Male Sprague–Dawley (SD) rats (weighing 180–220 g) were purchased from the Laboratory Animal Research Center of Tongji Medical College of Huazhong University of Science and Technology (Wuhan, China), and were given access to a commercial rat chow diet and tap water. The animals were housed, three per cage, and maintained at 22 ± 2°C and 50–60% relative humidity, under a 12 h light–dark cycle. The experiments were initiated after acclimation under these conditions for at least 1 week. The rats were then randomly divided into three groups: ABT group (antibiotic treatment group), CON group (control group), and FMT group (fecal microbiota transplant group; n = 14 or 15). The experiments were performed in accordance with the “Guiding Principles in the Use of Animals in Toxicology” adopted by the Society of Toxicology (United States) in July 1989 and revised in March 1999.

Antibiotics were administered for 25 days in the drinking water (Staley et al., 2017). Two kinds of antibiotic cocktail were used in the study. Antibiotic cocktail I consisted of neomycin 1 mg/ml, vancomycin 1 mg/ml, and ertapenem 1 mg/ml. Antibiotic cocktail II consisted of clindamycin 1 mg/ml, ampicillin 1 mg/ml, and cefoperazolone 1 mg/ml. And the solutions were freshly prepared every day. Rats in ABT group and FMT group were administrated with antibiotic cocktails I on days 1 to 7 and 19 to 25. And on days 10 to 16, rats were treated with antibiotic cocktail II. There was a two-day break between antibiotic changes. The feces were collected from control rats and vortex into suspension with buffer phosphate solution (200 mg in 1 ml), followed by centrifugation at 2000 rpm for 5 min to obtain supernatant. The rats in FMT group were treated with the supernatant 2 ml per rat for 7 days, followed by 2 weeks of normal feeding for colonization. The experimental timeline is shown in Figure 1. Rats in CON group were fed sterile water instead of the antibiotic cocktail for 46 days in the same environment.

There was a 24-h interval after pretreatment to reduce the occurrence of antibiotic-drug interactions. After the 24 h interval, a single dose of CSA (100 mg/kg), dissolved in virgin olive oil, was administered to the rats via oral gavage. Whole blood samples were collected at 1, 2, 4, 6, 7, 8, 10, 12, 24, 48 and 72 hours after CSA administration. After the last whole blood sample was collected, the rats were sacrificed with pentobarbital sodium by intraperitoneal injection and dissected to collect tissue and cecum contents. All samples were snap frozen after collected and kept at-80°C until further analysis.

The protein precipitation method and high-performance liquid chromatography-mass spectrometry (HPLC–MS) were used to extract and detect CSA in whole blood samples. The method for the determination of CsA was based on our previous developed LC–MS/MS methods (Yang et al., 2018). The linear concentration range of CsA was 5–4,000 ng/ml, and the lower limit of quantification was 5 ng/ml. More details were provided in Supplementary Material.

The blood concentration data were analyzed by the non-compartmental method using Drug and Statistics software (DAS, version 3.2.8, Shanghai BioGuider Medicinal Technology Co. Ltd., Shanghai, China). The peak blood concentration (Cmax) and time to reach Cmax (Tmax) of CsA were acquired directly from the concentration-time curve. The elimination rate constant (Kel) was calculated by log-linear regression of the phase-eliminated data. The area under the plasma concentration-time curve (AUC0-t) from time zero to the time of last measured concentration (Clast) was calculated by the linear trapezoidal rule. The AUC zero to infinity (AUC0-∞) was obtained by the addition of AUC0-t and the extrapolated area determined by Clast/Kel. And the terminal half-life (T1/2) was calculated by 0.693/Kel. The mean residence time (MRT) was calculated by AUMC/AUC, where AUMC represented the area under the first moment versus time curve. Apparent clearance (CL/F) was calculated by Dose/AUC0-∞ and the apparent volume of distribution (V/F) was calculated by CL/Kel.

Total genome DNA from samples was extracted using CTAB/SDS method. DNA concentration and purity was monitored on 1% agarose gels. According to the concentration, DNA was diluted to 1 ng/μL using sterile water. 16S rRNA genes of distinct regions (16SV3-V4) were amplified used specific primer with the barcode. All PCR reactions were carried out with Phusion^® High-Fidelity PCR Master Mix (New England Biolabs). Mix same volume of 1X loading buffer (contained SYB green) with PCR products and operate electrophoresis on 2% agarose gel for detection. Samples with bright main strip between 400 and 450 bp were chosen for further experiments. PCR products was mixed in equidensity ratios. Then, the mixture of PCR products was purified with Qiagen Gel Extraction Kit (Qiagen, Germany). Sequencing libraries were generated using TruSeq^® DNA PCR-Free Sample Preparation Kit (Illumina, United States) following manufacturer’s recommendations and index codes were added. The library quality was assessed on the Qubit^® 2.0 Fluorometer (Thermo Scientific) and Agilent Bioanalyzer 2,100 system. At last, the library was sequenced on an Illumina NovaSeq 6,000 platform and 250 bp paired-end reads were generated. More details were provided in Supplementary Material.

Place 20 mg tissue in round-bottom microcentrifuge tubes or Eppendorf tubes，adding 400 μl of ice-cold lysis buffer (with PMSF) and homogenized with an electric homogenizer. Centrifuge for 20 min at 12,000 rpm at 4°C in a microcentrifuge. Gently remove the tubes from the centrifuge and place on ice, aspirate the supernatant, and place in a fresh tube kept on ice. Concentrations of total cellular protein were determined using a BCA assay kit (Beyotime Biotechnology, China). Total protein samples were analyzed by 8% SDS-PAGE gel and transferred to PVDF membranes by electroblotting. Primary antibodies against CYP3A1 (1/5000, ab22733), CYP3A2 (1/5000, ab195627), UGT1A1 (1/6000, ab194697), P-gp (1/6000, ab170904), BSEP (1/3000, ab217532), MRP2 (1/5000, ab203397) and NTCP (1/5000, ab131084) were probed with proteins on the membrane for 3 h at room temperature, and then incubated with goat anti-rabbit secondary antibody (1/10000, ab6721) for 1 h. Bands were detected by enhanced chemiluminescence (ECL) kit (Beyotime Biotechnology, China). The intensity of the bands of interest was analyzed by Image J software (Rawak Software, Inc. Munich, Germany). The gray scale of internal reference protein was normalized for statistical analysis, and the significance was analyzed by one-way variance test and Dunnett test.

T-test, Wilcox rank-sum test, Tukey test, and Kruskal test were performed to analyze whether the differences between groups were significant. Linear discriminant analysis (LDA) was used to reduce the dimension of the data and evaluate the influence of species with significant differences (LDA Score). Zero-mean normalization was performed to normalize the values of relative abundance using the mean and standard deviation (Z Score). Data of pharmacokinetic, including AUC, Cmax and so on, and western blot were expressed as mean ± SD. Spearman’s correlation coefficient to determine the monotonic correlation. The absolute value of Spearman’s correlation coefficient (ρ) reflected the strength of correlation. Meanwhile ρ > 0 meant a positive correlation, and ρ < 0 meant a negative correlation. Statistical analysis was performed using GraphPad Prism 5.0 software (GraphPad Software, Inc., La Jolla, CA, United States) and SPSS (Statistical Product Service Solutions, IBM, United States). The threshold for statistical significance was set at p < 0.05.

To evaluate the effects of antibiotic treatment and fecal transplantation on the gut microbiota of rats, Sequencing of the bacterial ribosomal RNA (16S rRNA) was performed to compare the bacterial populations on the caecum content. The results revealed that the species richness and diversity were lower after antibiotic treatment than in the CON group, but the depletion was reversed after feces microbiota transplantation (Figure 2A).

The difference in the number of OTU was small (Figure 2B). Moreover, compared with the rats in the ABT, the composition of intestinal microbes of rats in the FMT was more similar to that in the CON group according Figure 2C; Supplementary Figure S1. Results of principal coordinate analysis (PCoA) showed that the species composition of ABT group was significantly different from that of CON group and FMT group (p < 0.01, Figure 2C). Differences at phylum and genus levels were also analyzed (Figures 2D,E). At the phylum level, several bacteria were altered by both interventions, with antibiotics causing a reduction of Firmicutes/Bacteroidetes ratio. Firmicutes and Bacteroidetes were dominant in CON group and FMT group (Figure 2D). However, in ABT group, the relative abundance of Proteobacteria and Verrucobacteria increased significantly, which became the four major phyla together with Bacteroidetes and Firmicutes. The comparison at the genus level was based on individual rats (Figure 2E). The relative abundance of top 30 was shown in different colors in the bar chart, and the remaining genus were assigned to others. The proportion of the top 30 was higher in the ABT group, which reflected the composition of bacterial genera in the ABT group was simpler, compared with the other two groups. In ABT group, relative abundance of Akkermansia, Parabacteroides and Enterobacter increased, while the relative abundance of Lachnospiraceae_NK4A136_group, Prevotella Lactobacillus and Alistipes decreased (Supplementary Figure S2).

We compared the relative abundance of each genus by t-test between groups, and p < 0.05 was considered to be significant difference (Figure 3). Compared with CON and FMT group, the relative abundance of genera including Enterobacter, Klebsiella, Parasutterella, Parabacteroides, Akkermansia, Escherichia-Shigella, Veillonella, Bacteroides and Cellulosilyticum was significantly increased in ABT group. And the relative abundance of Prevotella_9, [Eubacterium]_xylanophilum_group, Lachnospiraceae_NK4A136_group, Colidextribacter, Alloprevotella, Phascolarctobacterium, Christensenellaceae_R-7_group, Lactobacillus, Limosilactobacillus, Lachnoclostridium, UCG-005, and Ruminococcus was markedly decreased in ABT group, compared with the other two groups. Despite the transplantation of feces from the CON group, the composition of the cecal contents of the FMT group was not the same as that of the CON group. Compared with CON group, the relative abundance of Prevotella and Romboutsia decreased, while the relative abundance of Roseburia and Anaerovirio increased in FMT group. This might indicate that compared with Prevotella and Romboutsia, Roseburia and Anaerovirio had stronger colonization ability in the process of feces microbiota transplantation.

After oral administration of CSA, blood concentrations of CSA were determined at each time point in the three groups (Figure 4A). The pharmacokinetic parameters were analyzed by non-atrioventricular model simulation (Figure 4C). Compared to the CON group, the blood concentration of CSA was significantly increased by antibiotic treatment, indicating that the bioavailability of CSA was improved by interfere with intestinal microbial composition. And the pharmacokinetic profiles of CSA in FMT group fell between the ABT group and the CON group. Fecal transplantation could reverse the increased bioavailability of CSA caused by antibiotic treatment.

In addition, statistical analysis of pharmacokinetic parameters showed that AUC_(0-t), AUC_(0-∞) and C_max had statistical differences (p < 0.05; Figure 4B). Compared with the CON group, the AUC_(0-T), AUC_(0-∞) and C_max of the ABT group were significantly increased. Antibiotic-induced microbiota depletion led to the increase in AUC_(0-t), AUC_(0-∞) and C_max of CSA by 155.6, 140.8 and 154.8%, respectively. After intestinal microbiota reconstruction by feces microbiota transplantation, the increased AUC_(0-T), AUC_(0-∞) and C_max were significantly reduced and basically returned to the control group level. Half-lives of CSA were not statistically different among the three groups.

After demonstrating that changes in the microbiome occurred in parallel with changes in the systemic absorption of CSA (Figure 3), we compared whether the relative abundance of specific taxa was associated with pharmacokinetic parameters [including AUC_(0-T), Cmax, MRD (0-T), t_1/2 and CLz/F] in three groups (Figure 5; Supplementary Figure S3). Spearman correlation analysis was performed to assess the correlation between relative abundance of genera in Figure 3 and CSA pharmacokinetics (p < 0.05 was considered to be relevant). Supplementary Figure S3 showed the scatter plots of 15 genera that were correlated with AUC _(0-T). The genera with the|ρ|value exceeding 0.4 were Alloprevolleta, Oscillospiraceae_UCG_005, Parasutterella and Eubacterium_xylanophilum (Figure 5; Supplementary Figure S3). The relative abundance of Akkermansia, Morganella, Parasutterella, Parabacteroides, Eeterobacter, Escherichia Shigella, Klesiella and Proteus positively and significantly correlated with AUC_(0-T) and C_max of CSA (Figure 5). Eubacterium Xylanophilum group, Desulfovibrio, Alloprevotella, Alistipes, Phascolarctobacterium, UCG 005, NK4A214 group, and Christensenellaceae R⁻⁷ group were negatively correlated with AUC_(0-T) and C_max of CSA (Figure 5).

To test whether the changes of intestinal microbiota had an effect on the drug metabolic enzymes (including CYP3A1, CYP3A2, UGT1A1) relevant for the metabolism of CSA in the liver and intestine, western blotting was performed to examine the level of protein expression (Figure 6; Supplementary Figure S4). CYP3A1 and CYP3A2 are the main metabolic enzymes of CSA, which are isomers of human CYP3A4 and CYP3A5 (Martignoni et al., 2006). Compared with the CON group, the expression of CYP3A1 of the ABT group decreased by 35.7% while there was no marked difference between the FMT group and CON group. Hepatic protein expression of UGT1A1, a major two-phase metabolic enzyme of CSA, was decreased by 66.0% in the ABT group and 30.8% in the FMT group compared to the CON group (Figure 6A). Contrary to the results in liver, there was no difference in the expression of UGT1A1 and CYP3A1 in the small intestine among the three groups, and the protein expression of CYP3A2 in the ABT groups was higher than that in the CON group (Supplementary Figure S4). As liver played a more important role in the metabolism of CSA than intestine, the alteration of hepatic metabolic enzymes was consistent with our pharmacokinetic results.

P-gp and MRP2 were important efflux transporters in the transport of CSA. The hepatic protein expression of P-gp of the ABT group and the FMT group decreased by 59.9 and 34.8% in comparison to the CON group (Figure 6A). Consistent with liver, the protein expression of the efflux transporters P-gp and MRP2 was decreased in the ABT group and reversed in the FMT group (Supplementary Figure S4). Efflux proteins located at the apical membrane, which include P-gp and MRP2, may drive compounds from inside the cell back into the intestinal lumen or biliary excretion, preventing their absorption into blood. In ABT group, the down-regulation of efflux transporters both in the liver and intestine resulted in the increased bioavailability of CSA.

Previous studies have suggested that intestinal microbiota regulate the bile metabolism (Sayin et al., 2013; Ramírez-Pérez et al., 2017; Ma et al., 2018; Winston and Theriot, 2020). For this reason, we assessed protein expression of two key transporters for the secretion of bile acids from hepatocytes into bile, BSEP and NTCP. Western blotting results showed the protein expression of BSEP and NTCP were not affected by either microbiome targeted intervention (Figure 6). However, compared with CON group, the hepatic level of nuclear protein FXR and PXR was significantly reduced in ABT and FMT group (Supplementary Figure S5). The protein expression of these two nuclear receptors in ABT group was lower than FMT group, but there was no significant difference.

In this study, liver and proximal colon sections were stained with H&E staining to observe the effects of intestinal microbiota intervention on physiologic morphology of liver and proximal colon (Figure 7). Figures 7A–C showed H&E staining of liver sections of the CON group, ABT group, and FMT group, respectively. According to H&E staining results, no significant morphological change was found among the three groups. H&E staining results showed that liver cells were clearly defined, and the cells were evenly and neatly distributed, without obvious aggregation of inflammatory cells among the three groups. Figures 7D–F showed the proximal colon section. The H&E pathological section of rat in CON, ABT and FMT groups did not reveal disappeared crypts and broken structure of colon wall (Figures 7D–F). There was no marked histological damage in livers and colons in three groups.