Role of the gut microbiota–bile acid axis in liver fibrosis based on microbiomics and targeted metabolomics

Abstract

Background

Effective diagnoses and treatments for liver fibrosis (LF) are lacking. The gut microbiota (GM)–bile acid (BA) axis is critically associated with LF development and may represent a potential target for delaying or reversing LF.

Methods

We combined fecal microbiomics and BA-targeted metabolomics to characterize the GM–BA axis in rats with carbon tetrachloride (CCl₄)-induced LF and used fecal microbiota transplantation (FMT) and probiotics (Bifidobacterium quadruple viable tablet solution) to regulate the GM–BA axis to investigate the role of the GM–BA axis in LF and its related mechanisms.

Results

The GM composition and GM β-diversity differed significantly between the control and model rats. Clostridia and others were significantly increased, whereas Bifidobacteriales and others were significantly decreased in model rats. Serum BA metabolism differed significantly between the groups. The concentrations of 19 BAs were significantly increased in model rats. FMT improved the disordered GM by increasing Bifidobacteriales and others and decreasing Clostridia and others. Probiotics improved the disordered GM by decreasing Clostridia, Lachnospiracea_incertae_sedis and others. FMT and probiotics improved BA metabolism by decreasing BAs such as tauroursodeoxycholic acid (TUDCA). FMT and probiotics were associated with a reduced degree of LF in model rats.

Conclusions

The GM–BA axis is strongly associated with LF, and the use of FMT and probiotics may contribute to regulating the GM–BA axis and to alleviating LF.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-026-05224-z.

Introduction

Liver fibrosis (LF) is a multi-etiological disease caused by various acute and chronic liver injuries, including viral, alcohol-related, pharmacological, immune-, and metabolism-related injuries [1, 2]. It is characterized by an imbalance between intrahepatic fiber formation and degradation after liver injury, resulting in excessive accumulation of the extracellular matrix and severe disruption of the normal structure and function of the liver [3]. LF is a potentially reversible and dynamic process that, if not prevented, may progress to cirrhosis, liver cancer, and other life-threatening diseases [4]. In 2017, there were 122.6 million cases of cirrhosis worldwide, constituting a 72.4% increase in the number of cases over 1990, and more than 1.32 million cirrhosis-related deaths [5]. Unfortunately, effective clinical diagnosis and treatment for LF are lacking. Invasive liver tissue biopsy remains the gold standard for diagnosing LF, and liver transplantation is the only promising treatment for decompensated cirrhosis [6, 7]. Therefore, development of effective LF diagnostic and treatment methods is socially and economically important.

The gut microbiota (GM) involve complex microbiota communities in the host intestinal tract that play important roles in the body’s nutritional, immunological, and metabolic processes [8, 9]. The role of the GM in LF is increasingly emphasized because of bidirectional communication between the gut and liver via the gut–liver axis [10–13]. The GM of patients with LF demonstrate significant dysbiosis, which is mainly manifested by a marked increase in pathogenic bacteria and a significant decrease in beneficial bacteria [14–17]. Several animal studies have confirmed the importance of the GM in LF [18–21]. A disordered GM promotes LF through mechanisms such as gut barrier damage, production of harmful metabolites, and destruction of the liver’s immune system [22–24].

Bile acids (BAs) are important GM metabolites, although they are synthesized in the liver, they diversify in response to the GM [25–28]. In the liver, cholesterol is used to synthesize primary BAs (PBAs), such as cholic acid (CA) and chenodeoxycholic acid (CDCA), and their conjugates, via classical and alternative pathways. After entering the intestine, a portion of the PBAs is converted to a variety of secondary BAs (SBAs), such as lithocholic acid (LCA) and deoxycholic acid (DCA), and their conjugates, while the remainder is returned to the liver via the enterohepatic cycle [29, 30]. Some studies have found that BA profiles are also significantly altered in patients with LF. These alterations include elevated serum total BAs and total liver BA levels, and decreased fecal total BA levels [31–34]. In addition to exerting anti‑fibrotic effects via the activation of nuclear receptors—such as the Farnesoid X receptor, Vitamin D receptor, and Pregnane X receptor—as well as the membrane receptor G protein‑coupled receptor 5 (TGR-5) [35–38], BAs also act as important signaling molecules that promote fibrosis by activating other membrane receptors, including Sphingosine‑1‑phosphate receptor 2 and the Epidermal growth factor receptor [39–45]. Moreover, BAs can directly disrupt the plasma membrane, activating pathways such as Mitogen‑Activated Protein Kinases and the pro‑inflammatory NF-κB signaling cascade. This process elevates levels of reactive oxygen species in hepatocytes, thereby further contributing to LF [36].

In addition to directly transforming BAs through processes such as decoupling, dehydroxylation, oxidation, isomerization, esterification, and desulfation [46, 47], the GM can regulate the composition of the BAs by influencing the enzymes and pathways involved in BA synthesis [48–50]. Additionally, BAs may modulate the composition of the GM by disrupting bacterial membranes and inducing nitric oxide synthase with antimicrobial peptides [51, 52]. This complex bidirectional interaction between the GM and BAs is also known as the GM–BA axis [26, 53, 54]. Both the GM and BA metabolism are disturbed in patients with LF [17, 33] and targeting the GM–BA axis might be a viable treatment approach to lessen the severity of LF [55–57].

FMT and probiotics are two important methods for improving GM disorders [58–61]. FMT refers to the transplantation of functional flora from the feces of healthy individuals into the digestive tract of patients with an imbalanced GM, to rebuild the GM and thus achieve disease treatment [62]. Probiotics can also be used to remodel the GM composition in individuals with disease by increasing the number of beneficial bacteria, thereby treating diseases associated with GM disorders [63]. FMT is currently used to treat relapsed and refractory Clostridium difficile infections and inflammatory bowel disease [64]. Probiotics are also effective against acute gastroenteritis, Clostridium difficile-associated diarrhea, irritable bowel syndrome, dyspepsia, and other intestinal diseases [65]. Although FMT and probiotics have not yet been used for the treatment of LF, FMT and probiotics are potential therapeutic measures for LF. Kang et al. [66] reported that FMT has the potential to treat chronic liver disease associated with hepatitis B. Bajaj et al. [67] found that FMT restored the composition and function of the GM in patients with LF, which in turn attenuated LF. Moreover, probiotics including Bifidobacterium and Lactobacillus play important roles in the treatment of non-alcoholic fatty liver disease [68–71]. Liu et al. [72] also found that the probiotic bacterium Lactobacillus rhamnosus could ameliorate LF by modulating the GM–BA axis.

Thus, the GM–BA axis appears to be critically involved in LF and may offer a potential target for delaying or reversing LF. However, the mechanism underlying this role is unknown, and clinical application is not yet possible [23]. Therefore, we constructed a rat model of LF and investigated the characteristics of the GM and BA metabolism and their correlation with LF. Additionally, we altered the GM–BA axis by modulating the GM through FMT and probiotics, and then we investigated how modulation of the GM–BA axis correlates with LF. Our study revealed the role of the GM–BA axis in LF, creating a basis for new strategies for the diagnosis and treatment of LF from the perspective of the GM and BA metabolism.

Methods

Materials

The BA standards used in the experiment were as follows: glycochenodeoxycholic acid (GCDCA), LCA, glycoursodeoxycholic acid (GUDCA), and glycodeoxycholic acid (GDCA) were purchased from Shanghai ANPELTRACE Standard Technical Service Co (Shanghai, China). GHDCA and glycocholic acid (GCA) were purchased from Shanghai Yuanye Bio-Technology Co. (Shanghai, China). Taurolithocholic acid (TLCA) was purchased from Chengdu Ruifenshi Biotechnology Co (Chengdu, China). Taurodeoxycholic acid (TDCA) was purchased from Shanghai Standard Technology Development Co (Shanghai, China). Glycolithocholic acid (GLCA) was purchased from Cato Research Chemicals (Guangzhou, China). Taurohyodeoxycholic acid (THDCA), TUDCA, CA, taurocholic acid (TCA), CDCA, taurochenodeoxycholic acid (TCDCA), ursodeoxycholic acid (UDCA), DCA, hyodeoxycholic acid (HDCA) were purchased from the China Academy of Food and Drug Control (Shanghai, China). β-Muricholic acid (β-MCA), α-MCA, tauro-α-MCA (Tα-MCA), and hyocholic acid (HCA) were purchased from Shanghai Zhenzhun Biotechnology Co. (Shanghai, China).

ELISA kits for procollagen type III N-terminal peptide (PIIINP), hyaluronic acid (HA), and collagen type IV (CoL IV) were purchased from Wuhan Elabscience Biotechnology Co. (Wuhan, China). An ELISA kit for rat laminin (LN) was purchased from Shanghai Animal Union Co. (Shanghai, China). Environmentally friendly dewaxing clear solution, hematoxylin and eosin (HE) dye set, and Masson dye set were purchased from Wuhan Servicebio Technology Co. (Wuhan, China). Formic acid and ammonia were purchased from Shanghai Aladdin Biochemical Technology Co. (Shanghai, China). Bifidobacterium quadruple viable tablets were purchased from Hangzhou Longda Xinke Bio-pharmaceutical Co., Ltd. (Hanghzhou, China; each tablet weighed 0.5 g, in which Bifidobacterium, Lactobacillus, and Enterococcus bacteria did not constitute less than 0.5 × 10⁶ CFU); while Bacillus was not less than 0.5 × 10⁵ CFU. Carbon tetrachloride solution (Tianjin Damao Chemical Reagent Factory, Tianjin, China), olive oil (Hebei Qianhou International Trading Co., Ltd., Hebei, China), sodium pentobarbital (Sigma-Aldrich Corp., St Louis, MO, USA), formalin fixative (Beijing Reagan Biotechnology Co., Ltd., Beijing, China), QIAamp^® Fecal DNA Genome Extraction Kit (Qiagen, Hilden, Germany), NovaSeq 6000 SP kit (Illumina, San Diego, CA, USA), TopTaq DNA polymerase kit (Beijing QuanShiJin Biotechnology Co., Ltd., Beijing, China), Agencourt AMPure XP magnetic beads for nucleic acid purification (Beckman Coulter, Brea, CA, USA), acetonitrile (Sigma-Aldrich).

Animal grouping and interventions

All experiments and procedures were approved by the Laboratory Animal Ethics Committee of Lanzhou University (ethics approval number: LDYYLL2023-294).

Fifty male Sprague–Dawley rats (weighing 100–150 g) were purchased from the Animal Experimentation Center of the Lanzhou Institute of Veterinary Medicine (License No: SCXK (Ga) 2020-0002) and housed in a specific pathogen-free-grade laboratory of the Medical Laboratory Animal Center of Lanzhou University. After 1 week of acclimatization, 50 rats were randomly divided into a control group (n = 12) and an experimental group (n = 38). The rats in the experimental group were injected subcutaneously with pure CCl₄ solution at a dose of 3 mL/kg for the first injection; 50% CCl₄ solution (a 1:1 mixture of CCl₄ solution and olive oil) at a dose of 3 mL/kg for the second injection; and then 50% CCl₄ solution at a dose of 2 mL/kg was injected intraperitoneally each time for a total of 4 weeks. During this period, the control rats were subcutaneously injected with the same volume of saline for the first injection, and then with the same volume of olive oil for the second and subsequent injections. Importantly, all injections of CCl₄ solution, olive oil, and saline were administered simultaneously to all rats in the experimental group following a consistent protocol throughout the 4-week modeling period. There was no sequential order of injections across the future subgroups, as group allocation occurred only after the completion of the modeling phase. Therefore, no systematic bias in fibrosis induction could have been introduced prior to intervention. During the 4‑week modeling period, 6 rats from the experimental group died, leaving 32 rats for subsequent procedures. To verify successful fibrosis induction before treatment initiation, two randomly selected rats per group (control and experimental) were euthanized after week 4 for histological assessment. They were anesthetized by intraperitoneal injection of sodium pentobarbital (2 mL/kg), and the depth of anesthesia was confirmed by the absence of pedal and corneal reflexes. Following the induction of deep anesthesia, the rats were euthanized by exsanguination via abdominal aorta puncture under aseptic conditions, ensuring they remained unconscious throughout the procedure. HE and Masson staining of liver tissue confirmed the establishment of LF in these animals (Fig. S1), indicating successful modeling in the experimental cohort.

The remaining 30 rats from the experimental group were then randomly allocated into three groups: model (n = 10), FMT (n = 10), and probiotics (n = 10). This randomization ensured comparable baseline fibrosis severity across groups before the initiation of interventions. To avoid cross‑contamination through coprophagy or direct contact, rats from different groups were housed separately within the same facility. Rats in the FMT group were intragastrically gavaged with a standardized fecal microbiota suspension prepared from daily collected, pooled, and homogenized fresh fecal samples of the control rats; rats in the probiotics group were gavaged with Bifidobacterium quadruple viable tablet preparation, and rats in the control and model groups were gavaged with an equal amount of physiological saline once a day for 12 weeks. All rats were weighed twice weekly, and the dosage was adjusted according to body weight. Fecal, serum, and liver tissues of the rats were sampled at the end of the 12-week period.

The specific methods of FMT and probiotics intervention were as follows. Fresh fecal samples from all rats in the control group were collected daily by anal stimulation with sterile cotton swabs. The feces were weighed on an ice box, immediately pooled together, and quickly transferred into a 50-mL sterile centrifuge tube. Refrigerated sterile saline was added at 5 times the fecal weight (v/w; e.g., 50 mL saline for 10 g feces), and the mixture was thoroughly homogenized. The suspension was initially filtered through a sterile iron wire screen and then re-filtered through three layers of sterile gauze. The entire process was performed at a low temperature, and the suspension was collected and used immediately. The rats in the FMT group were gavaged with the prepared fecal microbiota suspension, 10 mL/kg, once a day for 12 weeks.

Rats in the probiotics group were gavaged with Bifidobacterium quadruple viable tablet solution (4 tablets mixed with 20 mL distilled water), at 10 mL/kg, once daily, for 12 weeks. The rats in the control and model groups were administered the same dose of sterile saline in the same way.

Sample collection

Collection and processing of blood specimens

Using the anesthesia and euthanasia methods described above, approximately 8–10 mL of blood was collected aseptically from the abdominal aorta. The blood was allowed to clot at 4℃ for 2 h and was then centrifuged at 3500 r/min for 10 min at 4℃. Finally, the serum supernatant was harvested, aliquoted, and stored at -80℃ until analysis.

Collection and processing of liver tissue specimens

Liver tissue sections of approximately 1 cm × 1 cm in length and width were prepared under aseptic conditions, loaded into tissue freezing tubes, and immediately placed in liquid nitrogen. They were subsequently transferred to a -80℃ refrigerator for storage until used. Moreover, liver tissues of approximately 1 cm × 1 cm × 0.5 cm (length, width, and height) were prepared under aseptic conditions, placed in centrifuge tubes containing 10% formalin fixative, and stored for 24–48 h before being used for HE and Masson staining.

Collection and processing of rat fecal specimens

After euthanasia, fecal matter was collected from the colon under aseptic conditions, loaded into 2 mL sterile tissue freezing tubes, which were immediately placed into liquid nitrogen, and then transferred to -80℃ refrigerator for storage. Three fecal samples were collected from each rat.

Histopathological staining and quantitative analysis of the liver

Liver tissues with length, width, and height of approximately 1 cm × 1 cm × 0.5 cm were collected and fixed in 10% formalin solution for 24–48 h, and then dehydrated in 75% alcohol for 4 h, 85% alcohol for 2 h, 90% alcohol for 2 h, 95% alcohol for 1 h, anhydrous ethanol I for 30 min, and anhydrous ethanol II for 30 min. Subsequently, benzene alcohol was added for 5–10 min, xylene I for 5–10 min, and xylene II for 5–10 min to render the tissue transparent. Afterward, the samples were made into wax blocks by immersion wax embedding, cut into 4-µm-thick sections, and stained according to the instructions of the HE staining set or the Masson staining set.

Tissue sections were scanned using a TissueFAXS system (TissueGnostics, Vienna, Austria), and TissueGnostics software was employed for quantitative analysis of the positive rate of blue-stained collagen fibers following Masson trichrome staining, to evaluate the degree of LF.

Determination of LF indexes by ELISA

Serum LF indicators, such as PIIINP, CoL IV, HA, and LN, were measured in the serum samples of the rats according to the instructions of the commercial ELISA kits.

Analysis of GM composition

DNA was extracted from the collected fecal samples using the QIAamp^® Fecal DNA Genome Extraction Kit according to the manufacturer’s instructions. The integrity of the genomic DNA was tested by agarose gel electrophoresis, and the concentration and purity of the genomic DNA was determined using a NanoDrop 2000 and a Qubit 3.0 spectrophotometer. The bacterial genomic DNA was then used as a template to amplify regions V3 to V4 of the bacterial 16S rRNA gene using forward primer 5’-CCTACGGGGNGGCWGCAG-3’ and reverse primer 5’-GACTACHVGGGTATCTAATCC-3’. Paired-end sequencing (2 × 250 bp) was performed on the Illumina NovaSeq 6000 platform (SP-Xp flow cell). The raw reads were processed and analyzed using QIIME 2 software.

BA metabolism analysis methods

Ultra-high-performance liquid chromatography-tandem mass spectrometry (QTRAP 5500) was used for the quantitative analysis of BAs. Chromatographic separation was performed on an Eclipse Plus C18 column (2.1 × 100 mm, 1.8 μm). An electrospray ionization source and the dynamic multiple reaction monitoring mode were used for mass spectrometry. Detailed chromatographic and mass spectrometric conditions, as well as specific operational methods, are described in Supplementary file (Tables S1-S3).

Statistical methods

Data were managed and analyzed using SPSS software (version 27.0; IBM Corp., Armonk, NY, USA) for data management and analysis, unless otherwise stated. Normally distributed data are expressed as mean ± standard error of the mean. Non-normally distributed data are expressed as median (interquartile range). Two independent samples t-tests and Mann–Whitney U-tests were used to compare differences between the two groups. The Kruskal–Wallis test and one-way analysis of variance (ANOVA) were used for comparison between multiple groups. For bioinformatics analysis of GM data, indices such as observed, Chao1, ACE, Shannon, Simpson, and coverage were calculated to analyze α-diversity of microbiota, while PCA, PCoA, and NMDS based on Euclidean, Bray, and Unifrac distances were used to analyze β-diversity of microbiota. LEfSe analyses were used to compare the microbiota that differed between the groups. Univariate statistical analysis of BA metabolomic data was performed using SPSS (version 27.0), and data were visualized using GraphPad Prism 9.0 (GraphPad Software Inc., La Jolla, CA, USA). BA metabolomics data, such as PCA and OPLS-DA, were subjected to multivariate statistical analyses and visualized using Metaboanalyst 5.0. Spearman’s rank correlation analysis of significantly altered GM and BAs in rats of the model group was performed using Wekemo BioIncloud.

Results

GM characteristics of rats with LF

To assess the changes occurring in the GM of LF model rats, we subjected fecal samples of control and model rats to 16 S rRNA microbiota sequencing. The rarefaction curves were flattened, indicating a reasonable amount of sequencing data for our samples (Fig. S2A). The Venn diagram showed that in both groups of rats, 3048 amplified sequence variants (ASVs) were co-clustered, with 1077 ASVs that were identical between the two groups; of these, 1018 ASVs were specific to the control group and 927 ASVs were specific to the LF model group (Fig. S2B).

We further analyzed the α-diversity and β-diversity of the rat GM in the two groups and assessed the differences in the composition of the rat GM between the two groups as a whole. While α-diversity showed no significant differences (Fig. S2C), β-diversity exhibited significant separation according to PERMANOVA (Bray-Curtis: P = 0.0001, R²= 0.2651; unweighted Unifrac: P = 0.0002, R²= 0.0846; weighted Unifrac: P = 0.0001, R²= 0.2034) and ANOSIM (P = 0.0002, R = 0.6489). This was visualized in ordination plots: PCA (Euclidean distance) showed distinct clustering along the first two principal components (PC1 = 41.55%, PC2 = 20.64%), NMDS (Bray-Curtis) indicated group-wise separation, and PCoA (unweighted Unifrac) revealed a clear divergence in community structure, collectively supporting a distinct microbiota profile in LF rats (Fig. 1A).

Fig. 1.

Characterization of GM in control and model rats. A Comparison of β-diversity of GM between control and model rats. B Phylum-level composition of GM in the two groups. C Differences in GM between control and model rats at different taxonomic levels. GM, gut microbiota; n=10 per group

At the phylum level, the GM of the two groups of rats were mainly Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria and Verrucomicrobia (Fig. 1B). To further dissect the GM changes at a higher taxonomic resolution, we analyzed the differences from phylum to genus levels (Fig. 1C). Compared with the control group, the model group exhibited significantly decreased abundance of p_Actinobacteria, c_Actinobacteria, c_Betaproteobacteria, o_Burkholderiales, o_Bifidobacteriales, f_Bifidobacteriaceae, g_Parasutterella, g_Bifidobacterium, g_Olsenella, g_Anaerostipes, g_Allobaculum, g_Saccharibacteria_genera_incertae_sedis. In contrast, the model group showed significantly increased abundance of c_Clostridia, o_Clostridiales, o_Enterobacteriales, f_Clostridiaceae_1, f_Enterobacteriaceae, g_Clostridium_sensu_stricto, g_Terrisporobacter, g_Erysipelotrichaceae_incertae_sedis, g_Cellulosilyticum, g_Sutterella, g_Escherichia_Shigella, g_Lachnospiracea_incertae_sedis. Statistical significance was determined using the Mann-Whitney U test with adjusted P < 0.05; detailed results, including exact P values and effect sizes, are presented in Fig. 1C.

Moreover, we analyzed the GM of the two groups of rats using LEfSe and a cladogram for the top 50 taxa with LDA > 2, P < 0.05, and the smallest and bar graphs for the top 10 microbiota with the smallest P values in each group. From the cladogram, we observed the evolutionary relationships of the different microbiota between the two groups (Fig. 2A). As visualized in the bar graph, the model group was significantly enriched in c_Clostridia, o_Clostridiales, f_Clostridiaceae_1, g_Clostridium_sensu_stricto, g_Terrisporobacter, f_ Enterobacteriaceae, g_Escherichia_Shigella, g_Sutterella, g_Cellulosilyticum, and g_Erysipelotrichaceae_incertae_sedis. By contrast, in control group, g_ Parasutterella, g_Anaerostipes, o_Burkholderiales, c_Betaproteobacteria, f_Bifidobacteriaceae, g_Olsenella, g_Bifidobacterium, o_ Bifidobacteriales, p_Actinobacteria, and g_Allobaculum were significantly enriched (Fig. 2B).

Fig. 2.

LEfSe analysis of GM and functional prediction in control and model rats. Cladogram (A) and bar graph (B) of LDA scores for LEfSe analysis of GM in control and model rats. C Differences in KEGG metabolic pathways predicted by GM function between control and model rats. LEfSe, linear discriminant analysis effect size. GM, gut microbiota; KEGG, Kyoto Encyclopedia of Genes and Genomes; n=10 per group

To explore the potential functional differences between the GM of LF rats and controls, we used PICRUSt2 for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway prediction and GM function analysis. The results showed that 102 KEGG pathways in the GM of LF and control rats were significantly different (Table S4). To identify the most significantly altered KEGG pathways, we applied a combined filtering strategy: pathways with an adjusted P < 0.05 and an effect size > 3 were retained. Among these, the top 10 pathways ranked by log2 fold change (log2FC) in descending order were as follows: “D-Arginine and D-ornithine metabolism, Amoebiasis, Carotenoid biosynthesis, Nitrotoluene degradation, Primary bile acid biosynthesis, Protein processing in endoplasmic reticulum, Secondary bile acid biosynthesis, Phosphonate and phosphinate metabolism, RNA transport and Epithelial cell signaling in Helicobacter pylori infection (Fig. 2C)”. Notably, two BA biosynthesis pathways, PBA biosynthesis (effect size = 3.1815, log2FC = 0.0603) and SBA biosynthesis (effect size = 3.17998, log2FC = 0.0517), exhibited strong effect sizes despite modest log2FC values. However, PBAs are synthesized exclusively in the host liver, and the GM lacks the complete enzymatic machinery for their de novo synthesis. Therefore, the predicted enrichment of “PBA biosynthesis” does not indicate microbial production of PBAs. Instead, it likely reflects increased abundance of bacterial genes involved in downstream modification, steroid core oxidation-reduction, or other shared metabolic steps that are annotated to this pathway in the KEGG database. In contrast, the “SBA biosynthesis” pathway more directly represents microbial transformation of BAs (e.g., deconjugation and dehydroxylation). With these clarifications, the results suggest a robust and biologically meaningful perturbation in microbial BA-related metabolism, which may play a role in LF progression. Further validation via metatranscriptomics or targeted functional assays is warranted.

Characteristics of BA metabolism in rats with LF

BAs are metabolites of the GM, and to study the changes in BA metabolism in rats with LF, we quantified 22 common BA components in the serum of rats in the control and model groups. After the multivariate statistical analysis of the serum BA profiles of the two groups of rats, we found significant differences in serum BA metabolism between the two groups, and the two groups began to separate in the PCA plot, although a small overlap persisted (Fig. 3A). The separation of the two groups was more obvious in the supervised OPLS-DA plot (Fig. 3B), and the permutation test of the OPLS-DA analysis revealed that the model had not been overfitted (Q² = 0.702, R²Y = 0.845, Fig. 3C). VIP score plots showed that Tα-MCA, TCDCA, GCDCA, GCA, TCA, THDCA, TUDCA, GHDCA, GUDCA, and α-MCA had VIP scores of > 1, and were the components that contributed more to the model (Fig. 3D).

Fig. 3.

Characteristics of BA metabolism in rats with LF. A–D Multivariate statistical analyses of serum BAs in control and model rats, including (A) PCA plots, B OPLS-DA plots, C permutation test of OPLS-DA plots, and (D) VIP score plots of the OPLS-DA plots. E Comparison of univariate analysis of 22 serum BAs in control and model rats. F Fold-change analysis of the 22 serum BAs in the control and model groups. G–P BA metrics in control and model rats, organized by functional modules of BA metabolism. Hepatic synthesis (PBAs,12α-OH BAs, non-12α-OH BAs, and 12α-OH BAs/non-12α-OH BAs, G–J), intestinal transformation (SBAs, PBAs/SBAs, CBAs, UCBAs, CBAs/UCBAs, K–O) and enterohepatic load (TBA, P) are shown in order. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. BA, bile acid; LF, liver fibrosis; PCA, principal component analysis; OPLS-DA, orthogonal partial least-squares discriminant analysis; TBA, total bile acid; PBAs, primary bile acids; SBAs, secondary bile acids; CBAs, conjugated bile acids; UCBAs, unconjugated bile acids; n=10 per group

Mann–Whitney U test analysis showed that the serum concentrations of 19 BAs, i.e., CA, CDCA, UDCA, α-MCA, β-MCA, HDCA, GCA, GCDCA, GUDCA, GDCA, GLCA, GHDCA, TCA, TCDCA, TUDCA, Tα-MCA, TDCA, TLCA, and THDCA, were significantly increased in concentration (Fig. 3E). Detailed statistical results are presented in Table S5.

As shown in Fig. 3F, Log2 FC analysis of the 22 serum BAs in the control and model groups showed that GCDCA, GHDCA, GUDCA, GCA, Tα-MCA, TCDCA, CDCA, α-MCA, GDCA, GLCA, TCA, UDCA, HDCA, TUDCA, THDCA, CA, β-MCA and TLCA all had FC > 2. The FC and Log2 FC values are presented in Table S6.

To systematically characterize BA metabolic alterations in LF, we classified serum BA parameters into three functional modules reflecting the physiological process of BA metabolism: hepatic synthesis (de novo synthesis and pathway preference), intestinal transformation (dehydroxylation and deconjugation), and enterohepatic load (total circulating pool size) (Fig. 3G-P). For hepatic synthesis, PBAs were significantly elevated in the model group relative to controls (P < 0.05, Fig. 3G), indicating enhanced de novo BA synthesis. Both 12α‑OH BAs and non‑12α‑OH BAs were also markedly increased (both P < 0.05, Fig. 3H, I), whereas the 12α‑OH/non‑12α‑OH BA ratio was comparable between groups (P > 0.05, Fig. 3J), demonstrating that the relative flux through the classical (12α‑OH‑producing) and alternative (non‑12α‑OH‑producing) synthetic pathways remained unaltered. Regarding intestinal transformation, SBAs were significantly elevated (P < 0.05, Fig. 3K), while the PBAs/SBAs ratio remained unchanged (P > 0.05, Fig. 3L), suggesting that dehydroxylation activity increased proportionally with the total BA pool without selective enhancement. Both conjugated BAs (CBAs) and unconjugated BAs (UCBAs) were elevated (both P < 0.05, Fig. 3M, N), whereas the CBAs/UCBAs ratio was unaltered (P > 0.05, Fig. 3O), indicating that deconjugation activity also increased proportionally without selective enhancement. Finally, with respect to enterohepatic load, total BA (TBA) were significantly elevated in the model group (P < 0.05, Fig. 3P), confirming an expanded circulating BA pool. Collectively, these findings demonstrate that LF is characterized by a quantitative expansion of the circulating BA pool, arising from increased hepatic synthesis and/or impaired clearance, rather than selective metabolic remodeling of BA metabolism. Detailed statistical results and calculation formulas for BA metrics are presented in Table S7 and Table S8, respectively.

Correlation analysis of GM and BA metabolism in LF rats

Crosstalk between the GM and BAs plays an important role in the process of LF; thus, we further investigated the correlation between the 20 microbiota organisms identified as significantly different between the control and model groups in the LEfSe analysis (adjusted P < 0.05, LDA > 2.0) with the 10 BAs that were found to differ in the serum between the groups (adjusted P < 0.05, VIP > 1, FC > 2), using Spearman correlation analyses (Fig. 4). Among them, TCA showed a significant strong negative correlation with g_Anaerostipes and p_Actinobacteria (r_s of -0.8016 and − 0.8000, P = 0.0031 and 0.0051, respectively), while TCDCA showed a significantly strong negative correlation with g_Bifidobacterium, o_Bifidobacteriales, and f_Bifidobacteriaceae (all r_s = -0.8105, all P = 0.0002). The remaining correlation coefficients, r_s, and P values are shown in Supplementary Table S9 and S10, respectively.

Fig. 4.

Correlation analysis of GM and BAs in rats with LF. Red and blue indicate positive and negative correlations, respectively; the darker the color, the stronger the correlation. *P < 0.05,**P < 0.01,***P < 0.001. GM, gut microbiota; BAs, bile acids; n=10 per group

Modulation of GM by FMT and probiotics in rats with LF

To assess the modulatory effects of FMT and probiotics on the GM of rats with LF, we analyzed the GM of rats in the control, model, FMT, and probiotics groups. As shown in Fig. S3A, the rarefaction curves indicated that the sequencing data of the samples in each group were reasonable. Venn analysis showed (Fig. 5A) that 4694 ASVs were aggregated in the four groups of rats. Among these, the FMT group shared 1039 ASVs with the model group, while 881 ASVs were specific to the FMT group and 959 ASVs were specific to the model group. Overall, 1128 ASVs were shared between the probiotics group and the model group, while 1216 ASVs were specific to the probiotics group and 870 ASVs were specific to the model group. α- analysis showed no significant difference in the α-diversity index of the GM among the four groups of rats, indicating that FMT and probiotics did not alter the abundance and homogeneity of the GM in rats with LF (Fig. S3B).

Fig. 5.

GM profiles and comparisons among control, model, FMT, and probiotics groups. A Venn diagram; B β-diversity; C Differences in GM between four groups of rats at different taxonomic levels. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001. Different letters represent significant differences. GM, gut microbiota; n=10 per group

According to the results of the PERMANOVA statistical analysis, in terms of the β-diversity analysis, based on unweighted Unifrac distance, significant differences existed between the FMT and model groups (unweighted Unifrac: P = 0.008, R2 = 0.0846) and between the probiotics and model groups (unweighted Unifrac: P = 0.005, R² = 0.0679), which indicated that the FMT and probiotics could to some extent alter the structure of the GM community in LF rats. Although we did not observe a complete separation between the groups in the Unifrac distance-based PCoA visualization analysis (Fig. 5B).

We further characterized the modulation of GM dysbiosis in LF rats following FMT and probiotics interventions at multiple taxonomic levels. As illustrated in Fig. 5C, FMT intervention led to significant increases in the relative abundances of p_Actinobacteria, c_Actinobacteria, c_Erysipelotrichia, o_Bifidobacteriales, o_Erysipelotrichales, f_Bifidobacteriaceae, f_Erysipelotrichaceae, g_Bifidobacterium, g_Allobaculum, and g_Olsenella, accompanied by marked reductions of c_Clostridia and o_Clostridiales. In contrast, probiotics resulted in enrichment of f_Carnobacteriaceae and g_Atopostipes, while decreasing the abundances of c_Clostridia, o_Clostridiales, f_Lachnospiraceae and g_Lachnospiracea_incertae_sedis. Detailed statistical results are presented in Table S11.

Effects of FMT and probiotics on BA metabolism in rats with LF

To assess the effect of FMT and probiotics on BA metabolism after improving the GM of LF rats, we first performed a multivariate analysis of the BA profiles of the control, model, FMT, and probiotics groups. As shown in Fig. 6A–D, in the OPLS-DA model, the FMT group was completely separate from the model group (Q² = 0.658, R²Y = 0.885), and the probiotics group was also separate from the model group (Q² = 0.693, R²Y = 0.736), suggesting that FMT and probiotics could alter BA metabolism in LF rats.

Fig. 6.

Effects of FMT and probiotics on BA metabolism in rats with LF. A–D Multivariate statistical analyses of serum BAs in four groups of rats, including (A) OPLS-DA plots between the model group and the FMT group and (B) permutation test of its OPLS-DA plots as well as (C) OPLS-DA plots between the model group and the probiotics group and (D) permutation test of its OPLS-DA plots. E Univariate analysis of 22 serum BAs in four groups of rats. *P < 0.05, **P < 0.01 for the FMT group compared with the model group. ^#P < 0.05, ^##P < 0.01, ^###P < 0.001 for the probiotics group compared with the model group. F-O BA metrics in four groups of rats, organized by functional modules of BA metabolism. Hepatic synthesis (PBAs,12α-OH BAs, non-12α-OH BAs, and 12α-OH BAs/non-12α-OH BAs, F–I), intestinal transformation (SBAs, PBAs/SBAs, CBAs, UCBAs, CBAs/UCBAs, J–N) and enterohepatic load (TBA, O) are shown in order. *P < 0.05,**P < 0.01,*** P < 0.001. BA, bile acid; LF, liver fibrosis; OPLS-DA, orthogonal partial least-squares discriminant analysis; TBA, total bile acid; PBAs, primary bile acids; SBAs, secondary bile acids; CBAs, conjugated bile acids; UCBAs, unconjugated bile acids; n=10 per group

As mentioned previously, the concentrations of 19 BAs, including CA and CDCA, were higher in the model group than in the control group. We further compared the differences in BA concentrations between the FMT and model groups, and between the probiotics and model groups, using the Mann–Whitney U test. As shown in Fig. 6E, the concentrations of 5 serum BAs (i.e., CDCA, UDCA, HDCA, TUDCA and THDCA) were decreased in the FMT group compared with the model group, and the difference was significant (adjusted P < 0.05). The concentrations of 17 BAs (i.e., CA, CDCA, UDCA, β-MCA, HDCA, GCA, GCDCA, GUDCA, GDCA, GHDCA, TCA, TCDCA, TUDCA, Tα-MCA, TDCA, TLCA, and THDCA) were reduced in the serum BAs in the probiotics group, and the difference was significant (adjusted P < 0.05). Detailed statistical results are presented in Tables S12-13.

To evaluate the impact of FMT and probiotics on BA metabolism, we applied the same functional framework—hepatic synthesis, intestinal transformation, and enterohepatic load—to analyze serum BA metrics in the control, model, FMT, and probiotics groups (Fig. 6F-O). For hepatic synthesis, PBAs were significantly reduced in both intervention groups compared with the model group (both adjusted P < 0.05, Fig. 6F), indicating suppression of de novo BA synthesis. Both 12α‑OH BAs and non‑12α‑OH BAs were also reduced: in the probiotics group, both were significantly decreased (both adjusted P < 0.05, Fig. 6G-H); in the FMT group, non‑12α‑OH BAs showed a significant reduction (adjusted P < 0.05), whereas 12α‑OH BAs exhibited only a decreasing trend. Notably, the 12α‑OH/non‑12α‑OH BA ratio remained unchanged in both groups (adjusted P > 0.05, Fig. 6I), demonstrating that the relative flux through the classical and alternative synthetic pathways was unaltered. Regarding intestinal transformation, SBAs were significantly decreased by both interventions (both adjusted P < 0.05, Fig. 6J), while the PBAs/SBAs ratio remained unchanged (adjusted P > 0.05, Fig. 6K), suggesting that dehydroxylation activity decreased proportionally without selective alteration. Both CBAs and UCBAs were reduced: in the probiotics group, both were significantly decreased (both adjusted P < 0.05, Fig. 6L-M); in the FMT group, UCBAs showed a significant reduction (adjusted P < 0.05), whereas CBAs exhibited only a decreasing trend. The CBAs/UCBAs ratio remained unchanged in both groups (adjusted P > 0.05, Fig. 6N), indicating that deconjugation activity also decreased proportionally without selective alteration. Finally, with respect to enterohepatic load, TBA were significantly reduced by both interventions (both adjusted P < 0.05, Fig. 6O), confirming that FMT and probiotics alleviate the expanded circulating BA pool in LF. Collectively, these findings demonstrate that both FMT and probiotics attenuate LF‑associated BA accumulation by reducing the global BA pool size, likely through decreased hepatic synthesis and/or enhanced clearance, rather than through selective metabolic remodeling of BA metabolism. Detailed statistical results are presented in Table S14.

Effects of FMT and probiotics on LF

Histopathological amelioration of LF

To evaluate the effects of FMT and probiotics on LF after modulating the GM–BA axis, we stained the liver tissues of rats in each group with HE (Fig. 7A), Masson staining (Fig. 7B), and the percentage of Masson-positive area was quantified. As shown in Fig. 7A-B, the liver tissue of rats in the control group had a clear confluent area, and the liver cord structure was neatly arranged in a radial arrangement. In contrast, the liver cord structure of rats in the model group was destroyed, the confluent area was structurally disorganized, a large number of collagen fibers were deposited, and pseudolobules were clearly formed, which redivided the liver tissue. In the FMT and probiotics groups, collagen fiber deposition was significantly reduced compared with that in the model group, and the pseudoleaflet structure was not completely formed.

Fig. 7.

Effects of FMT and probiotics on LF. HE (A) and Masson (B) staining of rat liver tissues in four groups (10×). Collagen fiber deposition is shown by black arrows. C Quantitative analysis of Masson-positive area (%) in each group (n=10 per group). D-G Effect of FMT and probiotics on serum LF indices in LF model rats (n=6 per group). H-I Correlation analyses between key GM (H) and BAs (I) with LF indices, respectively (n=6 per group). LF, liver fibrosis; HE, hematoxylin-eosin; PIIINP, procollagen type iii n-terminal peptide; CoL_IV, collagen type IV; HA, hyaluronic acid; LN, laminin

As shown in Fig. 7C, the Masson-positive area was markedly increased in the Model group compared to the Control group (adjusted P < 0.0001), confirming successful establishment of LF. In contrast, both FMT and probiotics treatments significantly reduced collagen deposition relative to the Model group, with reductions of 33.2% (FMT, adjusted P < 0.05) and 36.9% (Probiotics, adjusted P < 0.05), respectively. Detailed statistical results are presented in Table S15.

Reduction of serum fibrosis markers

In addition, we measured the levels of LF indicators (PIIINP, CoL IV, HA, and LN), in the serum of rats in each group by ELISA. As shown in Fig. 7D-G, all four these indices in the serum of rats in the model group were significantly elevated compared with those in the control group (adjusted P < 0.05). Compared with the model group, the liver four indices in both the FMT and probiotics groups were significantly lower (adjusted P < 0.05), indicating that FMT and probiotics reduced the concentration of the four serum LF markers in rats with LF. Detailed statistical results are presented in Table S15.

Associations between key GM, BAs, and fibrotic markers

To determine whether the anti‑fibrotic effects were linked to specific alterations in the GM–BA axis, Spearman correlation analysis was performed between the 20 differentially abundant GM, 10 key BAs, and five fibrosis markers across all groups (n = 6 per group). As shown in Fig. 7H, beneficial GM enriched by FMT—including g_Allobaculum and g_Olsenella—exhibited significant negative correlations with PIIINP (r_S>0.5, P < 0.05). Conversely, pro‑fibrotic GM, such as c_Clostridia and o_Clostridiales which were reduced by FMT and probiotics, showed positive correlations with Masson‑positive area (%) and LN (r_S>0.5, P < 0.05). Regarding BAs (Fig. 7I), conjugated species—particularly THDCA, TUDCA, GUDCA, and GHDCA—were positively correlated with HA (r_S>0.7, P < 0.0001). The remaining P values and r_s are shown in Tables S16 and S17, respectively. These correlation analyses reveal that the anti-fibrotic effects of FMT and probiotics are statistically linked to specific shifts in the GM-BA axis.

Discussion

LF plays an important role in the progression of various chronic liver diseases. However, effective diagnostic and therapeutic approaches are lacking [73, 74]. The GM–BA axis is a potential target for the diagnosis and treatment of LF, but further studies are needed to clarify the mechanism before clinical application can be considered [23, 72, 75]. Therefore, we constructed an LF rat model to explore the characteristics of GM and BA metabolism and their correlation in LF rats, based on microbiome and metabolomic analyses. We also investigated the role of the altered GM–BA axis on LF by modulating the GM through FMT or probiotics. We found that the GM–BA axis was significantly disturbed in rats with LF, and that both FMT and probiotics were associated with improvements in dysregulated GM and disturbed BA metabolism, concomitant with attenuated LF.

The GM plays an important role in many chronic liver diseases, and several studies demonstrated alterations in the GM in patients with different LF etiologies [14, 76–78]. Our results showed that the GM of rats with carbon tetrachloride-induced LF was significantly altered. Although no significant difference was observed in the α-diversity of the GM between the control and model rats, β-diversity differed significantly. At the phylum level, the dominant microbiota in both groups were Firmicutes, Actinobacteria, Bacteroidetes, Proteobacteria, and Verrucomicrobia. We found that the abundance of Actinobacteria, as well as of Bifidobacteriales, Bifidobacteriaceae, and Bifidobacterium within the Actinobacteria, was significantly reduced in LF model rats, which aligned with previous findings [79–81]. Bifidobacteria are the most representative beneficial bacteria of the Actinobacteria and play an important role in maintaining the gut barrier; their reduction leads to increased gut permeability [79, 82]. Following increased gut permeability, lipopolysaccharide, a cell wall component of Gram-negative bacteria, promotes LF through activation of the MyD88–NF-κB signaling pathway, or by affecting the TGFβ/SMAD signaling pathway, after passing through gaps in the gut barrier and entering the liver to bind to TLRs [83–87]. Additionally, the abundances Clostridia, Clostridiales, Clostridium sensu stricto, and Clostridiaceae1, belonging to Firmicutes, were significantly increased in LF model rats, which was in line with the results of many previous studies [88, 89]. Some studies have also found decreased Firmicutes in non-alcoholic liver disease [14, 15, 80] and Clostridiales XIV in cirrhotic patients [90]. The difference may be related to the different species included in the analysis or to the different degrees and etiologies of the disease. Additionally, the abundance of Erysipelotrichaceae_incertae_sedis belonging to Firmicutes was significantly reduced, which is also similar to previous studies [76]. Although we did not find significant differences in Proteobacteria at the phylum level, Enterobacteriales, Enterobacteriaceae, and Escherichia _Shigella belonging to Proteobacteria increased significantly in abundance. This is in agreement with many previous studies, such as Raman [91] et al. who found elevated abundance of Gammaproteobacteria in a study of nonalcoholic fatty liver disease and Shen [92] et al. who found significant elevation of Enterobacteriaceae, Escherichia _Shigella in patients with LF. A significant abundance of Enterobacteriaceae has been noted in patients with cirrhosis [17, 90, 93–95]. Moreover, Minicis et al. [81] found in mice with LF constructed with a high-fat diet and bile duct ligation that transplantation of Gram-negative bacteria exacerbated LF with an increase in Enterobacteriaceae and a massive decrease in Bifidobacteriaceae.

BAs are important metabolites of the GM and involved in the LF process through various pathways [23, 33, 36]. The serum TBA to cholesterol ratio and TBA levels combined with age, body mass index, and glucose, aspartate aminotransferase, and cholesterol levels can predict the degree of LF more accurately [96–98]. Our analysis of the serum BA profile of rats with carbon tetrachloride-induced LF revealed that serum BA metabolism was significantly disturbed in LF rats, causing a significant increase in serum TBA concentration, consistent with the findings of previous studies [99]. Excessive accumulation of BAs in the body promotes the progression of LF [72, 100, 101]. We measured 22 common BAs, of which 19 were significantly elevated in LF model rats. Among these, CA, GCA, TCA, GDCA, and TDCA are 12α-OH BAs synthesized in the classical pathway. Xie et al. [33] found that 12α-OH BAs were significantly elevated in the serum of patients with non-alcoholic fatty liver disease, and later demonstrated through animal experiments that TDCA and GDCA could, through TGR5-mediated signaling via p38MAPK and ERK1/2 significantly promote LF. Interestingly, we did not observe any changes in secondary UCBAs, such as DCA, LCA, and HCA. DCA and LCA are often considered to be deleterious BAs that promote disease progression due to their high hydrophobicity and toxicity to cells [102, 103]. We further found by OPLS-DA multivariate analysis that 10 BAs were major distinctions between the model and control groups. Among these 10 significantly different BAs, all of them were conjugated BAs except α-MCA, and all of them were PBAs in rats except THDCA and GHDCA. This suggests that the elevation of serum total BAs in rats with LF is mainly the elevation of primary conjugated BAs [32, 34, 104, 105]. Among them, conjugated BAs, especially TCA, may promote LF through activation of ERK1/2 and AKT pathways, among others, upon binding to S1PR2 [39, 40, 45, 106]. The study of Svegliati et al. [107] also found that GCDCA, upon binding to EGFR, may induce hepatic stellate cell proliferation leading to LF through activation of the EGFR/ERK signaling pathway [41]. Moreover, a study by Zhang [108] et al. showed that GCDCA may promote LF by promoting hepatic stellate cell proliferation and motility through the regulation of the p38/JNK signaling pathway. However, it is important to note that the ratios of PBAs/SBAs, CBAs/UCBAs and 12α-OH/non-12α-OH BAs remained unchanged between the model and control groups. This indicates that the observed BA elevation reflects a global expansion of the circulating BA pool-likely resulting from increased hepatic synthesis and/or impaired clearance-rather than a selective shift in synthetic pathway preference, deconjugation efficiency, or dehydroxylation activity.

Although the liver is the only site of BA synthesis, GM metabolism diversifies the BA species [25–28]. Dysregulation of the GM will result in altered BA metabolism. In this study we used the PICRUSt2 tool to predict the functionality of the GM and found predicted enrichment of SBA and PBA biosynthesis pathways in LF model rats. As noted in the Results, the PBA biosynthesis prediction should not be interpreted as microbial de novo synthesis of PBAs. A significant increase in the synthesis of PBAs, and consequently increased microbial conversion to SBAs, in rats with LF would lead to an increase in the excretion of SBAs in the feces, which would result in a significant increase in PBAs entering the enterohepatic circulation, consistent with our finding that the elevation of serum TBA was mainly due to an elevation of PBAs. Beneficial bacteria, such as Bifidobacteria, have bile salt hydrolase activity [109, 110], which was significantly reduced in LF rats and may be related to the significant increase in serum CBAs. Our correlation analysis of GM and serum BAs also revealed that many of the primary conjugated BAs, such as TCDCA, were significantly negatively correlated with Bifidobacterium, Bifidobacteriales, and Bifidobacteriaceae. Although Clostridiales [110] also has bile salt hydrolase activity, Clostridiales, Clostridia, Clostridiaceae_1 and Clostridium_sensu_stricto in the present study were significantly increased in LF rats and were significantly positively correlated with primary conjugated BAs. This may be due to the stronger bile salt hydrolase activity of Bifidobacterium than Clostridium.

FMT and probiotics are two important methods for improving GM [58–61]. In this study, we used FMT and probiotics to modulate the GM, thus altering the GM–BA axis. We found that, when fecal microbiota fluids from normal rats were transplanted to rats with LF, Actinobacteria, Erysipelotrichia, Bifidobacteriales, Erysipelotrichaceae, Bifidobacteriaceae, Bifidobacterium, Allobaculum and Olsenella, which were reduced in LF model rats, were increased after FMT, whereas Clostridia and Clostridiales which were increased in LF model rats, were decreased after FMT, suggesting that FMT improves the dysregulated GM associated with LF. Moreover, FMT also reduced the levels of 5 BAs, including CDCA, UDCA, HDCA, TUDCA and THDCA, which were significantly increased in LF model rats. Notably, although the ratios of PBAs/SBAs, CBAs/UCBAs, and 12α-OH/non-12α-OH BAs remained unchanged following FMT, the intervention significantly reduced the absolute levels of PBAs, non-12α-OH BAs, UCBAs, and TBA, indicating that the reductions reflected a proportional decrease in the global BA pool rather than selective modulation of dehydroxylation, deconjugation or synthetic pathway preference. After FMT improved the disordered GM–BA axis in rats with LF, HE and Masson staining of liver tissues and serum LF indicators showed that the degree of LF was reduced. Currently, FMT has been well documented for use against C. difficile infections in adults and children and is included in clinical practice guidelines [111, 112]. FMT has also shown significant efficacy in the treatment of ulcerative colitis and Crohn’s disease [113–116] and liver diseases such as hepatic encephalopathy [117], non-alcoholic fatty liver disease [118], alcoholic liver injury [119] and liver failure [120]. The mechanism by which FMT can treat enterohepatic diseases may be related to its ability to remodel the GM and improve the GM–BA axis.

Similarly, in the probiotics group, supplementation with the Bifidobacterium quadruple viable tablet increased Carnobacteriaceae and Atopostipes, while decreasing the abundances of Clostridia, Clostridiales, Lachnospiracea_incertae_sedis and Lachnospiraceae, thereby improving the dysregulated GM associated with LF. Interestingly, we increased beneficial bifidobacteria by FMT, but Bifidobacteria were not increased in the probiotics group that received gavage of Bifidobacterium tetrads, which may be due to the reason that the organism is more dependent on native probiotics and exogenous supplementation is difficult to colonize [121]. Additionally, the probiotics also reduced serum BAs, such as CA, CDCA, UDCA, β-MCA, HDCA, GCA, GCDCA, GUDCA, GDCA, GHDCA, TCA, TCDCA, TUDCA, Tα-MCA, TDCA, TLCA, and THDCA, which are 17 BAs that are significantly increased in rats with LF. Similarly, although the ratios of PBAs/SBAs, CBAs/UCBAs and 12α-OH/non-12α-OH BAs remained unchanged following probiotics intervention, the treatment significantly reduced the absolute levels of PBAs, 12α-OH BAs, non-12α-OH BAs, CBAs, UCBAs, and TBA, indicating that the reductions similarly reflected a proportional decrease in the global BA pool rather than selective modulation of dehydroxylation, deconjugation, or synthetic pathway preference. In the present study, probiotics attenuated the degree of LF in rats by ameliorating the disturbed GM–BA axis in rats with LF. Currently, probiotics have good efficacy in the treatment of enterohepatic diseases due to their important functions such as improving the disordered state of GM and their metabolites, repairing the gut barrier, and regulating the body’s immunity [122, 123]. Liu et al. [72] found that the probiotic Lactobacillus rhamnosus could regulate GM and thus increase BA excretion and improve fibrosis induced by excess BAs in the liver in both humans and animals. With the depth of research, the future application of probiotics to improve GM and their metabolites for the treatment of clinical diseases will become more and more widespread.

Despite the potential of our findings to be good reference for the diagnosis and treatment of LF, our study also had some limitations. First, while the CCl₄-induced LF model is a well-established tool for investigating core fibrogenic mechanisms, it reflects only one etiology among the heterogeneous spectrum of human LF. The distinct GM and BA profiles identified in this study may therefore be model-specific. Accordingly, future investigations employing LF models induced by diverse etiologies are warranted to validate the generalizability of our findings and to further elucidate etiology-specific mechanisms underlying the GM-BA axis. Second, although only male animals were included in this study to control for potential confounding effects of hormonal fluctuations related to the estrous cycle, this design precludes the assessment of sex-specific responses to fibrogenic injury and therapeutic interventions. Future studies involving both male and female animals are warranted to elucidate the potential sex-dependent mechanisms. Third, the sample size of our study is relatively small, and a multi-center, large sample size clinical study will have greater significance in guiding clinical practice. Fourth, to investigate GM composition, we employed 16 S rRNA sequencing, which provides taxonomic insights but does not directly assess microbial function. Accordingly, the functional predictions derived from PICRUSt2 are inferred rather than measured and require orthogonal validation. In particular, the inferred enrichment of the “PBA biosynthesis” pathway should not be misinterpreted as microbial de novo synthesis of PBAs, as this pathway is primarily host-specific. Future studies utilizing shotgun metagenomic sequencing, metatranscriptomics, or targeted qPCR for functional genes (e.g., bsh, bai) are warranted to confirm the predicted alterations in BA metabolism and to elucidate the underlying mechanisms. Fifth, this study investigated only the metabolism of serum BAs, combining the analysis of liver and fecal BAs should be more comprehensive to elucidate the role of BA metabolism in LF in terms of the synthesis, metabolism, and transport of BAs. Sixth, while our integrative microbiomics and metabolomics approach revealed significant associations between the GM, BAs and LF, the observational design precludes any causal inference. Specifically, although FMT and probiotics normalized several serum BA levels and improved liver histology, we cannot conclude that these beneficial effects were directly mediated by microbial BA metabolism. Such favorable outcomes may also be secondary to other microbiome-derived metabolites (e.g., short-chain fatty acids), restoration of gut barrier integrity, or modulation of host immunity. Thus, our findings should be regarded as hypothesis-generating rather than definitive evidence for a causal role of the GM–BA axis in LF. Furthermore, the absence of functional data layers such as transcriptomics or proteomics limits the depth of mechanistic insights. Future studies employing causal experimental designs, integrated with multi-omics approaches (e.g., transcriptomics, proteomics), are warranted to further elucidate the molecular mechanisms by which the GM–BA axis regulates fibrogenic signaling pathways.

Conclusions

Both the GM profile and the BA metabolic profile were significantly altered in rats with LF, and the altered GM was significantly correlated with changes in BAs. The differential GM and BAs we screened may serve as candidate biomarkers for LF. Furthermore, modulation of the GM, and by extension the GM–BA axis, via FMT or probiotics was associated with reduced LF severity. These findings underscore a robust association between the GM–BA axis and LF progression, providing a rationale for future mechanistic and causal studies, while acknowledging that causality remains to be established.

Abbreviations

ANOVA

Analysis of variance

BA

Bile acid

CA

Cholic acid

CBAs

Conjugated bile acids

CDCA

Chenodeoxycholic acid

CoL IV

Collagen type IV

DCA

Deoxycholic acid

FMT

Fecal microbiota transplantation

GCA

Glycocholic acid

GCDCA

Glycochenodeoxycholic acid

GDCA

Glycodeoxycholic acid

GLCA

Glycolithocholic acid

GM

Gut microbiota

GUDCA

Glycoursodeoxycholic acid

HA

Hyaluronic acid

HCA

Hyocholic acid

HDCA

Hyodeoxycholic acid

HE

Hematoxylin and eosin

KEGG

Kyoto Encyclopedia of Genes and Genomes

LCA

Lithocholic acid

LEfSe

Linear discriminant analysis effect size

LF

Liver fibrosis

LN

Laminin

NMDS

Non-metric multidimensional scaling

OPLS-DA

Orthogonal partial least-squares discriminant analysis

PBAs

Primary bile acids

PCA

Principal component analysis

PCoA

Principal coordinate analysis

PIIINP

Procollagen type III N-terminal peptide

SBAs

Secondary bile acids

TBA

Total bile acid

TCA

Taurocholic acid

TCDCA

Taurochenodeoxycholic acid

TDCA

Taurodeoxycholic acid

THDCA

Taurohyodeoxycholic acid

TLCA

Taurolithocholic acid

TUDCA

Tauroursodeoxycholic acid

Tα-MCA

Tauro-α-muricholic acid MCA

UCBAs

Unconjugated bile acids

UDCA

Ursodeoxycholic acid

β-MCA

β-Muricholic acid

Authors’ contributions

YLZ: Conceptualization, Methodology, Software, Writing - original draft. HZG: Methodology, Supervision, Visualization. ZJL: Investigation, Project administration. JHX: Resources, Validation. XJW: Data curation, Supervision. DZ: Validation, Conceptualization. LFR: Investigation. XJS: Writing - review & editing. LZ: Funding acquisition, Project administration, Resources, Supervision, Writing - review & editing. All authors have read and approved the final version of the manuscript.

Funding

This work was supported by the Gansu Provincial University Teachers’ Innovation Fund Project (2025B-010), Fund of the First Hospital of Lanzhou University (ldyyyn2023-87), National Natural Science Foundation of China (31960236), Open Fund of Gansu Key Laboratory of Microorganisms for Extreme Environments (EEMRE202403), Joint Scientific Research Foundation of Gansu Province (23JRRA1498) and Lanzhou Talent Innovation and Entrepreneurship Project (2019-RC-34).

Data availability

The data that support the findings of this study are available from the corresponding author upon request. The data have been stored in a public database (10.6084/m9.figshare.26889613).

Declarations

Ethics approval and consent to participate

All experiments and procedures were approved by the Laboratory Animal Ethics Committee of Lanzhou University (ethics approval number: LDYYLL2023-294). This study involves animal experiments only and is not a clinical trial involving human participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.