacteriota. Firmicutes and Bacteroidota account for about 90% of the relative abundance of the gut microbiota, and their relationship plays a crucial role in maintaining gut homeostasis. Actinobacteria and Proteobacteria account for the remaining 10% (Arumugam et al., 2011; Segata et al., 2012). In summary, based on the LDA scores and evolutionary clustering analysis, the microbial composition at different time points during HT fermentation underwent significant changes. Microbes, such as Bacteroidetes, Actinobacteria, Proteobacteria, and Firmicutes, play important roles in maintaining gut barrier homeostasis and metabolic regulation.

The formation of metabolites during fermentation is closely related to microorganisms (Ferrocino et al., 2018). Therefore, exploring the relationship between microbial and differential substances is necessary. As shown in Figure 7a, certain gut microbiota such as, Bacteroides, Faecalibacterium, Klebsiella, Lachnospira, and Fusicatenibacter, positively correlate with the production of indole‐3‐acetic acid (p < 0.05). Studies have shown that Klebsiella can produce indole‐3‐acetic acid (Celloto et al., 2012), playing a crucial role in gut health. Indole‐3‐acetic acid not only alleviates the severity of DSS‐induced colitis but also enhances mucosal barrier function by promoting mucin sulfation. Furthermore, indole‐3‐acetic acid can bind to AhR, facilitating the translocation of the AhR complex into the nucleus to regulate the expression of target genes, thereby further supporting its beneficial effects on gut barrier function (M. Li et al., 2024). In addition to the indole‐3‐acetic acid, other microbial metabolites, such as IAld and kynurenine, were found to be positively correlated with specific gut microbiota. Barnesiella was positively correlated with the production of IAld (p < 0.05), while Bacteroides, Collinsella, Parabacteroides, and Klebsiella showed a positive correlation with kynurenine production (p < 0.05). Research indicates that specific gut microbiota, such as Bacteroides and Klebsiella, can influence host tryptophan metabolism, leading to the production of kynurenine. This metabolic interaction highlights kynurenine as an important metabolite in host‐microbiome interplay, with implications for immune modulation and gut health (Sun et al., 2023). Meanwhile, these Gram‐negative obligate anaerobes play various roles in the human gut microbiota, and they are key participants in maintaining the gut microbial food web (Wexler, 2007). Furthermore, SCFAs are key molecules in the interaction between gut microbiota and the host immune system. Phascolarctobacterium, Coprococcus, and Sutterella are positively correlated with the production of acetic acid, n‐butyric acid, and isobutyric acid (p < 0.05). Roseburia is positively correlated with isobutyric acid production (p < 0.05). Notably, these SCFA‐producing genera were significantly enriched in the HT48 group, suggesting that HT fermentation promotes the growth of these microbial populations, which in turn enhances SCFA production. In summary, HT fermentation has a regulatory effect on the gut microbial community and its metabolic functions. During HT fermentation, Bacteroides, Faecalibacterium, Klebsiella, and Lachnospira positively correlate with the production of metabolites, such as indole‐3‐acetic acid, IAld, and kynurenine. While other genera like Phascolarctobacterium, Coprococcus, and Roseburia are positively correlated with SCFAs production. These findings highlight the potential of HT fermentation in shaping gut microbial diversity and promoting beneficial metabolic pathways.

FIGURE 7.

FIGURE 7

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Key microbial communities' characteristics. (a) Correlation between microbes, key differential metabolites, and short‐chain fatty acids (SCFAs). (b) the relative abundance of Bacteroides; (c) the relative abundance of Klebsiella; (d) the relative abundance of Collinsella; (e) the relative abundance of Fusicatenibacter; (f) the relative abundance of Faecalibacterium; and (g) the relative abundance of Parabacteroide. Data are mean ± SD (n = 4), different lowercase letters above the bars indicate significant differences between groups (p ≤ 0.05); the red color denotes a positive correlation, while blue color denotes a negative correlation. The intensity of the color is proportional to the strength of Spearman correlation.

Recent studies have shown that the gut microbiota produces various tryptophan metabolites. The gut microbiota produces tryptophan or indole metabolites, which act as ligands for AhR. Indole metabolites enhance intestinal homeostasis through AhR‐mediated IEC IL‐10R1 regulation (Alexeev et al., 2018). Indole, as an AhR ligand, has anti‐inflammatory activity and maintains intestinal homeostasis (Lee & Lee, 2010). The genus Lactobacillus can convert tryptophan into IAld, which can activate AhR by inducing IL‐22, further promoting intestinal homeostasis (Zelante et al., 2013). Skatole is a common intestinal metabolite produced by Bacteroides and Clostridium through the decarboxylation of indole acetic acid (Russell et al., 2013; Smith & Macfarlane, 1996; Whitehead et al., 2008). Skatole induces AhR activation, thereby regulating IEC death (Kurata et al., 2019). Microbially derived tryptophan metabolite kynurenine has the ability to bind and activate AhR (Kurata et al., 2019). Moreover, AhR is activated in IECs to enhance the intestinal barrier function, alleviate inflammation, and maintain overall mucosal homeostasis (Metidji et al., 2018; Yin et al., 2019). AhR can also improve the repositioning of zonula occludens‐1, enhancing the integrity of the intestinal epithelium, ameliorating hypoxia‐induced changes in gut permeability, and maintaining normal gut barrier function (Han et al., 2016). These findings demonstrate the important role of AhR in maintaining gut health.

3.6. Analysis of molecular docking simulation results

AhR, as a ligand‐dependent transcription factor, plays an important role in gut health. It contributes to homeostasis in the gastrointestinal tract, and its activation can treat intestinal barrier damage by restoring the integrity of TJs (Qiu et al., 2012; M. Yu et al., 2018). In the present study, computer‐simulated molecular docking was used for binding simulation to explore the binding affinity between HT metabolites and derivatives and the intestinal barrier‐related receptor AhR. Molecular docking is a technique that simulates the interaction between a small‐ligand molecule and a receptor protein, calculating the binding energy between them to predict their binding affinity. A binding energy below 0 indicates spontaneous binding of the two molecules, with lower energies indicating more stable conformations. According to earlier studies, a binding affinity of <−4.52 kcal/mol indicates that two molecules can bind, <−5 kcal/mol indicates good binding, and <−7.0 kcal/mol indicates significant binding (Grosdidier et al., 2011). All of them showed a binding energy with AhR <−6.0 kcal/mol (Table 3), indicating a high affinity between these compounds and AhR (X. Li et al., 2022; Wei et al., 2020). Therefore, these compounds may activate the AhR pathway and play a key role in repairing the intestinal barrier.

TABLE 3.

Binding energy of five major differential metabolites for molecular docking with aryl hydrocarbon receptor (AhR).

Sequence Substance Structure Binding energy(kcal/mol)
1 Indole‐3‐acetic acid C10H9NO2 −6.9
2 Indole‐3‐acetaldehyde C10H9NO −6.7
3 Skatole C9H9N −6.6
4 Kynurenine C10H12N2O3 −6.5
5 Homovanillic acid C9H10O4 −6
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To investigate the potential interaction between HT metabolites and the key target AhR, molecular docking simulations were conducted to screen for compounds with binding energies <−6 kcal/mol. Five key HT metabolites, indole‐3‐acetic acid, IAld, skatole, kynurenine, and homovanillic acid, were identified for further analysis. The interactions between these metabolites and AhR were visually analyzed using PyMOL software to provide detailed insights into their binding modes. Indole‐3‐acetic acid interacts with the amino acid residue LYS‐38 of AhR through hydrogen bonding. Two hydrogen bonds were observed between indole‐3‐acetic acid and the amino acid residue LYS‐38 of AhR. IAld interacts with the amino acid residues VAL‐376 and GLN‐389 of AhR. Kynurenine interacts with the amino acid residues GLN‐389, ASP‐170, and ARG‐173 of AhR. Homovanillic acid interacts with the amino acid residues ASP‐161, ASN‐158, and LYS‐104 of AhR, and skatole forms a hydrogen bond with the amino acid residue CYS‐224 of AhR (Figure 8a–e). In summary, five compounds in HT metabolites with high binding affinity to AhR were identified through computer‐simulated molecular docking and molecular dynamics simulations. These compounds may repair intestinal barrier function by activating the AhR pathway.

FIGURE 8.

FIGURE 8

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Diagram of molecular docking. (a) The 3D interaction modes of AhR with indole‐3‐acetic acid; (b) the 3D interaction modes of AhR with indole‐3‐acetaldehyde; (c) the 3D interaction modes of AhR with skatole; (d) the 3D interaction modes of AhR with kynurenin; and (e) the 3D interaction modes of AhR with homovanillic acid. The yellow structure indicates the amino acid residue of AhR, and the yellow dashed line indicates the bond length.

4. CONCLUSION

In this study, an in vitro simulated digestion and colon fermentation model was employed to evaluate the metabolism of HT. Our findings indicate that after simulated intestinal digestion, 79.25% of the ingested HT remain intact and reach the colon, suggesting that colon fecal bacterial fermentation is the primary pathway for HT transformation. During the simulated in vitro digestion and colon fermentation processes, colon fecal bacterial fermentation was observed to be the main pathway for HT transformation. HT was metabolized and utilized by the intestinal microbiota, producing various metabolites and derived metabolites, modulating the structure of the intestinal microbiota, and promoting the growth of SCFA‐producing bacteria. Specific intestinal bacteria, such as Bacteroides, Faecalibacterium, Klebsiella, and Lachnospira, showed a positive correlation with the production of specific metabolites such as indole‐3‐acetic acid, IAld, skatole, kynurenine, and homovanillic acid. Through computer‐simulated molecular docking, we demonstrated that these five metabolites can tightly bind to the AhR, suggesting a potential mechanism by which they may enhance the intestinal barrier function. Our study not only highlights the transformative role of HT and its metabolites in gut health but also opens avenues for future research into their therapeutic potential. Understanding how these metabolites interact with gut receptors may lead to innovative strategies for enhancing intestinal barrier function and addressing related health issues.

AUTHOR CONTRIBUTIONS

Yuqing Song: Methodology; validation; writing—original draft. Mengting Li: Methodology; resources. Jingle Liu: Resources. Juan Wang: Resources. Aimei Zhou: Supervision. Yong Cao: Review and editing. Shan Duan: Review and editing. Qun Wang: Conceptualization; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; validation; writing—original draft; writing—review and editing.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

ACKNOWLEDGMENTS

This work was financially supported by the Basic and Applied Basic Research Fund of Guangdong Province (22201910240002498 to Q. Wang), Guangdong Provincial Key Laboratory of Nutraceuticals, and Functional Foods (2018B030322010).

Song, Y. , Li, M. , Liu, J. , Wang, J. , Zhou, A. , Cao, Y. , Duan, S. , & Wang, Q. (2024). Screening study of hydroxytyrosol metabolites from in vitro fecal fermentation and their interaction with intestinal barrier repair receptor AhR. Journal of Food Science, 89, 10134–10151. 10.1111/1750-3841.17609

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