Influence of the gut microbiota on the pharmacokinetics of tacrolimus in liver transplant recipients: insights from microbiome analysis

Introduction: Tacrolimus is crucial for immunosuppression after liver transplantation, but its pharmacokinetics vary markedly among individuals. Emerging evidence suggests that the gut microbiota may influence its metabolism, although the underlying mechanisms remain unclear.

Methods: This study analyzed the fecal microbiota from 38 postliver transplant patients and 31 healthy controls via 16S rDNA amplicon and shotgun metagenomic sequencing. Patients were stratified into three groups on the basis of oral tacrolimus dosage and blood concentration: LDLBC (low dose, low blood concentration), LDHBC (low dose, high blood concentration), and SDLBC (standard dose, low blood concentration).

Results: Posttransplant patients presented significantly reduced gut microbial diversity. Specific bacterial taxa, including Enterococcus raffinosus, Intestinibacter bartlettii, and Bacteroides fragilis, were enriched in patients with lower tacrolimus blood concentrations. In contrast, Phascolarctobacterium faecium and Streptococcus salivarius were associated with increased drug levels. Functional analysis revealed differences between patient subgroups in ATP-binding cassette (ABC) transporters and drug efflux pumps, suggesting a potential microbial influence on tacrolimus absorption and metabolism. Additionally, antibiotic resistance genes were more abundant in patients with lower tacrolimus blood concentrations, particularly in the Escherichia coli-enriched groups.

Discussion: These findings underscore the influence of the gut microbiota on tacrolimus pharmacokinetics and support the potential of microbial composition as a biomarker for optimizing immunosuppressive therapy.

1 Introduction

Liver transplantation is an effective treatment for end-stage liver disease, yet rejection remains an independent risk factor for long-term survival (Kosuta et al., 2024). Immunosuppressive drugs effectively prevent and treat rejection, and most liver transplant centers in China currently favor a multidrug regimen based on calcineurin inhibitors. Tacrolimus is the most commonly used calcineurin inhibitor. Following oral administration, tacrolimus is primarily absorbed in the small intestine (Mohammed Ali et al., 2023) and metabolized in the liver by cytochrome P450 enzymes (CYPs), mainly CYP3A4 and CYP3A5 (Tillman et al., 2023). Additionally, the efflux pump transporter ATP-binding cassette subfamily B member 1 (ABCB1) in the cell membrane can transport tacrolimus back into the intestinal lumen (Henkel et al., 2023). Owing to its narrow therapeutic window, complex drug interactions, and poor correlation between dose and blood concentration, tacrolimus dosing requirements, efficacy, and toxicity vary significantly among patients (Campagne et al., 2019). Consequently, physicians routinely monitor tacrolimus blood concentrations, although individual variability may result in subtherapeutic or supratherapeutic concentrations that can lead to rejection or transplant complications (Tremblay et al., 2017; Campagne et al., 2019). The most crucial cause of interindividual variability is the difference in CYP3A5 genotype (Csikany et al., 2021). However, most liver transplant centers do not routinely test CYP3A5 genotypes to guide immunosuppressant therapy.

Recent research on the gut microbiome suggests that microbial diversity may contribute to interindividual variability in tacrolimus pharmacokinetics and pharmacodynamics (Guo et al., 2019). Immunosuppressants after liver transplantation can also alter the intestinal microecology (Sucu et al., 2023). Disturbance of the intestinal microecology can lead to the release of inflammatory factors, liver function damage, and graft dysfunction and even correlate with increased mortality after transplantation (Swarte et al., 2022). Some studies have shown that Subdoligranulum is associated with a significantly reduced risk of death after liver transplantation, whereas Enterococcus is linked to an increased risk of all-cause mortality (Choudhary et al., 2017).

Few studies have examined the relationship between immunosuppressants and intestinal microecology in solid organ transplantation, and most remain at the animal research stage (Jiang et al., 2018; Degraeve et al., 2023). Thus, the underlying mechanism requires further elucidation. For example, an increased tacrolimus dose during the first month after kidney transplantation is positively correlated with the abundance of Faecalibacterium prausnitzii in the first postoperative week (Lee et al., 2015). In vitro experiments have subsequently shown that most Clostridiales bacteria can metabolize tacrolimus (Guo et al., 2019). In our study, we combined 16S rDNA amplicon sequencing with shotgun metagenomics sequencing to further analyze the changes in the intestinal flora after liver transplantation. We also investigated the correlations between the gut microbiota and clinical characteristics.

2 Materials and methods

2.1 Study design

We prospectively collected fecal samples from 38 patients who underwent liver transplantation at the First Hospital of Shanxi Medical University from 2021 to 2023. In addition, 31 healthy adults were recruited as the control group (CG). We classified the 38 postliver transplantation patients into three groups according to Chinese immunosuppressive therapy guidelines (Transplantation, 2021): the LDLBC group (low dose, low blood concentration, n = 19), the LDHBC group (low dose, high blood concentration, n = 9) and the SDLBC group (Standard dose, low blood concentration, n = 10). According to the guidelines (Transplantation, 2021), a tacrolimus dose of 0.05–0.15 mg·kg⁻¹·d⁻¹ is recommended for oral combination therapy in two divided doses. None of the patients included in this study exceeded the recommended oral dosage range. We classified patients with oral tacrolimus doses below the recommended range into the low-dose group, whereas the other patients were categorized into the standard-dose group. The target blood concentration ranges of tacrolimus are 8–12 ng/mL during the first 3 months posttransplant, 7–10 ng/mL between 3 and 6 months, 6–8 ng/mL between 6 and 12 months, and approximately 5 ng/mL after 12 months. For the postoperative period, we defined patients with blood tacrolimus concentrations below the reference range as the low blood concentration group and those with concentrations equal to or above the lower limit of the reference range as the high blood concentration group. Clinical data, including medical history, laboratory test results, clinical manifestations, and disease classifications, were obtained from medical records and the laboratory information system. We first performed 16S rDNA amplicon sequencing analysis, followed by the discriminant method in microPITA software (v1.0.1) to select six representative samples from the center of each group (Supplementary Figure S1A). These selected samples were then further subjected to shotgun metagenomic sequencing (Figure 1). In addition, we conducted an association analysis between clinical characteristics and the gut microbiota and developed a predictive model to assess the impact of clinical features and the gut microbiota on tacrolimus intrapatient variability (Tac-IPV).

Figure 1

Figure 1. Research methods and processes.

The detailed methods for fecal sample collection, 16S rDNA amplicon sequencing, metagenomic sequencing, and bioinformatics analysis employed in this study are provided in the online Supplementary materials and methods.

2.2 Statistical analysis

Clinical data were analyzed via chi-square tests or one-way ANOVA to assess intergroup differences, with multiple comparisons performed via Tukey’s multiple comparison tests or the Kruskal–Wallis test. Statistical analyses were conducted via QIIME2 (v202202), LEfSe (v1.1.01), Perl (v5.26.2), and R software (v4.0.3). Feature selection was performed via the random forest and SHAP methods. Model prediction analysis was performed via a combination of cross-validation, bootstrap, random forest, and extreme gradient boosting (XGBoost) methods. A p value < 0.05 was considered statistically significant.

3 Results

3.1 Patient information

Our grouping is justified by the differences in tacrolimus dosage (TACD) and blood drug concentration (BCD) within the study group (Table 1). The healthy group exhibited a significantly lower average age than the study groups did (p < 0.05). Except for preoperative aspartate aminotransferase (AST) (LDHBC vs. LDLBC, p < 0.05), no significant differences were observed in the collected baseline clinical data (Table 1; Supplementary Table S1). Notably, the LDLBC group had the highest proportion of patients with liver malignancies, whereas the SDLBC group had the greatest proportion of patients with ascites. Additionally, the LDHBC group presented no complications such as hypertension, diabetes, or malignancies, and these differences may be worth our consideration and further exploration. We introduced the Tac-IPV index to better elucidate the variability between the tacrolimus oral dose and blood concentration. We quantified this variability via the coefficient of variation (CV), a widely adopted metric (Xie et al., 2024). The results demonstrated that the CV in the LDHBC group was significantly lower than that in the LDLBC group (p < 0.05).

Table 1

Table 1. Cohort characteristics.

3.2 Analysis of the species community composition

In our research, compared with the 84 amplicon sequence variants unannotated via 16S rDNA amplicon sequencing, 87,033 gene catalogs (unigenes) were unannotated via metagenomic sequencing. Overall, The top five phyla identified via 16S rDNA amplicon and metagenomic sequencing accounted for 99.80 and 88.78% of the total annotated microorganisms, respectively (Figure 2A; Supplementary Table S2). Metagenomic sequencing characterizes the shared and unique gene information among different groups. The CG group had the most specific gene sequences, followed by the SDLBC group, and the LDLBC and LDHBC groups had almost equal numbers of specific gene sequences (Figure 2B).

Figure 2

Figure 2. Changes in the intestinal microbiota structure after liver transplantation. (A) Phylogenetic tree of the top 100 genera based on 16S rDNA amplicon sequencing. The colors of the sectors represent phyla, and the stacked bar charts on the outer rings indicate bacterial genus abundance across groups. The left legend denotes group information, whereas the legend shows phylum level classification. (B) Venn diagram of the metagenomics sequence data. (C) Differences in the observed feature indices across all samples. (D) Differences in the Chao1 index between the four groups based on 16S rDNA amplicon sequencing. (E) NMDS analysis of weighted Unifrac distances between the four groups on the basis of 16S rDNA amplicon sequencing. Each point represents a sample; distances between points reflect differences in community structure (Stress < 0.2 indicates reliable NMDS analysis). (F) Beta–Wilcox weighted Unifrac differences between the four groups were determined via 16S rDNA amplicon sequencing. (G) Differences in the Simpson index at the species level between groups, based on metagenomic sequence data (*p < 0.05, **p < 0.01, ***p < 0.001).

3.3 Changes in the intestinal microbial structure after liver transplantation

We first used 16S amplicon sequencing to characterize the diversity of the intestinal flora in patients after liver transplantation. The observed feature indices indicated that the sequencing depth of the 16S rDNA amplicon sequencing was reasonable (Figure 2C). The species diversity of the liver transplantation groups (LDLBC, LDHBC, and SDLBC groups) was significantly lower than that of the CG group (p < 0.001) (Figure 2D). The results of deep metagenomic sequencing were also intuitive (GENEBOX diagram) (Supplementary Figure S1B). Differences in the abundance and evenness of the gut microbiota between liver transplant recipients and healthy individuals may be related to intestinal congestion caused by preoperative cirrhosis and portal hypertension (Wu et al., 2023), brief anhepatic periods during surgery (Lai et al., 2022), and perioperative antibiotic use (Fu et al., 2023). The nonmetric multidimensional scaling (NMDS) results also support the uniqueness of the CG group, and the spatial distance difference among the three groups after liver transplantation was relatively small (Figures 2E,F). In the metagenomic sequencing results, the Simpson index was significantly different among the LDHBC, LDLBC, and CG groups (p < 0.05). There were significant differences between the LDLBC and SDLBC groups (p < 0.05), suggesting that the LDLBC group is unique and that its dominant species may not have a high abundance (Figure 2G). To further verify the uniqueness of the LDLBC group, we performed NMDS analysis on the metagenomic sequencing results. The results at the phylum, class, order, family, genus, and species levels were consistent with those of 16S rDNA amplicon sequencing (all with stress < 0.2) (Supplementary Figure S1C). The CG group presented the greatest distance from the other groups at each classification level, followed by the LDLBC group.

3.4 Analysis of species differences between groups

Linear discriminant analysis effect size (LEfSe) analysis (Linear Discriminant Analysis, LDA > 4) based on metagenomic sequencing revealed statistically significant differences among the CG, LDLBC, LDHBC, and SDLBC groups (Figures 3A,B; Supplementary Table S3). In the SDLBC group, Intestinibacter, Intestinibacter bartlettii, and Enterococcus raffinosus were dominant. The abundance of Phascolarctobacterium faecium was significantly increased in the LDHBC group (LDA = 4.53, p < 0.05). Phascolarctobacterium faecium promotes digestion, nutrient absorption, immune regulation, and antibacterial activity and maintains the gut microbiota balance and host health (Wu et al., 2017; Ikeyama et al., 2020). Furthermore, comparisons between the LDLBC and LDHBC groups revealed that Streptococcus and Streptococcus salivarius were more abundant in the LDHBC group (LDA = 4.59, p < 0.05) (Supplementary Figure S2B; Supplementary Table S3). Similarly, 16S rDNA amplicon sequencing also revealed a significant increase in the abundances of Streptococcaceae and Streptococcus in the LDHBC group. In the LDLBC group, Escherichia differed significantly from that in the other groups (LDA = 4.19, p < 0.05). Moreover, subgroup analysis revealed that both Escherichia and Escherichia coli were more abundant in the LDLBC group than in the LDHBC (Supplementary Figure S2B; Supplementary Table S3) and SDLBC groups (Supplementary Figure S2C; Supplementary Table S3). 16S rDNA amplicon sequencing revealed that the LDLBC group presented statistically significant differences in the abundances of Proteobacteria, Gammaproteobacteria, Enterobacterales, Enterobacteriaceae, and Escherichia Shigella (the old NCBI hierarchical classification; Supplementary Figure S2A; Supplementary Table S4). These successive taxonomic units showed significant differences, indicating that the observed variations originated at lower classification levels. Similarly, the CG group demonstrated significant differences across taxonomic ranks from high to low levels. For example, Bacteroidaceae, Phocaeicola, Bacteroides stercoris, Phocaeicola vulgatus, and Alistipes putredinis were significantly different (Figure 3A; Supplementary Table S3).

Figure 3

Figure 3. Species differences between groups annotated by metagenomic sequencing. (A) Characteristic bacteria were identified between groups via LEfSe analysis on the basis of metagenomic sequence data (LDA > 4). (B) Clade diagram of species that differ between groups. Circles represent taxonomic levels from phylum to species, with circle size indicating abundance. Yellow circles represent species with no significant differences, whereas colored circles indicate distinct species biomarkers. (C) Bar plot showing the abundances of the top 10 species by abundance at each taxonomic level (class, order, family, genus, and species) on the basis of the metagenomic sequencing data. (D) Heatmap of gene abundance differences between samples at the species level, based on GENENUMS analysis.

3.5 Relative abundance differences observed between groups

We considered that differences in the relative abundance of species could better reflect intergroup species differences when a small sample size was used. We performed the analysis primarily on the basis of the metagenomic sequencing results (Figure 3C), using 16S rDNA amplicon sequencing as a reference. At the class level, the relative abundance of Bacteroidia was greater in the CG group than in all the other three groups (the genera Bacteroides stercoris, Phocaeicola vulgatus, and Alistipes putredinis were all dominant in the CG group). Our results are consistent with those of previous studies, which revealed that the abundance of Bacteroidia in cirrhotic mice was lower than that in healthy mice (Chen R. et al., 2024). Bacilli (containing Streptococcus salivarius predominant in the LDHBC group, and Enterococcus raffinosus predominant in the SDLBC), as potential probiotics, were relatively low in the CG group and relatively high in the other three groups, which may be closely related to the use of oral probiotic preparations during the postoperative recovery period (Tang et al., 2024). The most abundant bacterial class in the LDLBC group was Gammaproteobacteria (with the subordinate taxa Enterobacteriaceae and Escherichia coli being dominant in LDLBC). Erysipelotrichia was the most abundant class in the LDHBC group (with the subordinate taxon Coprobacillaceae being dominant in LDHBC). Consistent with the class level, at the order level, the relative abundance of Bacteroidales was greater in the CG group than in the other three groups; the relative abundance of Lactobacillales was low in the CG group but high in the other three groups; Enterobacterales was the greatest in the LDLBC group; and Erysipelotrichales was the most abundant in the LDHBC group. At the family level, we observed that Enterobacteriaceae had the highest relative abundance in the LDLBC group. Interestingly, at the order level, the Lactobacillales with high relative abundance in the three groups subjected to liver transplantation presented different high-abundance microbial taxa in their subordinate families: the relative abundance of Lactobacillaceae was the highest in the LDLBC group; the relative abundance of Enterococcaceae was the highest in the SDLBC group; compared with the other groups, Streptococcaceae was more abundant in the LDHBC group. These flora may play different roles in the absorption or metabolism of tacrolimus. Consistent with the 16S rDNA amplicon sequencing results (Supplementary Figure S2D), the metagenomic sequencing results at the genus level revealed that Faecalibacterium was highly abundant in the CG group (Figure 3C). Although the difference in the relative abundance of species level bacteria gradually decreased, Escherichia coli still had a high relative abundance in the LDLBC group. Escherichia coli is an opportunistic pathogen that can ferment various sugars to produce acid and gas, which is usually not conducive to the postoperative recovery of patients (Han et al., 2024). We also obtained species level sequencing results consistent with the 16S rDNA amplicon sequencing results (Supplementary Figure S2D), and Bacteroides fragilis was significantly more abundant in the SDLBC group (Figure 3C). Bacteroides fragilis has beneficial functions such as immune regulation. However, it may also become an opportunistic pathogen under specific conditions, which deserves attention (Chen Z. et al., 2024).

The heatmap results revealed that the relative abundance of Faecalibacterium prausnitzii was significantly greater in the CG group than in the other groups (Supplementary Figure S1D; Supplementary Table S5). In terms of gene counts at the species level, Faecalibacterium prausnitzii was present almost exclusively in the CG group and at relatively high counts (Figure 3D). Our results differ from those of a previous study, which revealed that patients requiring increased oral tacrolimus doses had higher abundances of Faecalibacterium prausnitzii in their gut microbiota. This discrepancy may be due to our smaller sample size; another possibility is that the previous study was conducted within 1 month postoperatively when the patients’ conditions were not entirely stable and their digestive function had not fully recovered.

3.6 Functional annotation

3.6.1 The CAZy database

From the perspective of gene annotations at various levels, the microbial carbohydrate metabolism enzymes of liver transplant recipients were significantly lower than those of healthy individuals (Supplementary Figure S3). This result may be due to the decreased abundance of the intestinal microbiota after liver transplantation. These enzymes belong mainly to glycosyl transferase family 2 (GT2) and glycosyl transferase family 4 (GT4) (Supplementary Figure S3A). Both are involved in catalyzing the transfer of glycosyl groups from one compound to another, thereby participating in protein glycosylation and the synthesis and degradation of microbial cell walls or the extracellular matrix. The synthesis and modification of biomolecules are essential for microorganism survival, reproduction, and environmental adaptation (Bretagne et al., 2024) (Supplementary Figure S3B). These enzymes are indispensable for maintaining normal physiological function, cell communication, the immune response, and other critical processes in living organisms.

3.6.2 The EggNOG database

The EggNOG database is an essential tool and database for functional annotation and gene family analysis (Supplementary Figure S4A). The functional annotation at the OG level (ortholog group ID) revealed that the LDHBC group annotated a greater number of COG1136 (ATP-binding cassette transporter (ABC transporter); lipoprotein transporter activity) and COG1131 (ABC transporter; ATPase activity) functional groups (Supplementary Figure S4B; Supplementary Table S6). The SDLBC group included COG1132 (ABC transporter; ATPase activity, coupled to transmembrane movement of substances) and ENOG501TP0B (ABC transporter) (Supplementary Figure S4B). These protein groups are all related to drug transport (Montanari and Ecker, 2015). However, at the same time, the SDLBC group presented relatively high abundances of the COG0577 (efflux transmembrane transporter activity) and COG0534 (Mate efflux family protein; drug transmembrane transporter activity) functional groups (Supplementary Figure S4B), which may be one of the reasons for the low serum concentration of tacrolimus (Demel et al., 2008). We also found that histidine kinase activity was relatively low in the LDLBC group and high in the SDLBC group (Supplementary Figures S4B,C). Previous studies have shown that Lactobacillus can inhibit the growth of pathogens by producing bacteriocins (Younas et al., 2022). Bacteriocin production depends on the induction peptide (IP) acting on histidine protein kinase (HPK) to activate the network regulation of cytoplasmic response regulator (RR), a two-component regulatory system. Previous studies have shown that acetate promotes bacteriocin synthesis in Lactobacillus plantarum (Meng et al., 2021a). Acetate activates Lactobacillus plantarum, Lactobacillus sakei, and Lactobacillus rhamnosus to produce plantaricin E, plantaricin F, sakacin A, and rhamnosin B by activating histidine kinase activity (Meng et al., 2021b). Despite the higher abundance of Lactobacillus in the LDLBC group and the higher histidine kinase activity in the SDLBC group, both groups may have produced fewer bacteriocins, which remains to be further verified. It is also possible that the lower production of bacteriocins in the LDLBC and SDLBC groups was related to the lower serum tacrolimus concentrations.

3.6.3 The KEGG database

Our functional annotation results revealed that more than half of the genes were annotated to the metabolism pathway (Figure 4A). The Environmental Information Processing, Cellular Processes, Human Diseases, and Metabolism functional groups were relatively abundant in the LDLBC group (Supplementary Figure S5A; Supplementary Table S7). Metastats analysis revealed that 13 level 2 functions significantly differed between the CG and LDLBC groups (Supplementary Figure S6A; Supplementary Table S8). The more accurate classification of functional notes suggests that relatively high functions in the LDLBC group include drug resistance, antimicrobial, infectious disease, bacterial, and cellular community-prokaryote functions (Supplementary Figure S6B). Cellular community-prokaryotes were annotated as biofilm formation - Escherichia coli at Level 3 (Supplementary Figure S5B; Supplementary Table S7), possibly due to the predominance of Escherichia coli in the LDLBC group. The SDLBC group presented greater abundances of K02003 (putative ABC transport system ATP-binding protein), K01990 (ABC-2 type transport system ATP-binding protein), and K02004 (putative ABC transport system permease protein) within the KEGG ortholog group (KO) (Figure 4B; Supplementary Table S7). Furthermore, K18888 and K18887 (ATP-binding cassette, subfamily B, multidrug efflux pump) were also significantly enriched within the SDLBC group (Figure 4B). We also found that K01992 (ABC-2 type transport system permease protein) was highly abundant in the LDHBC group (Figure 4B). Consistent with the annotation results of the CAZy database, the protein groups responsible for transport function were all low in abundance in the LDLBC group, which may explain why an appropriate dose of oral tacrolimus resulted in lower blood concentrations. However, the specific mechanism underlying the high abundance of this multidrug efflux pump is unknown. In addition, the relative abundances of transposase and putative transposase were significantly greater in the three liver transplant groups but lower in the CG group (Figure 4B), suggesting that microbial gene expression regulation is active in the guts of liver transplant recipients. Our analysis of functional abundance at the module level revealed that the intergroup difference between the LDLBC and SDLBC groups was more significant than the intragroup difference. Moreover, principal co-ordinates analysis (PCoA) based on the Bray–Curtis distance revealed that the distance between the SDLBC and LDLBC groups was the greatest (Figures 4C,D). To further clarify the functional differences between groups, we performed LEfSe analysis on the functional annotations at each level (Figure 4E; Supplementary Table S9). The LEfSe analysis (LDA > 3) results indicated that the findings were broadly consistent with those reported above. Compared with those in the LDLBC and SDLBC groups, cell growth and death were significantly higher in the SDLBC group (LDA = 3.23, p < 0.05), and the metabolism of other amino acids was significantly greater in the LDLBC group (LDA = 3.33, p < 0.05). The differences in CG vs. LDLBC vs. LDHBC vs. SDLBC at the KO level revealed that the LDLBC group presented a greater abundance of K07484 transposase (LDA = 3.10, p < 0.05). This result was consistent with the greater abundance of the LDLBC group COG2963 (transposase activity) function group, whose genes were annotated in the EggNOG database. At the module level, through comparative analysis of the differences between LDLBC vs. LDHBC vs. SDLBC, we found that the different functions of the SDLBC group were M00026 (histidine biosynthesis, 5-phospho-alpha-D-ribose 1-diphosphate → histidine), M00035 (methionine degradation), M00159 (V/A-type ATPase, prokaryotes), and M00023 (tryptophan biosynthesis, chorismite → tryptophan).

Figure 4

Figure 4. Function Comments on the basis of the KEGG database. (A) Numbers of annotated genes in the KEGG database (six function classes). (B) Cluster analysis of functional relative abundance at the KEGG ortholog (KO) level. (C) Anosim was used to compare the functional abundance between the two groups at the module level (KEGG module number). An R value > 0 indicates that between-group differences are more significant than within-group differencesare, whereas an R value < 0 suggests the opposite. A p value < 0.05 indicates statistical significance. (D) Dimensionality reduction analysis of Bray–Curtis distance (PCoA) on the basis of module level functional abundance. (E) LEfSe analysis of functional differences based on the KEGG database (LDA > 3) (Level 2: LDLBC vs. SDLBC; KO: CG vs. LDLBC vs. SDLBC vs. LDHBC; Module: LDLBC vs. SDLBC vs. LDHBC; includes an abundance clustering heatmap for significantly different functions).

Another significant finding from the KEGG database annotation was pathwaymaps (Supplementary Pathwaymaps). By comparing different groups, we observed that the gut microbes in the LDHBC group play a diminished role in the pathway promoting steroid production. Previous studies have shown that glucocorticoids induce CYP3A4 and ABCB1 in drug interactions, leading to a significantly higher tacrolimus dose requirement after kidney transplantation when glucocorticoids are coadministered (Anglicheau et al., 2003). The locus of enterocyte effacement encoded effectors, which are specific to the LDLBC group and play a vital role in intestinal pathogens such as enteropathogenic Escherichia coli (EPEC) and enterohemorrhagic Escherichia coli (EHEC). Prior reports have indicated increased intestinal Escherichia coli after liver and kidney transplantation and urinary tract colonization by pathogenic Escherichia coli (Swarte et al., 2022). After liver transplantation, the proliferation of Escherichia coli in patients’ intestines leads to the production of abundant virulence effector proteins, ultimately causing diarrhea (Kaur and Dudeja, 2023). Moreover, enteritis caused by infectious diarrhea can lead to a short-term increase in tacrolimus blood concentrations (Nakamura et al., 2014). Additionally, we found that the Ras homolog gene family, member A (RhoA), was expressed explicitly in multiple metabolic pathways in the LDHBC group, playing crucial roles in cell signal transduction and cytoskeletal recombination. In contrast, Son of Sevenless (SOS), which promotes the conversion of the Ras protein from its inactive state (RAS GDP) to its active state (RAS GTP) and is closely linked to various clinical diseases (Bandaru et al., 2019), was not detected in the SDLBC group. Multiple signaling pathways in the LDLBC and LDHBC groups revealed explicit SOS expression in cell proliferation, signal transduction, and immune function pathways. Previous studies have confirmed that SOS promotes cell proliferation. SOS1 can activate the Ras protein, and the Ras protein mutation is a critical factor in various tumors (Luo et al., 2023). However, the reason for the absence of SOS in the intestinal microbial metabolic pathway of the SDLBC group remains unclear.

3.7 Antibiotic resistance gene

Our results indicated that the LDLBC group had the most antibiotic resistance (AR) genes (Figure 5A), including several mutant resistance genes in Escherichia coli (Figures 5B,C; Supplementary Table S10), which may be the reason for the high abundance of Escherichia coli in that group. Association analysis of resistance genes and species affiliation revealed that Bacillota, Bacteroidota, and Pseudomonadota were the top three categories. These three categories accounted for more than 80% of each group’s annotation of resistance genes. Pseudomonadota had the most annotated resistance genes within the LDLBC group compared to other groups. The group with the most resistance genes corresponding to Bacillota was the SDLBC group, and the SDLBC group had the most Bacillota (Figure 5D). Studies have shown that Bacillus in probiotic preparations has extensive antibiotic resistance (Jin et al., 2024), suggesting that patients in the SDLBC group may be at potential risk of AR and bacterial pathogenicity. The overview-circle diagram (Figure 5E) further corroborated these findings.

Figure 5

Figure 5. Resistance gene annotation on the basis of the CARD database. (A) Box plot showing the number of resistance genes across groups. (B,C) LEfSe analysis of resistance gene differences (LDA > 2); above is a clustering heatmap of the abundance of differential resistance genes between groups. (D) Two-circle map of the relationships between resistance genes and phylum level flora. The inner circle depicts the species distribution of ARO, whereas the outer circle illustrates the species distribution of genes across all samples, directly reflecting the enrichment of resistance genes within specific species. (E) Overview-circle diagram of resistance mechanisms and species (the right side depicts information on phylum level species, and the left side shows data on resistance mechanisms. The inner ring color indicates the resistance mechanism corresponding to each species).

3.8 Correlation analysis of clinical characteristics

We performed Spearman correlation analysis between the collected clinical characteristics and the metagenomic sequencing annotations of the gut microbes. The results revealed that Fusobacteriales were significantly positively correlated with T. RATE (TACD/BCD) (p < 0.001) and significantly negatively correlated with B. RATE (BCD/TACD) (p < 0.001) (Supplementary Figure S7A), suggesting that Fusobacteriales are abundant in the intestines of patients who have taken more tacrolimus orally and whose blood concentrations are low. We found that the WBC count was positively correlated with several common pathogenic bacteria (p < 0.05), such as Klebsiella and Escherichia; TBIL was positively correlated with Ligilactobacillus salivarius and Streptococcus salivarius (p < 0.05). STBIL and SAST were positively correlated with Erysipelotrichales (p < 0.05) and negatively correlated with Klebsiella and Flavonifractor plautii (p < 0.05) (Supplementary Figures S7A–C). In addition, we found a significant correlation between liver cancer and AR richness (p < 0.05) (Figure 6A), which was consistent with previous scholars’ findings. AR occurs in patients with tumors due to neutropenia caused by prophylactic or therapeutic antibiotics or chemotherapy (Nanayakkara et al., 2021). We also found that ALP and TC were positively correlated with AR richness: that the preoperative lipid indices (TC and LDL) were positively correlated with the Shannon index; and that WBC, AIT, BMI, and DM were negatively correlated with the Shannon index (Figure 6A). However, none of these correlations reached statistical significance. In addition, species richness and evenness were significantly correlated with some clinical phenotypes, as revealed by 16S amplicon sequencing annotation analysis (Figure 6B). In conclusion, these clinical characteristics influence the community distribution of the intestinal microecology in patients after liver transplantation. The general characteristics of liver transplant patients (age, gender, BMI) and underlying conditions (DM, HTN, cancer) explained less variance in the gut microbiome distribution than did preoperative liver function parameters (AST, ALB, TBIL). In contrast, preoperative lipid profiles (TC, LDL, HDL) and tacrolimus-related parameters (TACD, BCD, CV) had greater explanatory power for microbiome distribution (Figure 6C).

Figure 6

Figure 6. Correlation analysis of clinical characteristics. (A) Spearman correlation analysis between clinical characteristics and the Shannon index (species) and AR genes, on the basis of metagenomic sequence data. (B) Spearman correlation analysis between clinical characteristics and the α diversity index, based on 16S rDNA amplicon sequencing data. (C) Contribution of clinical characteristics to intestinal microbiota ecology (*p < 0.05, **p < 0.01).

3.9 Prediction model

We stratified 38 patients by tacrolimus CV into high CV (CV > 30, n = 13) and low CV (CV < 30, n = 25) groups (Xie et al., 2024). A predictive model was constructed with a high CV as the outcome, incorporating clinical features and the top 30 abundant genera. Top eight variables (Blautia, TACD, BCD, Roseburia, Streptococcus, Ruminococcus gnavus group, AST, and Age) were selected (Figures 7A,B; Supplementary Table S11). The results indicated that clinical features, including Streptococcus, effectively predict tacrolimus variability in liver transplant patients (Figures 7C–F; Supplementary Table S12). Notably, Blautia, Roseburia, and Ruminococcus gnavus belong to the Lachnospiraceae family, while Streptococcus was enriched in the LDHBC group and may be linked to increased tacrolimus blood concentrations. Additionally, previous studies have reported that tacrolimus use is associated with reduced Lachnospiraceae (Han et al., 2019).

Figure 7

Figure 7. Model prediction analysis of tacrolimus intrapatient variability (coefficient of variation) on the basis of representative clinical characteristics and top flora. (A) SHAP heatmap illustrating the correlation between 26 clinical features, the top 30 flora, and the coefficient of variation. The vertical axis represents patient IDs, and the horizontal axis denotes feature names. The black vertical line above each feature indicates the distribution range of its corresponding SHAP values. (B) SHAP bar chart displaying the importance ranking of feature variables affecting tacrolimus intrapatient variability. (C) ROC curve plot of cross-validation results using random forest. (D) ROC curve plot of the cross-validation results obtained via XGBoost. (E) ROC curve plot of the bootstrap results obtained via random forest. (F) ROC curve plot of the bootstrap results obtained via XGBoost.

4 Discussion

In this study, we observed alterations in the intestinal microecological structure of postliver transplantation patients compared with healthy individuals. Bacilli dominated the intestinal microbiota of liver transplant recipients. Group comparisons revealed that patients with low oral doses and high blood concentrations exhibited a predominance of Phascolarctobacterium faecium and Streptococcus salivarius in the gut. Research has identified Streptococcus salivarius as a pathogenic bacterial group associated with various diseases (Gacesa et al., 2022). Enterococcus raffinosus, Intestinibacter bartlettii, and Bacteroides fragilis predominate in the intestines of patients with appropriate oral doses of tacrolimus and blood concentrations below the recommended values. Studies have shown that Enterococcus raffinosus and Bacteroides fragilis can usually cause pathogenic infections (Lee et al., 2022; Patrick, 2022). The increase in these harmful bacteria may hinder the absorption of tacrolimus or promote its metabolism. Our results suggested that patients with low-dose tacrolimus and low blood concentrations had a greater abundance of Escherichia coli in the gut, including associated AR and protein functional groups. Although previous studies have documented this phenomenon (Kaur and Dudeja, 2023; Han et al., 2024), the mechanism by which Escherichia coli influences tacrolimus pharmacokinetics remains unclear. We also observed differences in the functional groups of the ABC transporter and drug efflux pump, which may directly cause differences in the oral dose and blood concentration. However, these findings are preliminary, and due to potential heterogeneity, further validation through in vitro or animal experiments is required.

Although our results confirmed that tacrolimus pharmacokinetics are related to intestinal microecology, they do not resolve the issue of causality. Multiple factors contribute to changes in the intestinal microecology after liver transplantation. In addition to recipient liver disease and transplantation procedures (Lai et al., 2022; Wu et al., 2023), various immunosuppressants play a key role in altering the gut microbiota. Postoperative immunosuppressive therapy impairs immune function and damages the intestinal immune barrier, leading to imbalances in the gut microbiota (Xu and Zhang, 2015; Tourret et al., 2017). Previous studies have shown that moderate doses of tacrolimus increase the abundance of Faecalibacterium prausnitzii and Bifidobacterium in the intestinal tract of rats after liver transplantation (Jiang et al., 2018). Furthermore, mouse experiments have shown that mycophenolate mofetil can selectively induce bacterial expression of β-glucuronidase (Taylor et al., 2019). Conversely, enzymes produced by intestinal microorganisms directly influence the transformation and metabolism of various drugs, including tacrolimus and mycophenolate mofetil (Zimmermann et al., 2019).

This study has several limitations. First, we did not include preoperative fecal samples from liver transplant patients. Although this cross-sectional study explored the relationships among tacrolimus oral dosage, blood concentration, and the gut microbiota composition, it did not fully account for the potential influence of preexisting liver disease and surgical intervention on the gut microbial landscape. Second, the relatively small sample size and group imbalance may have introduced bias and limited the generalizability of our findings. In future studies, a longitudinal design incorporating pre- and posttransplant samples from the same individuals would provide more robust evidence and clearer insights into the effects of transplantation and immunosuppression on the gut microbiome. Additionally, the study did not include CYP3A5 genotyping for liver transplant patients, which may have contributed to an overinterpretation of the causal role of gut microbiota in tacrolimus metabolism. To address these limitations, our research team has initiated follow-up studies incorporating genetic profiling, which will be published as soon as relevant findings are available.

In conclusion, our study revealed that the diversity and abundance of gut microbes in patients after liver transplantation are lower than those in healthy adults. Enterococcus raffinosus, Intestinibacter bartlettii, Bacteroides fragilis, and Fusobacteriales in the intestinal microbiota of postliver transplant recipients may be associated with decreased tacrolimus blood concentrations. Phascolarctobacterium faecium and Streptococcus salivarius may be associated with high blood concentrations of tacrolimus. The ABC transport enzyme ensures the absorption of tacrolimus as much as possible, but the drug efflux pump protein leads to difficulty in maintaining the blood drug concentration. Although there are many findings in our study, the small sample size may lead to errors in the results, and it is still necessary to expand the sample size or conduct in vitro or animal experiments for verification.

Data availability statement

The original contributions presented in the study are publicly available. This data can be found at: https://www.ncbi.nlm.nih.gov/, PRJNA1200201.

Ethics statement

The studies involving humans were approved by the Ethics Committee of the First Hospital of Shanxi Medical University. The studies were conducted in accordance with local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

YW (first author): Project administration, Data curation, Formal analysis, Writing – original draft, Methodology, Supervision, Conceptualization, Writing – review & editing. ZB: Formal analysis, Investigation, Data curation, Writing – review & editing. YL: Methodology, Writing – review & editing, Investigation, Validation. YW (fourth author): Conceptualization, Writing – review & editing, Supervision. JX: Writing – review & editing, Funding acquisition, Supervision, Conceptualization. ZL: Project administration, Funding acquisition, Writing – review & editing, Conceptualization.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by the Natural Science Foundation of Shanxi (Grant No. 202103021224408); the Shanxi Provincial Department of Science and Technology (Grant Nos. 202204010931008 and 202302130501013); and the First Hospital of Shanxi Medical University Introduction Talent Fund (Grant No. SYYYRC-2022006), Shanxi Central Guidance Local Science and Technology Development Fund Project (YDZJSX2021B012) and National Natural Science Foundation of China (82470693).

Acknowledgments

We are grateful to the research team members for their insightful discussions and contributions to this work. We also sincerely thank Novogene Co. Ltd. for their invaluable support in bioinformatics analysis, specifically through their Novomagic cloud analysis platform.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

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Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2025.1616985/full#supplementary-material

References

Anglicheau, D., Flamant, M., Schlageter, M. H., Martinez, F., Cassinat, B., Beaune, P., et al. (2003). Pharmacokinetic interaction between corticosteroids and tacrolimus after renal transplantation. Nephrol. Dial. Transplant. 18, 2409–2414. doi: 10.1093/ndt/gfg381

PubMed Abstract | Crossref Full Text | Google Scholar

Bandaru, P., Kondo, Y., and Kuriyan, J. (2019). The interdependent activation of son-of-Sevenless and Ras. Cold Spring Harb. Perspect. Med. 9:a031534. doi: 10.1101/cshperspect.a031534

PubMed Abstract | Crossref Full Text | Google Scholar

Bretagne, D., Paris, A., Matthews, D., Fougere, L., Burrini, N., Wagner, G. K., et al. (2024). "mix and match" auto-assembly of glycosyltransferase domains delivers biocatalysts with improved substrate promiscuity. J. Biol. Chem. 300:105747. doi: 10.1016/j.jbc.2024.105747

PubMed Abstract | Crossref Full Text | Google Scholar

Campagne, O., Mager, D. E., and Tornatore, K. M. (2019). Population pharmacokinetics of tacrolimus in transplant recipients: what did we learn about sources of Interindividual variabilities? J. Clin. Pharmacol. 59, 309–325. doi: 10.1002/jcph.1325

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, Z., Chen, H., Huang, W., Guo, X., Yu, L., Shan, J., et al. (2024). Bacteroides fragilis alleviates necrotizing enterocolitis through restoring bile acid metabolism balance using bile salt hydrolase and inhibiting FXR-NLRP3 signaling pathway. Gut Microbes 16:2379566. doi: 10.1080/19490976.2024.2379566

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, R., Wu, F., Zeng, G., Chen, Y., Lu, S., and Huang, H. (2024). Therapeutic effect of fecal microbiota transplantation on rats with liver cirrhosis and its influence on gut microbiota. Iran. J. Basic Med. Sci. 27, 1148–1154. doi: 10.22038/IJBMS.2024.74280.16142

PubMed Abstract | Crossref Full Text | Google Scholar

Choudhary, N. S., Saigal, S., Bansal, R. K., Saraf, N., Gautam, D., and Soin, A. S. (2017). Acute and chronic rejection after liver transplantation: what a clinician needs to know. J. Clin. Exp. Hepatol. 7, 358–366. doi: 10.1016/j.jceh.2017.10.003

PubMed Abstract | Crossref Full Text | Google Scholar

Csikany, N., Kiss, A., Deri, M., Fekete, F., Minus, A., Toth, K., et al. (2021). Clinical significance of personalized tacrolimus dosing by adjusting to donor CYP3A-status in liver transplant recipients. Br. J. Clin. Pharmacol. 87, 1790–1800. doi: 10.1111/bcp.14566

PubMed Abstract | Crossref Full Text | Google Scholar

Degraeve, A. L., Haufroid, V., Loriot, A., Gatto, L., Andries, V., Vereecke, L., et al. (2023). Gut microbiome modulates tacrolimus pharmacokinetics through the transcriptional regulation of ABCB1. Microbiome 11:138. doi: 10.1186/s40168-023-01578-y

PubMed Abstract | Crossref Full Text | Google Scholar

Demel, M. A., Schwaha, R., Kramer, O., Ettmayer, P., Haaksma, E. E., and Ecker, G. F. (2008). In silico prediction of substrate properties for ABC-multidrug transporters. Expert Opin. Drug Metab. Toxicol. 4, 1167–1180. doi: 10.1517/17425255.4.9.1167

PubMed Abstract | Crossref Full Text | Google Scholar

Fu, C., Ni, J., Huang, R., Gao, Y., Li, S., Li, Y., et al. (2023). Sex different effect of antibiotic and probiotic treatment on intestinal microbiota composition in chemically induced liver injury rats. Genomics 115:110647. doi: 10.1016/j.ygeno.2023.110647

PubMed Abstract | Crossref Full Text | Google Scholar

Gacesa, R., Kurilshikov, A., Vich Vila, A., Sinha, T., Klaassen, M. A. Y., Bolte, L. A., et al. (2022). Environmental factors shaping the gut microbiome in a Dutch population. Nature 604, 732–739. doi: 10.1038/s41586-022-04567-7

PubMed Abstract | Crossref Full Text | Google Scholar

Guo, Y., Crnkovic, C. M., Won, K. J., Yang, X., Lee, J. R., Orjala, J., et al. (2019). Commensal gut Bacteria convert the immunosuppressant tacrolimus to less potent metabolites. Drug Metab. Dispos. 47, 194–202. doi: 10.1124/dmd.118.084772

PubMed Abstract | Crossref Full Text | Google Scholar

Han, Y., Jiang, X., Ling, Q., Wu, L., Wu, P., Tang, R., et al. (2019). Antibiotics-mediated intestinal microbiome perturbation aggravates tacrolimus-induced glucose disorders in mice. Front. Med. 13, 471–481. doi: 10.1007/s11684-019-0686-8

PubMed Abstract | Crossref Full Text | Google Scholar

Han, N., Peng, X., Zhang, T., Qiang, Y., Li, X., and Zhang, W. (2024). Rapid turnover and short-term blooms of Escherichia coli in the human gut. J. Bacteriol. 206:e0023923. doi: 10.1128/jb.00239-23

PubMed Abstract | Crossref Full Text | Google Scholar

Henkel, L., Jehn, U., Tholking, G., and Reuter, S. (2023). Tacrolimus-why pharmacokinetics matter in the clinic. Front. Transplant. 2:1160752. doi: 10.3389/frtra.2023.1160752

PubMed Abstract | Crossref Full Text | Google Scholar

Ikeyama, N., Murakami, T., Toyoda, A., Mori, H., Iino, T., Ohkuma, M., et al. (2020). Microbial interaction between the succinate-utilizing bacterium Phascolarctobacterium faecium and the gut commensal Bacteroides thetaiotaomicron. Microbiology 9:e1111. doi: 10.1002/mbo3.1111

PubMed Abstract | Crossref Full Text | Google Scholar

Jiang, J. W., Ren, Z. G., Lu, H. F., Zhang, H., Li, A., Cui, G. Y., et al. (2018). Optimal immunosuppressor induces stable gut microbiota after liver transplantation. World J. Gastroenterol. 24, 3871–3883. doi: 10.3748/wjg.v24.i34.3871

PubMed Abstract | Crossref Full Text | Google Scholar

Jin, X., Zhang, L., Cao, Y., Dai, Z., Ge, X., Cai, R., et al. (2024). Antibiotic resistance characterization, virulence factors and molecular characteristics of Bacillus species isolated from probiotic preparations in China. J. Glob. Antimicrob. Resist. 43, 35–39. doi: 10.1016/j.jgar.2024.06.015

Crossref Full Text | Google Scholar

Kaur, P., and Dudeja, P. K. (2023). Pathophysiology of enteropathogenic Escherichia coli-induced diarrhea. Newborn 2, 102–113. doi: 10.5005/jp-journals-11002-0056

PubMed Abstract | Crossref Full Text | Google Scholar

Kosuta, I., Kelava, T., Ostojic, A., Sesa, V., Mrzljak, A., and Lalic, H. (2024). Immunology demystified: a guide for transplant hepatologists. World J. Transplant. 14:89772. doi: 10.5500/wjt.v14.i1.89772

PubMed Abstract | Crossref Full Text | Google Scholar

Lai, Z., Chen, Z., Zhang, A., Niu, Z., Cheng, M., Huo, C., et al. (2022). The gut microbiota in liver transplantation recipients during the perioperative period. Front. Physiol. 13:854017. doi: 10.3389/fphys.2022.854017

PubMed Abstract | Crossref Full Text | Google Scholar

Lee, Y. W., Lim, S. Y., Jung, J., Kim, M. J., Chong, Y. P., Kim, S. H., et al. (2022). Enterococcus raffinosus bacteremia: clinical experience with 49 adult patients. Eur. J. Clin. Microbiol. Infect. Dis. 41, 415–420. doi: 10.1007/s10096-021-04389-x

PubMed Abstract | Crossref Full Text | Google Scholar

Lee, J. R., Muthukumar, T., Dadhania, D., Taur, Y., Jenq, R. R., Toussaint, N. C., et al. (2015). Gut microbiota and tacrolimus dosing in kidney transplantation. PLoS One 10:e0122399. doi: 10.1371/journal.pone.0122399

PubMed Abstract | Crossref Full Text | Google Scholar

Luo, G., Wang, B., Hou, Q., and Wu, X. (2023). Development of son of Sevenless homologue 1 (SOS1) modulators to treat cancers by regulating RAS signaling. J. Med. Chem. 66, 4324–4341. doi: 10.1021/acs.jmedchem.2c01729

PubMed Abstract | Crossref Full Text | Google Scholar

Meng, F., Lu, F., Du, H., Nie, T., Zhu, X., Connerton, I. F., et al. (2021a). Acetate and auto-inducing peptide are independent triggers of quorum sensing in Lactobacillus plantarum. Mol. Microbiol. 116, 298–310. doi: 10.1111/mmi.14709

PubMed Abstract | Crossref Full Text | Google Scholar

Meng, F., Zhao, H., Nie, T., Lu, F., Zhang, C., Lu, Y., et al. (2021b). Acetate activates Lactobacillus Bacteriocin synthesis by controlling quorum sensing. Appl. Environ. Microbiol. 87:e0072021. doi: 10.1128/AEM.00720-21

PubMed Abstract | Crossref Full Text | Google Scholar

Mohammed Ali, Z., Meertens, M., Fernandez, B., Fontova, P., Vidal-Alabro, A., Rigo-Bonnin, R., et al. (2023). CYP3A5*3 and CYP3A4*22 cluster polymorphism effects on LCP-tac tacrolimus exposure: population pharmacokinetic approach. Pharmaceutics 15:2699. doi: 10.3390/pharmaceutics15122699

PubMed Abstract | Crossref Full Text | Google Scholar

Montanari, F., and Ecker, G. F. (2015). Prediction of drug-ABC-transporter interaction--recent advances and future challenges. Adv. Drug Deliv. Rev. 86, 17–26. doi: 10.1016/j.addr.2015.03.001

PubMed Abstract | Crossref Full Text | Google Scholar

Nakamura, A., Amada, N., Haga, I., Tokodai, K., and Kashiwadate, T. (2014). Effects of elevated tacrolimus trough levels in association with infectious enteritis on graft function in renal transplant recipients. Transplant. Proc. 46, 592–594. doi: 10.1016/j.transproceed.2013.11.040

Crossref Full Text | Google Scholar

Nanayakkara, A. K., Boucher, H. W., Fowler, V. G. Jr., Jezek, A., Outterson, K., and Greenberg, D. E. (2021). Antibiotic resistance in the patient with cancer: escalating challenges and paths forward. CA Cancer J. Clin. 71, 488–504. doi: 10.3322/caac.21697

PubMed Abstract | Crossref Full Text | Google Scholar

Patrick, S. (2022). A tale of two habitats: Bacteroides fragilis, a lethal pathogen and resident in the human gastrointestinal microbiome. Microbiology (Reading) 168:001156. doi: 10.1099/mic.0.001156

PubMed Abstract | Crossref Full Text | Google Scholar

Sucu, S., Basarir, K. E., Mihaylov, P., Balik, E., Lee, J. T. C., Fridell, J. A., et al. (2023). Impact of gut microbiota on liver transplantation. Am. J. Transplant. 23, 1485–1495. doi: 10.1016/j.ajt.2023.05.030

PubMed Abstract | Crossref Full Text | Google Scholar

Swarte, J. C., Li, Y., Hu, S., Bjork, J. R., Gacesa, R., Vich Vila, A., et al. (2022). Gut microbiome dysbiosis is associated with increased mortality after solid organ transplantation. Sci. Transl. Med. 14:eabn7566. doi: 10.1126/scitranslmed.abn7566

Crossref Full Text | Google Scholar

Tang, X., Zeng, Y., Xiong, K., and Zhong, J. (2024). Bacillus spp. as potential probiotics: promoting piglet growth by improving intestinal health. Front. Vet. Sci. 11:1429233. doi: 10.3389/fvets.2024.1429233

PubMed Abstract | Crossref Full Text | Google Scholar

Taylor, M. R., Flannigan, K. L., Rahim, H., Mohamud, A., Lewis, I. A., Hirota, S. A., et al. (2019). Vancomycin relieves mycophenolate mofetil-induced gastrointestinal toxicity by eliminating gut bacterial beta-glucuronidase activity. Sci. Adv. 5:eaax2358. doi: 10.1126/sciadv.aax2358

PubMed Abstract | Crossref Full Text | Google Scholar

Tillman, E., Nikirk, M. G., Chen, J., Skaar, T. C., Shugg, T., Maddatu, J. P., et al. (2023). Implementation of clinical cytochrome P450 3A genotyping for tacrolimus dosing in a large kidney transplant program. J. Clin. Pharmacol. 63, 961–967. doi: 10.1002/jcph.2249

PubMed Abstract | Crossref Full Text | Google Scholar

Tourret, J., Willing, B. P., Dion, S., MacPherson, J., Denamur, E., and Finlay, B. B. (2017). Immunosuppressive treatment alters secretion of ileal antimicrobial peptides and gut microbiota, and favors subsequent colonization by uropathogenic Escherichia coli. Transplantation 101, 74–82. doi: 10.1097/TP.0000000000001492

Crossref Full Text | Google Scholar

Transplantation, C. M. A. B. O. (2021). Diagnosis and treatment specification for immunosuppressive therapy and rejection of liver transplantation in China (2019 edition). Organ Transplant. 12, 8–14, 28. doi: 10.3969/j.issn.1674-7445.2021.01.002

Crossref Full Text | Google Scholar

Tremblay, S., Nigro, V., Weinberg, J., Woodle, E. S., and Alloway, R. R. (2017). A steady-state head-to-head pharmacokinetic comparison of all FK-506 (tacrolimus) formulations (ASTCOFF): an open-label, prospective, randomized, two-arm, three-period crossover study. Am. J. Transplant. 17, 432–442. doi: 10.1111/ajt.13935

PubMed Abstract | Crossref Full Text | Google Scholar

Wu, F., Guo, X., Zhang, J., Zhang, M., Ou, Z., and Peng, Y. (2017). Phascolarctobacterium faecium abundant colonization in human gastrointestinal tract. Exp. Ther. Med. 14, 3122–3126. doi: 10.3892/etm.2017.4878

PubMed Abstract | Crossref Full Text | Google Scholar

Wu, Z., Zhou, H., Liu, D., and Deng, F. (2023). Alterations in the gut microbiota and the efficacy of adjuvant probiotic therapy in liver cirrhosis. Front. Cell. Infect. Microbiol. 13:1218552. doi: 10.3389/fcimb.2023.1218552

PubMed Abstract | Crossref Full Text | Google Scholar

Xie, W., Fan, S., Liu, R., Yan, W., Su, C., Zheng, K., et al. (2024). Tacrolimus intra-patient variability measures and its associations with allograft clinical outcomes in kidney transplantation. Transplant. Rev. (Orlando) 38:100842. doi: 10.1016/j.trre.2024.100842

Crossref Full Text | Google Scholar

Xu, X., and Zhang, X. (2015). Effects of cyclophosphamide on immune system and gut microbiota in mice. Microbiol. Res. 171, 97–106. doi: 10.1016/j.micres.2014.11.002

PubMed Abstract | Crossref Full Text | Google Scholar

Younas, S., Mazhar, B., Liaqat, I., Ali, S., Tahir, H. M., and Ali, N. M. (2022). Bacteriocin production by lactobacilli and their role as antibacterial tool against common pathogens. J. Oleo Sci. 71, 541–550. doi: 10.5650/jos.ess21424

PubMed Abstract | Crossref Full Text | Google Scholar

Zimmermann, M., Zimmermann-Kogadeeva, M., Wegmann, R., and Goodman, A. L. (2019). Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570, 462–467. doi: 10.1038/s41586-019-1291-3

PubMed Abstract | Crossref Full Text | Google Scholar