
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39256599
72225
10.1038/s41598-024-72225-1
Article
Impact of ginsenoside Rb1 on gut microbiome and associated changes in pharmacokinetics in rats
Chen Yue 1
Zhang Kang-xi 2
Liu Hui 23
Zhu Yue 1
Bu Qing-yun 23
Song Shu-xia 2
Li Ya-chun 2
Zou Hong hzhou01@sinh.ac.cn

4
You Xiao-yan youxy@tib.cas.cn

123
Zhao Guo-ping gpzhao@sibs.ac.cn

145
1 grid.9227.e 0000000119573309 Master Lab for Innovative Application of Nature Products, National Center of Technology Innovation for Synthetic Biology, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, 300308 China
2 https://ror.org/05d80kz58 grid.453074.1 0000 0000 9797 0900 Henan Engineering Research Center of Food Microbiology, College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China
3 Haihe Laboratory of Synthetic Biology, Tianjin, 300308 People’s Republic of China
4 grid.9227.e 0000000119573309 Engineering Laboratory for Nutrition, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031 China
5 grid.9227.e 0000000119573309 CAS-Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai, 200032 China
10 9 2024
10 9 2024
2024
14 211682 4 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Ginsenoside Rb1 exhibits a wide range of biological activities, and gut microbiota is considered the main metabolic site for Rb1. However, the impact of gut microbiota on the pharmacokinetics of Rb1 are still uncertain. In this study, we investigated the gut microbiome changes and the pharmacokinetics after a 30 d Rb1 intervention. Results reveal that the systemic exposure and metabolic clearance rate of Rb1 and Rd were substantially affected after orally supplementing Rb1 (60 mg/kg) to rats. Significant increase in the relative abundance of Bacteroides cellulosilyticus in gut microbiota and specific glycoside hydrolase (GH) families, such as GH2, GH92, and GH20 were observed based on microbiome and metagenomic analysis. Moreover, a robust association was identified between the pharmacokinetic parameters of Rb1 and the relative abundance of specific Bacteroides species, and glycoside hydrolase families. Our study demonstrates that Rb1 administration significantly affects the gut microbiome, revealing a complex relationship between B. cellulosilyticus, key GH families, and Rb1 pharmacokinetics.

Keywords

Ginsenoside Rb1
Pharmacokinetics
Gut microbiome
Glycoside hydrolase
Bacteroides cellulosilyticus
Subject terms

Biochemistry
Microbiology
Health care
Tianjin Synthetic Biotechnology Innovation Capacity Improvement ProjectsTSBICIP-CXRC-042 TSBICIP-CXRC-008 Chen Yue Zhao Guo-ping Major Project of Haihe Laboratory of Synthetic BiologyE2M9560201 You Xiao-yan National Natural Science Foundation of China31200035 You Xiao-yan Strategic Priority Research Program of the Chinese Academy of SciencesXDC 0110300 You Xiao-yan issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Panax ginseng has been utilized as both medicinal and edible plant for thousands of years in China, and has wide applications in the medical and food industries. Ginsenosides are considered as the main active ingredients in ginseng1. Current research has revealed that the gut microbiome is the primary metabolic site for ginsenosides2. Various conversion pathways have been identified based on gut microbiome, such as the conversion of ginsenoside Rb1 and Rb2 into CK through ginsenoside Rd3, and the metabolism of ginsenoside Rg1 to protopanaxatriol via ginsenoside Re4,5. Enzymes play a crucial role in this process. During their growth and reproduction, gut microbiome produces various types of enzymes that can enhance enzyme-catalyzed phase I reactions. For example, Bacteroides and Lactobacillus can facilitate deglycosylation, whereas Bifidobacterium and Eubacterium promote oxidation and hydrolysis. These distinct bacterial groups contribute to the enzyme-catalyzed phase I reactions of ginsenosides, leading to the formation of numerous structurally diverse secondary metabolites6.

Ginsenoside Rb1 (Fig. S1) is among the most abundant and important ginsenosides isolated from ginseng. Its antidiabetic7, anti-inflammatory8, and antitumor activities have been previously validated9. Regulating the composition of the gut microbiome, which is crucial for the in vivo biotransformation of ginsenosides, may influence the pharmacokinetics and biological activity of ginsenoside Rb1. In our previous study, Rb1 could improve hyperlipidemia, hyperinsulinemia, and insulin resistance in high-fat diet-induced obese mice, potentially due to the modulation of key bacterial genera and intestinal fatty acid profiles10. Meanwhile, antibiotics decrease the concentration of ginsenoside Rb1 metabolites and β-glucosidase activity in rats, which induced the increase of the area under the concentration–time curve (AUC) and terminal elimination half-life (T1/2) of ginsenoside Rb111. Despite being one of the main active ingredients in edible resources, few studies focus on the influence of long-term Rb1 intervention on gut microbiome in normal animal model. Moreover, a direct connection between the key intestinal bacteria and ginsenoside Rb1 pharmacokinetics has not been established owing to the complex and functionally redundant nature of the gut microbiome. We investigated the effects of a 30 d ginsenoside Rb1 intervention in a normal rat on its pharmacokinetic characteristics and changes in the composition of the gut microbiome.

Materials and methods

Materials

Ginsenoside Rb1, Rd, Rg3, F2, CK, PPD, and berberine (purity > 98.0%) were purchased from Yuanye Bio-technology Co., Ltd. (Shanghai, China). β-galactosidase and β-glucosidase staining kits were obtained from Solarbio Science & Technology Co., Ltd. (Beijing, China), High-performance liquid chromatography (HPLC) grade methanol and acetonitrile were purchased from Merck (New Jersey, USA) and HPLC grade water was prepared using a Milli-Q purification system (Millipore, Bedford, MA). All other compounds and solvents were of analytical grade.

Animals

Male Sprague Dawley (SD) rats (8 weeks and 200 ± 20 g) were obtained from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). The animals were housed in a specific pathogen-free animal room (environment temperature: 20–25 °C and relative humidity: 50% ± 5%), with a controlled 12 light/dark cycle. All animals were provided food and water ad libitum and the procedures related to animal care. All experimental protocols were approved by the Ethics Committee at the Animal Center of Nankai University, China (No.2022-SYDWLL-000569). All methods were performed following with the American Veterinary Medical Association (AVMA) Guidelines for the Euthanasia of Animals (2020) and the Chinese government's guidelines and regulations for using laboratory animals.

Pharmacokinetics of ginsenoside Rb1 in rats

The animal experimental design is shown in Fig. S2. After 1 week of adaptive feeding, the male SD rats (n = 24) were randomized into three groups (n = 8 per group), namely, ND, RinL, and RinH groups. The rats in the RinL and RinH groups orally received daily of 5 mg/kg and 60 mg/kg ginsenoside Rb1, respectively. The rats in ND group orally received an equivalent volume of water daily. Fecal samples collected from all rats after 30 d of drug administration and were stored at − 80 °C for subsequent analyses. The pharmacokinetic experiment was performed 48 h after the final administration. Before experiment initiation, all rats were made to fasted for 12 h but were provided water ad libitum. When the experiment commenced, each group orally received 200 mg/kg of ginsenoside Rb1. Based on our preliminary experiment to obtain a more comprehensive plasma concentration–time profile of ginsenoside Rb1 and its related metabolites, this dose was selected. The rats were removed from their housing cages and placed in an anesthesia chamber. Anesthesia was induced using 4% isoflurane administered via a small animal anesthesia machine for 1 min. Once anesthetized, the rats were removed from the chamber. Blood samples were collected via the orbital sinus at 0.083, 0.25, 0.5, 1, 2, 4, 6, 8, 12, 24, 36, 48, and 72 h using glass capillary tubes. After blood collection, hemostasis was ensured, and the rats were returned to their housing cages. The animals typically regained consciousness within one to two minutes. The collected blood samples were immediately centrifuged at 2500 rpm for 10 min at 4 °C to obtain plasma samples, which were subsequently stored at − 80 °C. All rat were euthanized using 100% carbon dioxide, which was infused into the cage at 50% VDR/min by using a flow meter. Fecal samples collected from all rats between 0 and 72 h after administration and were stored at − 80 °C. Phoenix WinNonlin (version 8.1.0, Certara USA, Inc., USA) was used to calculate the pharmacokinetic parameters, including the area under the curve (AUC) and the terminal elimination half-life (T1/2). The maximum plasma concentration (Cmax) and the time to reach the maximum concentration (Tmax) were directly obtained from the plasma concentration–time plots.

Liquid chromatography with tandem mass spectrometry analysis of ginsenoside Rb1 and its metabolites

The analyses were based on a previous study12 with slight modifications. For the fecal samples, specimens collected within 72 h were homogenized and physiological saline was added at a ratio of 1:9 (g: mL) to prepare a fecal suspension. The proteins in the plasma sample and fecal suspension were precipitated by adding 500 µL of the IS solution (0.05 ng/mL berberine in methanol) to 100 µL of the sample. After vortexing for 15 min, the mixture was centrifuged at 16,100×g for 5 min. The supernatant (500 µL) was subsequently moved into a clean tube and dried under a nitrogen stream at 40 °C. The remaining residue was dissolved with 100 μL of 20% acetonitrile containing 0.1% formic acid.

Ginsenoside Rb1 and its major metabolites were detected using a 6500 Q-TRAP from AB Sciex equipped with Shimadzu LC-20A and Autosampler systems. Chromatographic separation was performed on a C18 column with water containing 0.1% formic acid (A) and acetonitrile (B) in the gradient elution mode and with a 0.5 mL/min flow rate; the sample injection volume was 2 µL. Data in MRM was collected using the positive ion mode and the parameters for detecting ginsenoside Rb1 and its major metabolites are shown in Table S1.

16S rRNA and metagenomic sequencing

The total genome DNA for the 16S rRNA sequencing was extracted from samples using the cetyltrimethylammonium bromide method. DNA concentration and purity were monitored on 1% agarose gel. The DNA was diluted to 1 ng/μL based on its concentration using sterile water. The 16S rRNA genes were performed using a specific primer (341F-806R) with a barcode. Phusion® High-Fidelity PCR Master Mix (New England Biolabs), 0.2 μM of forward and reverse primers, and approximately 10 ng of template DNA were used in all 30μL reactions during polymerase chain reaction (PCR) amplification. Thermal cycling comprised initial denaturation at 98 °C for 1 min, followed by 30 cycles of denaturation at 98 °C for 10 s, annealing at 50 °C for 30 s, and elongation at 72 °C for 60 s. Finally, 72 °C for another 5 min. The sequencing libraries with the addition of index codes were generated using the NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, USA) by the manufacturer’s instructions. The library quality was evaluated using the Qubit 2.0 Fluorometer (Thermo Scientific) and the Agilent Bioanalyzer 2100 system. The library was sequenced using an Illumina NovaSeq 600 platform, targeting the V3-V4 region of the 16S rRNA gene, resulting in the generation of 250 bp paired-end reads. Data were analyzes using the EasyAmplicon pipeline13. Briefly, the analyses of the raw sequence date were performed using USEARCH v10.0.240 (http://www.drive5.com/usearch) and VSEARCH v2.13.6 (https://github.com/torognes/vsearch). After sequence merging and quality control, UNOISE3 was used for ASV denoising. Species annotation tables were generated through the alignment of the sequences against the SILVA database (Silva_16s_v123). Further α- and β-diversity calculated using USEARCH and the data were rarefied to 10,000 reads per sample to ensure comparability. α-diversity metrics including ACE, observed ASV, and Simpson index and β-diversity was assessed using Jaccard distances and Bray–Curtis distances.

DNA sample testing, library construction, and sequencing with an Illumina HiSeq platform were conducted at Shanghai Applied Protein Technology Co., Ltd for the metagenomic sequencing. The sequencing data were analyzed using the EasyMetagenome pipeline14. Briefly, the raw reads were processed using Kneaddata (v.0.7.4), which performs quality control, trimming with Trimmomatic, removal of host-derived sequences using bowtie2 by comparing it against a reference genome, species annotation using Kraken2 on filtered data, and evaluation of the abundance of each species using Bracken2. Additionally, the differential abundance of the bacterial taxa was assessed through linear discriminant analysis (LDA) effect size (LefSe)15. Subsequently, paired-end reads were assembled de novo using MEGAHIT (version 1.2.9)16, and ORFs from the generated sequences were predicted using metaProdigal (version 2.6.3). For nonredundant genes, CD-HIT (version 4.6.8) was used. A gene abundance table was generated using Salmon17,18. Furthermore, the functions of the genes via the Kyoto Encyclopedia of Genes and Genomes (KEGG) database were annotated using eggNOG-mapper (version 2)19–21. The carbohydrate active enzymes (CAZymes) in the metagenomes were predicted using run-dbcan (v4.0.0) and the dbCAN CAZyme database22.

Assay of β-galactosidase and β-glucosidase activities

The enzyme activities in the ND and RinH groups were measured using the β-galactosidase and β-glucosidase kits. Briefly, the feces were homogenized on ice with an extraction solvent at a ratio of 1:5. The mixture was centrifuged at 15,000×g for 20 min at 4 °C, and the supernatant was supplemented with the reaction solvent. The reaction mixture was incubated for 30 min at 37 °C and its absorbance was subsequently read at 400 nm.

Statistical analyses

The results were presented as the mean ± standard deviation. Statistical differences among the three groups were evaluated using a one-way analysis of variance, followed by a Tukey’s test for post hoc analysis. The statistical differences between the two groups were determined using unpaired t-tests. Correlation analyses (R-values) were conducted using Spearman's correlation coefficient, and the significance was determined using a two-tailed P-value. All analyses were performed using GraphPad Prism 8 (GraphPad Software Inc., San Diego, CA, USA), and P ≤ 0.05 was considered statistically significant.

Results

Pharmacokinetics of ginsenoside Rb1 and its main metabolite

We evaluated the pharmacokinetics of ginsenoside Rb1 and its main metabolite Rd in each group. The calculated pharmacokinetic parameters included AUC, T1/2, Cmax, and Tmax. Figure 1A–C reveal the primary ginsenoside compounds present in plasma include ginsenoside Rb1 and Rd, as well as small amounts of F2, Rg3, CK, and PPD, which is consistent with previous studies23.Fig. 1 (A) Plasma concentration–time curves of ginsenoside Rb1 and its metabolite in the ND group following oral administration of 200 mg/kg ginsenoside Rb1. (B) Plasma concentration–time curves of ginsenoside Rb1 and its metabolite in the RinL group following oral administration of 200 mg/kg ginsenoside Rb1. (C) Plasma concentration–time curves of ginsenoside Rb1 and its metabolite in the RinH group following oral administration of 200 mg/kg ginsenoside Rb1. (D) Plasma concentration–time curves of ginsenoside Rb1 in each group. (E) Plasma concentration–time curves of ginsenoside Rd in each group. (F) Differences in the ratio of Rb1 AUC0-t and Rd AUC0-t between ND and RinH groups (*P ≤ 0.05 vs. ND group).

Further, the plasma concentration–time profiles of Rb1 and its main metabolite Rd in each group were shown in Fig. 1D and E. We calculated the pharmacokinetic parameters of ginsenoside Rb1 and the main metabolite Rd in each group (Table 1). The T1/2 of Rb1 was significantly increased in the RinH group compared with those in the ND group, and the Tmax of Rd and Cmax of Rb1 were found to significantly decrease in the RinH group. Furthermore, the Rb1 AUC0-t/Rd AUC0-t ratio significantly decreased in the RinH group (Fig. 1F). Therefore, systemic exposure of Rb1, accompanied by a significant enhancement in the efficiency of Rb1 conversion to Rd (Rb1 AUC0-t/Rd AUC0-t) were found to significantly change. Based on these results, we further evaluated the mean concentration of ginsenosides in rat feces over 0–72 h after administration of 200 mg/kg ginsenoside Rb1. The mean concentrations of Rb1, Rd, and F2 were significantly decreased in the RinH group, whereas the concentrations of CK and PPD were significantly increased (Table S2). These suggests that significant difference exist in the metabolism of Rb1 by gut microbiome among different groups.Table 1 The main pharmacokinetic parameters of ginsenoside Rb1 and ginsenoside Rd after oral administration of ginsenoside Rb1 (200 mg/kg) in the ND, RinL and RinH groups (mean ± SD, *P ≤ 0.05 vs. ND group).

Group	T1/2(h)	Tmax (h)	Cmax (ng/mL)	AUC0-t (min·ng/mL)	
(n = 8)	Rb1	Rd	Rb1	Rd	Rb1	Rd	Rb1	Rd	
ND	18.36 ± 5.15	36.97 ± 21.22	9.00 ± 2.62	12.00 ± 0.00	1962.99 ± 543.64	978.15 ± 351.68	4,126,606.74 ± 1,156,833.77	1,494,531.09 ± 355,440.33	
RinL	22.79 ± 4.70	58.46 ± 49.12	9.00 ± 2.62	11.00 ± 1.73	2073.24 ± 821.27	1160.97 ± 554.36	4,036,864.45 ± 1,507,944.96	1,530,445.23 ± 480,355.39	
RinH	30.04 ± 10.13*	87.92 ± 107.79	11.50 ± 5.42	10.00 ± 2.14*	1367.04 ± 170.30*	1056.08 ± 316.28	3,251,437.92 ± 462,405.92	2,101,104.24 ± 773,395.33	

Effects of ginsenoside Rb1 on gut microbiome composition

Different ginsenoside Rb1 doses were administered to the SD rats and the changes were analyzed in the gut microbiome using 16S rRNA and metagenomic sequencing to determine the effect of orally administering ginsenoside Rb1 on the composition of the gut microbiome and changes in metabolic function.

Firstly, the feces of rats from the ND, RinL, and RinH groups were subjected to 16S rRNA sequencing. The quality control statistics of 16S rRNA sequencing data were shown in Table S3. In total, 1959 Amplicon Sequence Variants (ASVs) were identified. The gut microbiome composition of rats from different treatment groups were compared based on annotation (Fig. S3). Compared to the ND group, the rats in the RinH group showed a significant decrease in both the ASVs index and the abundance-based coverage estimator indices for α diversity (P ≤ 0.05). The results of the principal coordinate analysis showed significant differences in clustering among the rats in ND, RinL, and RinH groups, indicating between-group diversity. Specifically, the composition of the gut microbiome did not differ between rats in the ND and RinL groups. However, the composition of the gut microbiome significantly varied in RinH group, suggesting that high ginsenoside Rb1 doses affected the composition of the gut microbiome. Furthermore, the microbial composition of the gut microbiomes in the ND, RinL, and RinH groups was similar, with a total of 14 genera having an average relative abundance of ≥ 0.5%, including Romboutsia, Lactobacillus, Limosilactobacillus, and Turicibacter. However, there were significant differences in their proportions (Fig. 2A). Subsequently, differential analyses were performed to identify the differences in the microbiome composition between the three groups (Fig. 2B and C). The findings indicate that Lactobacillus and Bacteroides showed a significant increase in relative abundance in the RinH group compared to when compared to the ND and RinL groups. Meanwhile, the relative abundance of Blautia and Ligilactobacillus increased in the RinH group, while Romboutsia, Limosilactobacillus, and Campylobacter decreased in the RinH group, though these changes were not significant.Fig. 2 (A) Taxonomic composition for gut microbiome composition at the genus level determined by 16S rRNA sequencing. The bacterial taxa with average relative abundance > 0.5% are shown. (B) The average relative abundance of Lactobacillus genus in different groups. (C) The average relative abundance of Bacteroides genus in different groups (**P ≤ 0.01, ****P ≤ 0.001 vs. ND group).

Based on the results of 16S rRNA and pharmacokinetic experiments, it was observed that there were no significant differences in the principal pharmacokinetic parameters of ginsenoside Rb1, nor in the composition of the gut microbiome at the genus level between ND group and RinL group. Consequently, the ND and RinH groups were subjected to metagenomic analysis to distinguish the specific ginsenoside Rb1-induced bacterial species. The total DNA was sequenced and we generated 57.09 GB of metagenomic sequencing data from 16 samples. The quality control statistics of metagenomic sequencing data were shown in Table S4. The results of species composition analyses revealed 20 species with an average relative abundance ≥ 0.5%. The relative abundance of Bacteroides species, including Bacteroides uniformis, Bacteroides caccae, and Bacteroides cellulosilyticus, increased in the RinH group. Additionally, Lactobacillus johnsonii, Parabacteroides distasonis, Phocaeicola vulgatus, and Clostridium innocuum also showed an increase. Meanwhile, Ligilactobacillus murinus, Muribaculum gordoncarteri, and Romboutsia ilealis decreased. Among them, the relative abundance of Bacteroides cellulosilyticus was significantly changed (Fig. 3A and C). The results of Lefse indicated significantly enriched various Bacteroides species, including Bacteroides cellulosilyticus and Bacteroides uniformis, in the RinH group (Fig. 3B). Therefore, a high ginsenoside Rb1 dose enhanced the growth of Bacteroides spp. growth in the rat gut.Fig. 3 (A) Taxonomic composition for gut microbiome composition at the species level determined by metagenome sequencing. The bacterial taxa with average relative abundance > 0.5% are shown. (B) Linear discriminant analysis effect size (LEfSe) demonstrating the taxonomic differences between ND and RinH at the species level. The threshold on the logarithmic LDA score was 2.0, and Kruskal‑Wallis test P ≤ 0.05. (C) The average relative abundance of Bacteroides cellulosilyticus in ND and RinH groups (****P ≤ 0.001 vs. ND group).

The effects of ginsenoside Rb1 on gut microbial functions and enzymes

To further understand the differences in microbial functions induced by high-dose ginsenoside Rb1, we performed functional annotation on the metagenomic data and Lefse. We observed significant differences between the ND and RinH groups in terms of the relative abundance of 13 KEGG metabolic pathways [log linear discriminant analysis (LDA) score > 2.0, P ≤ 0.05] (Fig. 4A). These pathways contain numerous functions related to carbohydrate metabolism. Of these pathways, the enrichment of the galactose metabolism (ko00052) and other glycan degradation pathways (ko00511) were the most significant in the RinH group. These pathways are closely related to carbohydrate metabolism and involve several glycosyl hydrolases, including EC3.2.1.20, EC3.2.1.22, EC3.2.1.23, EC3.2.1.26, EC3.2.1.208, and EC3.2.1.85. Thus, ginsenoside Rb1-induced alterations in the gut microbiome composition are accompanied by significant changes in specific functions related to carbohydrate metabolism.Fig. 4 (A) LEfSe identified the differentially abundant KEGG pathway between ND and RinH group. Threshold on the logarithmic LDA score was 2.0, and Kruskal‑Wallis test P ≤ 0.05. Galactose metabolism (ko00052), other glycan degradation (ko00511), lysosome (ko04142), fatty acid biosynthesis (ko00061), sphingolipid metabolism (ko00600), thiamine metabolism (ko00730), cyanoamino acid metabolism (ko00460), phenylpropanoid biosynthesis (ko00940), biosynthesis of various antibiotics (ko00998), biofilm formation—vibrio cholerae (ko05111), glycosaminoglycan degradation (ko00531), glycosphingolipid biosynthesis—globo and isoglobo series (ko00603), atrazine degradation (ko00791). (B) Percentage of CAZymes classified in the ND group using dbCAN. (C) Percentage of CAZymes classified in the RinH group with dbCAN. (D) Identification of CAZymes with a significant increase or decrease in the RinH group when compared to that in the ND group. (E) Differences in the β-glucosidase (EC 3.2.1.21) activities between the ND and RinH groups. (F) Differences in the β-galactosidase (EC 3.2.1.23) activities between the ND and RinH groups (*P ≤ 0.05, **P ≤ 0.01 vs. ND group).

Subsequently, the Carbohydrate-Active EnZymes (CAZy) database was used to analyze the distribution and abundance of CAZymes in the metagenome data according to the hidden Markov models profile. A total of 28,219 and 25,943 open reading frames containing catalytic domains or carbohydrate-binding modules were identified in the ND and RinH groups, respectively. CAZyme compositions of the two groups were similar (Fig. 4B and C). Glycoside hydrolases (GHs) were the most abundant enzymes catalyzing the hydrolysis of glycosidic bonds in carbohydrates for both groups (ND group: 54.83%; RinH group: 55.06%). The differential analyses between the ND and RinH groups revealed that in enzyme families with an average relative abundance ≥ 0.5%, the GH families of GH2 and GH20 were significantly enriched in the RinH group, whereas GH25 and GT5 were significantly enriched in the ND group. Meanwhile, GH3 was enriched in the RinH group (Fig. 4D).

To further validate the metagenomic results, we evaluated the differences in β-glucosidase (EC 3.2.1.21) and β-galactosidase (EC 3.2.1.23) activities between fecal samples of the ND and RinH groups. The results indicated that both β-glucosidase and β-galactosidase activities were significantly higher in the RinH group than in the ND group, confirming the metagenomic results (Fig. 4E and F). Therefore, high-dose ginsenoside Rb1 induces changes in the gut microbiome composition, resulting in significant changes in the compositions of carbohydrate-degrading enzymes in the gut. These changes may be closely associated with changes in the pharmacokinetics of ginsenoside Rb1 in the body.

Correlation analyses

To investigate the potential relationship between the pharmacokinetic parameters of ginsenoside and the gut microbiome, Spearman’s correlational analyses were performed (Fig. 5A–C). The relative abundance of the gut microbiome at the species level (average relative abundance < 0.5%) was correlated with the pharmacokinetic parameters of ginsenosides Rb1 and Rd. The results revealed that the average relative abundances of Bacteroides cellulosilyticus was significantly correlated with Rb1 T1/2 and Rb1 Cmax (P ≤ 0.05). In particular, the relative abundance of Bacteroides cellulosilyticus was strongly positively correlated with Rb1 T1/2 (R > 0.6) and strongly negatively correlated with Rb1 Cmax (R <  − 0.6). The correlation between the relative abundance of CAZymes (average relative abundance > 0.5%) and the pharmacokinetic parameters of Rb1 and Rd (Fig. 5D–G) revealed that GH2, GH20, and GH92 showed a strong positively correlated with Rb1 T1/2 (R > 0.6). Combining the results of Spearman’s correlational analyses between the relative abundance of CAZymes (average relative abundance > 0.5%) and at the species level (average relative abundance > 0.5%) (Fig. 5H–K) with changes in the gut microbiome and CAZymes, the Bacteroides genus is suggested to be the key bacterial component affecting the pharmacokinetics of ginsenoside Rb1, especially Bacteroides cellulosilyticus. The underlying mechanism was closely associated with the GH family, including GH2, GH20 and GH92. Furthermore, the relative abundance of Bacteroides cellulosilyticus was correlated with mean concentration of ginsenosides in rat feces over 0–72 h (Fig. S4). The results revealed that the relative abundance of Bacteroides cellulosilyticus was strongly negatively correlated with Rb1 C0-72h, Rd C0-72h and F2 C0-72h. Meanwhile, the relative abundance of Bacteroides cellulosilyticus was strongly positively correlated with CK C0-72h, PPD C0-72h. However, no correlation was observed between the relative abundance of Bacteroides cellulosilyticus and Rg3 C0-72h. The results confirm the critical role of Bacteroides cellulosilyticus as a key gut microbiota modulator in the metabolism of ginsenosides in rats.Fig. 5 (A) Spearman correlation of the gut microbiome at the species level (average relative abundance > 0.5%) with the main pharmacokinetic parameters of ginsenoside Rb1 and ginsenoside Rd. (B) Correlational analyses between the relative abundance of Bacteroides cellulosilyticus with Rb1 Cmax. (C) Correlational analyses between the relative abundance of Bacteroides cellulosilyticus with Rb1 T1/2. (D) Spearman correlation of CAZymes (average relative abundance > 0.5%) with the main pharmacokinetic parameters of ginsenoside Rb1 and ginsenoside Rd. (E) Correlational analyses between the relative abundance of GH20 with Rb1 Cmax. (F) Correlational analyses between the relative abundance of GH2 with Rb1 T1/2. (G) Correlation analysis between relative abundance of GH92 with Rb1 T1/2. (H) Spearman correlation of the gut microbiome at the species level (average relative abundance > 0.5%) with CAZymes (average relative abundance > 0.5%). (I) Correlational analyses between the relative abundance of Bacteroides cellulosilyticus with GH92. (J) Correlational analyses between the relative abundance of Bacteroides cellulosilyticus with GH2. (K) Correlational analyses between the relative abundance of Bacteroides cellulosilyticus with GH20.

Discussion

Significant research has been conducted on the pharmacological properties of ginsenoside Rb1. However, numerous studies have evaluated the pharmacokinetics of ginsenosides administered orally to rats, typically finding that their oral bioavailability is below 5%24. Improving the oral bioavailability of ginsenosides has become a research focus. Meanwhile, the metabolism and interrelationships between the gut microbiome and the pharmacokinetics of ginsenoside Rb1 remain to be evaluated. This study investigated the effects of ginsenoside Rb1 on the gut microbiome and related pharmacokinetic via the administration of different ginsenoside Rb1 doses to SD rats. Subsequently, the relationship between the gut microbiome and pharmacokinetics was analyzed.

Pharmacokinetic results indicated that after an oral dose of ginsenoside Rb1 (200 mg/kg) in SD rats, the deglycosylation metabolites (Rd, F2, Rg3, CK, and PPD) along with Rb1 were found in the plasma, and ginsenoside Rd is the main metabolite. A high-dose administration of ginsenosides Rb1 (60 mg/kg) for 30 d resulted in significant changes in pharmacokinetic parameters compared to the ND group, including a significant increase in Rb1 T1/2, and a significant reduction in Rb1 Cmax, Rd Tmax, and the ratio of Rb1 AUC0-t/Rd AUC0-t. Such changes in drug metabolism are often caused by alterations in the liver function or gut microbiome25,26. Previous studies have shown that gut microbiome can interact with drugs, potentially influencing drug metabolism in the human body27. The gut microbiome also secretes various enzymes, and these enzymes play crucial roles in drug metabolism6,28. The intestinal microbiome can contribute to drug metabolism via bacterial metabolism, bacterial transport, and other mechanisms. It has been confirmed in previous literature that specific environmental factors could induce changes in the gut microbiome, leading to changes in the pharmacokinetics of ginsenoside Rb1 and its deglycosylation metabolites2. Meanwhile, the human gut microbiome can influence drug metabolism in the body by affecting hepatic drug-metabolizing enzymes, such as CYP3A29,30. Ginsenosides may have altered kinetics via changes in host drug-metabolizing enzymes due to fluctuations in the gut microbiota. Therefore, we studied the changes in the composition of the gut microbiome after the administration of ginsenoside Rb1 for 30 d. The 16S rRNA and metagenomic analyses revealed that Rb1 administration led to significant changes in both α- and β- diversity of the gut microbiome compared to the ND group, along with a significant increase in the average relative abundance of Bacteroides cellulosilyticus. Bacteroides belong to the phylum Bacteroidetes, class Bacteroidia, order Bacteroidales, and family Bacteroidaceae within the domain Bacteria. It is one of the dominant genera in the human gut microbiome31. The bacterial species of this genus are non-spore-forming, bile-resistant, motile or non-motile, gram-negative, and strictly anaerobic. Due to its diverse carbohydrate enzyme activity, the Bacteroides spp. utilize a wide range of carbohydrates32. Their ability to metabolize carbohydrates can impact the host’s biological utilization of carbohydrates (monosaccharides and polysaccharides). The branched structure of ginsenoside Rb1 contains numerous sugar groups, the carbohydrate-metabolizing capability of Bacteroides spp. is believed to contribute to the modifications in the pharmacokinetics of ginsenoside Rb1. The Bacteroides genus has a wide range of CAZymes, which efficiently degrade of complex polysaccharides. Bacteroides cellulosilyticus, isolated in 2007 from human feces, degrades cellulose better than other Bacteroides species33. Bacteroides belong to the phylum Bacteroidetes, class Bacteroidia, order Bacteroidales, and family Bacteroidaceae within the domain Bacteria. It is one of the dominant genera in the human gut microbiome31. The bacterial species of this genus are non-spore-forming, bile-resistant, motile or non-motile, gram-negative, and strictly anaerobic. Due to its diverse carbohydrate enzyme activity, the Bacteroides spp. utilize a wide range of carbohydrates32. Their ability to metabolize carbohydrates can impact the host’s biological utilization of carbohydrates (monosaccharides and polysaccharides). The branched structure of ginsenoside Rb1 contains numerous sugar groups; therefore, the capability of Bacteroides spp. The ability to metabolize carbohydrates is speculated to affect the pharmacokinetics of ginsenoside Rb1. Studies have also reported that Bacteroides cellulosilyticus has more genes encoding CAZymes than other Bacteroides spp., facilitating its adaptation to various dietary substrates34. Besides Bacteroides species, the relative abundances of Lactobacillus johnsonii, Parabacteroides distasonis, Phocaeicola vulgatus, Clostridium innocuum, Ligilactobacillus murinus, Muribaculum gordoncarteri, and Romboutsia ilealis also changed in the RinH group. These bacteria may also influence the metabolism of ginsenosides in rats. For example, the core genome of Clostridium innocuum includes multiple genes associated with the utilization of various saccharides such as glucose, mannose, fructose, xylose, mannitol, chitin, xylan, and other starches and peptidoglycans. This indicates its ability to directly use plant polysaccharides or by-products of polysaccharide degradation by other commensals35.

Accordingly, we investigated the changes in the gut microbiome function and carbohydrate enzyme compositions between the ND and RinH groups. The results revealed that in the RinH group, galactose metabolism and other glycan degradation were significantly enriched. The galactose metabolism pathway demonstrates the conversion of galactose into glucose, lactose, and various sugar intermediates, which can be utilized in a wide range of metabolic processes. Other glycan degradation pathway specifically includes enzymes that degrade complex carbohydrates such as cellulose, hemicellulose, and pectin into simpler sugars. These enzymes, including glycoside hydrolases, play a crucial role in breaking down these complex glycans, providing essential nutrients and energy sources for the gut microbiota. The galactose metabolism pathway and other glycan degradation pathways involve various glycosyl hydrolases, indicating that a high dose of ginsenoside Rb1 increased the activity of carbohydrate-degrading enzymes in the rat gut. Meanwhile, sphingolipid metabolism and thiamine metabolism were also enriched in the RinH group, indicating that ginsenoside Rb1 can enhance related functions of the gut microbiome. Existing literature suggests that sphingolipids are closely associated with neurodegenerative processes, metabolic disorders, and various cancers36. Thiamine is an essential vitamin for maintaining normal cellular function and is closely related to the proper functioning of the nervous system37. These results may further explain the potential of ginsenoside Rb1 in regulating human health. Further, GHs (EC 3.2.1.X) hydrolyze the glycosidic bond between carbohydrates or components that consist of carbohydrate and non-carbohydrate groups. GHs are classified based on their amino acid sequence similarity, rather than substrate specificity38. The CAZy database assigns GHs to different families (GH1-GH180) according to their sequences and structural similarities39. Previous studies have reported that enzymes from multiple GH families exhibit ginsenoside deglycosylation activities, including GH1, GH3, GH42, GH55 and others (Y. S. Kim & Ma, 2018; Siddiqi, Hashmi, Oh, Chun, & Im, 2019; Siddiqi, Srinivasan, Park, & Im, 2020; Viborg et al., 2017). Various GH families in the RinH group were also significantly enriched, including GH2, GH105, and GH92. Enzymes in the GH2 family primarily include β-galactosidase (EC 3.2.1.23), β-mannosidase (EC 3.2.1.25), β-glucuronidase (EC 3.2.1.31), and β-glucosidase (EC 3.2.1.21). The β-glucosidase enzyme has been reported to effectively hydrolyze the sugar side chain of ginsenoside Rb1, converting into secondary ginsenosides such as Rd, CK, and PPD40. The R1 and R2 groups of ginsenoside Rb1 are connected by glycosidic bonds, making them susceptible to hydrolysis by β-glucosidase in the GH2 family. Therefore, the changes in GH activity due to changes in the gut microbiome could contribute to changes in the pharmacokinetics of ginsenosides Rb1. Further, the GH105 family consists of unsaturated glucuronyl/galacturonyl hydrolases that hydrolyze substrates by hydrating the double bond between the C-4 and C-5 carbons of the terminal monosaccharide28. The enrichment effect of ginsenoside Rb1 on GH105 suggests that ginsenoside Rb1 may also influence the metabolism of other carbohydrates.

Meanwhile, we performed correlation analyses between gut microbiome composition, carbohydrate enzyme composition, and pharmacokinetic parameters of ginsenosides. The results revealed that the average relative abundances of Bacteroides cellulosilyticus were also significantly correlated with Rb1 T1/2 and Rb1 Cmax. GH2, GH20, and GH92 were also significantly correlated with Rb1 T1/2. These findings confirmed that the gut microbiome and GH activity significantly affect the pharmacokinetic processes of ginsenoside Rb1. Furthermore, the correlation analyses between gut microbiome composition and mean concentration of ginsenosides in rat feces over 0–72 h revealed that Bacteroides cellulosilyticus exerts different effects on various types of ginsenosides. The absence of correlation with Rg3 might be a key issue in clarifying the impact of Bacteroides cellulosilyticus on the metabolism of ginsenosides.

Conclusion

In summary, this study clarifies the effects of ginsenoside Rb1 on the gut microbiome and its consequential effects on pharmacokinetics in SD rats. The results revealed that the average relative abundance of Bacteroides cellulosilyticus and specific GH family including GH2, might crucially contribute to the pharmacokinetic behavior of ginsenoside Rb1. These findings provide essential insights into the pharmacokinetic characteristics of ginsenoside Rb1 and can facilitate the applications of ginsenoside compounds. The limitations of this study include the fact that these outcomes were only studied on 8 SD rats in each category. A larger study might be able to more clearly delineate the role of different bacteria. Additionally, some aspects of Rb1 metabolism might be influenced by genetic and microbiome differences between humans and rats. It is important to emphasize that the results obtained in this experiment are based on rat studies. Bacteroides cellulosilyticus should not be applied to human use until its safety and efficacy are confirmed through clinical trials.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72225-1.

Acknowledgements

This research was supported by the Tianjin Synthetic Biotechnology Innovation Capacity Improvement Projects (TSBICIP-CXRC-008 & TSBICIP-CXRC-042), Major Project of Haihe Laboratory of Synthetic Biology (E2M9560201), Strategic Priority Research Program of the Chinese Academy of Sciences (XDC 0110300), and National Natural Science Foundation of China (31200035). We would like to extend our appreciation to Zhi-dan Zhang for her help in the LC-MS analysis. We would like to extend our appreciation to Zhi-dan Zhang for her help in the LC-MS analysis.

Author contributions

Gou-ping Zhao, Xiao-yan You conceived and designed the experiments. Yue Chen, Kang-xi Zhang, Hui Liu, Yue Zhu, Qing-yun Bu, Shu-xia Song and Ya-chun Li performed the animal experiment. Yue Chen and Hui Liu prepared LC–MS samples and performed LC–MS analysis. Yue Chen carried out the data analysis and drafted the manuscript. Xiao-yan You, Hong Zou and Guo-ping Zhao revised the manuscript. The author(s) read and approved the final manuscript.

Data availability

The datasets generated and analyzed during the current study are available in the Genome Sequence Archive (https://ngdc.cncb.ac.cn/bioproject/browse/PRJCA025173) repository (BioProject: PRJCA025173).

Competing interests

The authors declare no competing interests.

Ethical and ARRIVE statement

The experiments adhered to the Ethics Committee at the Animal Center of Nankai University, China (2022-SYDWLL-000569). The study is reported in accordance with ARRIVE guidelines.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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