Emodin ameliorates cholestatic liver injury by regulating bile acid metabolism and gut microbiota in mice

Abstract

BACKGROUND

Cholestatic liver injury (CLI) is associated with accumulation of bile acids (BAs) in the liver and lacks effective treatment, ultimately progressing to end-stage liver diseases. Emodin (Emo) is an active compound of several traditional Chinese medicines and exhibits low oral bioavailability but still exerts a protective effect against CLI. Therefore, we speculate that the gut microbiota mediates its effect. To the best of our knowledge, the role of gut microbiota-mediated metabolism in the therapeutic effects of Emo has not been elucidated.

AIM

To investigate the mechanism of Emo associated with the BA metabolism and microbial communities in CLI mice.

METHODS

The CLI model was established by administration of α-naphthylisothiocyanate. Histopathological and biochemical analyses were performed to assess the protective effects of Emo. Untargeted metabolomics and targeted BA metabolomics were integrated and conducted to investigate the effect of Emo on endogenous metabolites. The gut microbiome profiles were analyzed by 16S rRNA sequencing. Fecal microbiota transplantation was used to evaluate the contribution of gut microbiota.

RESULTS

Emo significantly attenuated α-naphthylisothiocyanate-induced CLI. Integrated untargeted metabolomic and targeted BA profiling implied that Emo markedly decreased CLI of mice by affecting BA metabolism. Emo influenced the intestinal microbiota composition, upregulated the abundance of Lachnoclostridium, and downregulated the abundance of Enterococcus, Escherichia-Shigella and Helicobacter. Fecal microbiota transplantation treatment further confirmed that the protective effect of Emo against CLI was mediated by alterations in the gut microbiota, especially Escherichia-Shigella (the highest proportion of abundance).

CONCLUSION

These findings indicated that Emo protected against CLI by modulating BA metabolism and shaping the gut microbiota.

Key Words: Emodin; Cholestatic liver injury; Metabolomics; Bile acid metabolism; Gut microbiota

Core Tip: Our study revealed the beneficial effects of emodin (Emo) in alleviating cholestatic liver injury (CLI). Bile acid metabolism was altered during CLI, which was normalized by Emo. Emo altered gut microbiota composition. Fecal microbiota transplantation confirmed the microbiota-dependent anti-CLI efficacy of Emo and its therapeutic effect on the gut microbiota, especially Escherichia-Shigella. These findings will provide new insights into exploring the anti-CLI mechanism of Emo and its potential treatment strategies for CLI.

INTRODUCTION

Cholestatic liver injury (CLI) refers to a pathological syndrome in which bile production, secretion, and excretion are impaired, leading to hepatic retention of bile acids (BAs) and hepatocellular damage. When left untreated, CLI progresses to liver fibrosis, cirrhosis, and ultimately liver failure[1]. Currently, ursodeoxycholic acid (UDCA) is the first-line treatment for CLI. Obeticholic acid was approved by the United States Food and Drug Administration for treatment of CLI in combination with UDCA in patients with poor response or intolerance to UDCA. However, it is often accompanied by severe itching, elevated levels of serum low-density lipoprotein cholesterol and other adverse reactions[2]. Although drug therapy for CLI has made advances, it still does not meet the demands of clinical practice[3]. Hence, novel therapeutic approaches are urgently needed for CLI.

The gut microbiota plays an important role in human health, and is involved in various physiological processes such as BA metabolism[4]. In recent years, increasing evidence has supported the close relationship between the gut microbiota and CIL progression. Several liver diseases are accompanied by damage to the intestinal barrier and disruption of the gut microbiota[5]. A recent study confirmed that CLI induced by bile duct ligation was associated with the gut microbiota, and Lactobacillus acidophilus promoted the recovery of liver function in CLI patients[6]. Several cross-sectional studies have shown that the intestinal microbiota composition of patients with primary sclerosing cholangitis (PSC) differed significantly from that of healthy individuals, with enrichment of Enterococcus being the most consistent and significant manifestation[7], accompanied by a reduction in Clostridiales[8]. Fecal microbiota transplantation (FMT) is a feasible and safe option to restore the microbiome in patients with PSC[9]. It is suggested that the relief of CLI is mediated in a gut-microbiota-dependent manner. BAs are the main components of bile, and disorders in BAs homeostasis characterize cholestasis[10]. BAs can be metabolized by the gut microbiota, and in turn, the communal structure of intestinal microbes can be influenced by BAs. Thus, the gut microbiome plays a central role in regulating BA metabolism[11]. Owing to the aforementioned reports, reversing the imbalance of gut microbiota may be a potential and effective treatment for CLI.

Emodin (Emo) is a typical anthraquinone compound isolated from several traditional Chinese medicines, including Rheum palmatum, Polygonum multiflorum and Polygonum cuspidatum. Pharmacological investigations have demonstrated that Emo exerts multiple pharmacological effects, such as antibacterial, anti-inflammatory, antioxidant, antitumor, antihepatic fibrosis, and hepatoprotective[12,13]. Although Emo has limited hydrophilicity and low oral bioavailability[14], it still exhibits significant activity against CLI[15,16]. Due to the poor solubility of Emo in the gastrointestinal tract (human intestinal absorption < 30%)[17], this may improve the opportunity to interact with the gut microbiota[18]. Therefore, we propose that the gut microbiota may an important mechanism of the underlying efficacy of Emo. Increasing evidence suggests that Emo can treat diseases by altering the gut microbiota effectively, including cardiovascular disease, ulcerative colitis and severe acute pancreatitis[19-21]. Nevertheless, whether Emo alleviates CLI in a gut-microbiota-dependent manner remains unclear.

In this study, we used α-naphthylisothiocyanate (ANIT) to establish the acute CLI model. The pharmacological results showed that Emo ameliorates ANIT-induced CLI. Untargeted metabolomics and targeted BAs profiling have revealed that Emo modulates BA metabolism. 16S rRNA gene sequencing and FMT have confirmed that Emo influences the composition of the gut microbiota and counteracts CLI in a gut-microbiota-dependent manner. Our study demonstrated the protective effects of Emo in an ANIT-induced CLI model mainly through regulating BA metabolism and gut microbiota composition.

MATERIALS AND METHODS

Chemicals and reagents

High-performance liquid chromatography (HPLC) grade acetonitrile (ACN), methanol, acetic acid, liquid chromatography/mass spectrometry (LC/MS)-grade ammonium acetate (AA), and trace metal-grade ammonia solution were purchased from Fisher Scientific (Fair lawn, NJ, United States). HPLC-grade formic acid was obtained from ACS (Wilmington, DE, United States). Ultra-pure water was produced by a Milli-Q water purification system (Millipore, Bedford, MA, United States). Emo (purity ≥ 95%), BA standards, and isotope internal standard (IIS) cholic acid-[d₄] were purchased from Shanghai Yuanye Biotech. Co., Ltd. (Shanghai, China). BAs were cholic acid (CA), α-muricholic acid (MCA), β-MCA, deoxycholic acid (DCA), UDCA, chenodeoxycholic acid, hyodesoxycholic acid (HDCA), taurocholic acid (TCA), taurodeoxycholic acid, tauro-α-MCA, tauro-β-MCA, tauroursodeoxycholic acid (TUDCA), and taurochenodeoxycholic acid (TCDCA). Carboxyl methyl cellulose sodium was purchased from Shanghai Macklin Biochemical Technology Co. Ltd. (Shanghai, China). ANIT (Lot#K2112555, purity > 98%) was purchased from Shanghai Aladdin Biochemical Technology Co. Ltd. (Shanghai, China). Corn oil (Lot#118762) was purchased from MedChemExpress (Monmouth Junction, NJ, United States). Heparin sodium was purchased from Tianjin Biochemical Pharmaceutical Co., Ltd. (Tianjin, China). 4% paraformaldehyde solution was purchased from Biosharp brand of Beijing Lanjieke Technology Co., Ltd. (Beijing, China). Serum alanine transaminase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), interleukin-6 (IL-6) and tumor necrosis factor α (TNF-α) assay kits were provided by Nanjing Jiancheng Bioengineering Research Institute (Nanjing, China).

Animals

Forty-eight male C57BL/6J mice (weighing 18-22 g) were purchased from Shanghai SLAC Laboratory Animal Co. Ltd. (Shanghai, China). The animal protocol was designed to minimize pain or discomfort to the animals. All mice were adapted to an environment with a temperature of 20 ± 5 °C, relative humidity of 40%-60%, and a 12-hour light-dark cycle for 7 days. Mice were allowed to eat and drink freely and fasted for 12 hours before the study. The study was authorized by the Laboratory Animal Care and Use Committee of the School of Medicine, Tongji University, No. TJBH16025101.

ANIT-induced mice model and Emo treatment

The mice were randomly divided into three groups of 16: Control (Cont) group, ANIT model group, and Emo-ANIT group. Mice in the Emo-ANIT group were administered Emo (100 mg/kg) daily for 7 days, while the Cont and ANIT groups were given 0.5% carboxyl methyl cellulose sodium. After 2 hours of administration on day 5, a cholestatic liver damage model was established by orally administering ANIT (50 mg/kg, dissolved in corn oil), while the Cont group was given an equal amount of corn oil. We allowed the mice free access to feed and water, and their body weight was recorded daily.

Sample collection

At 1 hour after the last drug administration, the mice were anesthetized with isoflurane, and their eyeballs were removed to collect blood samples. Serum was separated from blood samples by centrifugation at 3000 rpm for 15 minutes at 4 °C, and the supernatant were stored at -80 °C for kit testing and BA determination. Plasma was obtained from blood by centrifugation at 4 °C at 14000 rpm for 10 minutes, and kept at -80 °C for untargeted metabolomics profiling. The cecum contents were collected in sterile cryotubes, immediately frozen in liquid nitrogen, and subsequently stored at -80 °C until analysis. Liver tissues were rinsed, dried in cold phosphate-buffered saline (PBS), weighed, and a portion of the liver was instantly fixed in 4% paraformaldehyde for assessment of morphological damage. The remaining liver tissues and distal ileum (flushed gently with PBS) were frozen in liquid nitrogen and stored at -80 °C prior to analysis.

Biochemical assays and histopathological examination

Serum ALT, AST, TBIL, IL-6 and TNF-α were measured. The fixed liver tissues were dehydrated, embedded in paraffin, and sectioned for hematoxylin and eosin (H&E) staining.

Metabolomics analysis based on ¹H nuclear magnetic resonance and ultra-HPLC/quadrupole-Orbitrap-MS

Sample preparation: The plasma and liver tissues were thawed on ice prior to extraction. Detailed sample preparation methods are provided in Supplementary material. Quality control samples were prepared by mixing equal amounts of each sample and used to evaluate the stability and repeatability of LC/MS-based metabolomics.

Data acquisition: Nuclear magnetic resonance (NMR) and MS metabolomics data acquisition processes are shown in Supplementary material.

Data processing: The ¹H NMR spectra were initially processed using Topspin 3.1 software (Bruker Biospin, Karlsruhe, Germany). After Fourier transformation, the spectra were referenced to the TSP (δ 0.00 ppm) and manually corrected for the phase and baseline. Subsequently, the data were processed with MestReNova 9.0.1 software (Mestrelab Research, Santiago de Compostela, Spain), including multiple spectra overlapping, peak alignment, segmented integration, and total area normalization. A data matrix was generated, converted into a .csv file and opened using SIMCA 14.1 software (Umetrics, Sweden) for principal component analysis (PCA) and orthogonal partial least square discriminate analysis (OPLS-DA). Based on the Std. dev. value and the p[1] and p(corr)[1] values under the General List item in the S-plots, the load and r values were calculated. The loading plot corresponding to OPLS-DA was drawn using Matlab software (MathWorks, Torrance, CA, United States). The differential metabolites are shown in different modes (positive or negative), and color projections (red to blue) of the spectrum displayed their significant alterations. Identification of NMR peaks was based on two-dimensional spectra, relevant literature, Chenomx Profiler version 8.3 combined with the NMR chemical shift database of our research group.

The original data were acquired with Xcalibur 4.0 software (Thermo Fisher Scientific, San Jose, CA, United States). Metabolomic data were preprocessed by Compound Discoverer (CD) 3.1 software (Thermo Fisher Scientific, San Jose, CA, United States) for retention time (RT) alignment, matched filtration, peak detection and peak matching. A data matrix containing m/z, RT and peak area intensity of each sample was generated. The resultant table was manually normalized, converted into .csv file format, and imported into SIMCA 14.1 software for PCA and OPLS-DA. In the OPLS-DA model, a metabolite with variable importance in projection value > 1 and P value < 0.05, would be considered as a differential metabolite. The differential metabolic features were identified by comparing the exact molecular mass (≤ 5 ppm), RT, and MS² fragment with reference standards, metabolomics identification software (CD 3.1; MassHunter PCDL Manager, Agilent Technologies, Santa Clara, CA, United States), and public databases (HMDB, https://hmdb.ca/ and PubChem, https://pubchem.ncbi.nlm.nih.gov/). The final differential metabolites were entered into the MetaboAnalyst 6.0 (https://www.metaboanalyst.ca/MetaboAnalyst/) for pathway enrichment analysis.

Serum and tissue BA analysis

Sample preparation: A 100-μL aliquot of serum was mixed with 10 μL IIS (10 μg/mL). Afterwards, 1 mL of ACN was added, and the solution was shaken for 30 minutes, centrifuged for 10 minutes at 14000 rpm at 4 °C, and vacuum-dried. The dried residue was redissolved in 50 μL of 50% methanol aqueous solution before analysis. A volume of 250 μL water was added to 50 mg liver tissue and homogenized. The homogenate was mixed with 10 μL IIS (10 μg/mL), and deproteinized with 2 mL ACN containing 3% ammonia. The mixture was vortex-mixed for 1 hour and centrifuged at 14000 rpm at 4 °C for 10 minutes. The supernatant was dried, and the residue was redissolved in 50 μL of 50% methanol aqueous solution until analysis. The extraction steps for distal ileum were the same as for liver tissue.

Ultra-HPLC/quadrupole-Orbitrap-MS analysis: Chromatographic separation was performed via a Waters UPLC BEH C₁₈ column (2.1 × 100 mm, 1.7 μm) with a mobile phase composed of 5 mmol/L AA in ACN: H₂O (80:20), adjusted to pH 4.5 using acetic acid (mobile phase A), and 5 mmol/L AA in ACN: H₂O (20:80), adjusted to pH 4.5 using acetic acid (mobile phase B). The gradient elution was set at 0-5 minutes, 20% A; 5-10 minutes, 20%-30% A; 10-13 minutes, 30% A; 13-13.5 minutes, 30%-41.5% A; 13.5-16 minutes, 41.5% A; 16-18 minutes, 41.5%-80% A; 18-24 minutes, 80% A; 24-25 minutes, 80%-20% A; 25-28 minutes, 20% A. The column temperature was maintained at 40 °C, and the flow rate was set at 0.35 mL/minute. The injection volume was 5 μL. MS detection was operated in negative ion mode by the selected ion monitoring.

Gut microbiota analysis

Bacterial profiles of cecal contents were determined using high-throughput sequencing of 16S rRNA. Total DNA was extracted using the DNeasy PowerSoil Pro Kit (Qiagen, Venlo, Netherlands Hilden, Germany). Qualified DNA samples were applied for amplification of the 16S rDNA V3-V4 region using primers 338 F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806 R (5’-GGACTACHVGGGTWTCTAAT-3’). The polymerase chain reaction products were purified via the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, United States) and quantified by Quantus™ Fluorometer system (Promega, Madison, WI, United States). The sequencing library was constructed using the NEXTFlex™ Rapid DNA-Seq Kit (Bioo Scientific, Austin, TX, United States). The eligible library was sequenced via the Illumina MiSeq PE300 platform (Illumina, San Diego, CA, United States) by Majorbio Bio-Pharm Technology Co. Ltd. (Shanghai, China). Later, the raw sequencing reads were quality-filtered by fastp (https://github.com/OpenGene/fastp, version 0.20.0) and merged using FLASH (http://www.cbcb.umd.edu/software/flash, version 1.2.7). The processed sequences were clustered into operational taxonomic units using UPARSE with a 97% similarity threshold. Data analysis was performed on the Majorbio Cloud platform (https://cloud.majorbio.com). Alpha diversity analysis of microbial community was firstly calculated. Taxonomic composition analysis was performed. Principal co-ordinates analysis (PCoA) based on Bray-Curtis distance metrics was conducted to assess intergroup diversity differences. Finally, two groups were compared by the Wilcoxon rank-sum test, and differentially enriched bacteria taxa were screened.

Correlation analysis

Spearman correlation analysis was used to assess the correlation between the BAs and gut microbiota associated with intrahepatic cholestasis (https://cloud.oebiotech.com/task/detail/correlation-multiomics-oehw/?version=old).

FMT

Feces from Cont, ANIT or Emo-treated mice were collected, snap-frozen in liquid nitrogen and stored at -80 °C. Fecal samples from donor mice of each group were pooled and 100 mg was resuspended in 1 mL sterile PBS. The solution was vigorously mixed for 10 seconds and centrifuged at 3000 rpm at 4 °C for 10 minutes. The supernatant was collected and used for FMT. Fresh transplant material was prepared within a timeframe of 10 minutes prior to oral gavage to prevent alterations bacterial composition. Mice were pretreated with an antibiotic cocktail (1 mg/mL ampicillin, 1 mg/mL metronidazole, 0.5 mg/mL vancomycin, and 0.5 mg/mL neomycin) for 5 days. Feces from the Cont mice were given to sterile Cont mice, which formed the F-Cont group. Feces from the ANIT mice were given to sterile ANIT mice, which formed the F-ANIT group. Feces from the Emo-treated mice were given to sterile ANIT mice, which formed the F-Emo-ANIT group. Transplantation was performed by oral gavage of transplant material (10 mL/kg) once every 3 days for 21 days. The blood, liver tissues, and fresh fecal samples of mice were collected for further studies.

Statistical analysis

Results were presented as mean ± SD. A two-tailed Student’s t-test was used to analyze inter-group differences in serum biochemical indices and metabolites (including BAs). Wilcoxon rank-sum test was employed to determine differences in microbiota genus. A P value less than 0.05 was considered statistically significant.

RESULTS

Emo ameliorated ANIT-induced cholestasis in mice

We evaluated whether Emo exerted anti-CLI activity in an intrahepatic cholestasis model. ANIT significantly increased the liver index, and levels of ALT, AST, TBIL, IL-6 and TNF-α in serum (Figure 1A). Compared with model group, treatment with 100 mg/kg Emo significantly decreased liver index and serum biochemical indices. Morphological observation of liver tissue showed that liver injury was generated and the color of bile was darker compared to those in the Cont group. In the Emo intervention group, the degree of liver tissue damage was less than in the model group, and the bile color returned to near normal (Figure 1B). H&E staining indicated that ANIT-induced mice exhibited infiltration of inflammatory cells, and hepatocyte necrosis and fibrosis. In contrast, Emo treatment decreased inflammatory cell infiltration and histopathological changes in the liver (Figure 1C). These results confirmed that Emo ameliorated CLI.

Figure 1 Emodin ameliorated α-naphthylisothiocyanate-induced cholestasis in mice. A: Liver index and serum biochemical analysis; B: Morphology observation of mice liver and gallbladder; C: Histopathological examination by hematoxylin and eosin staining in mice liver sections, magnification 200 ×, n = 3 (scale bar: 200 μm). Data were analyzed via two-tailed Student’s t-test. n = 6. ^aP < 0.05 vs control group, ^bP < 0.01 vs control group, ^cP < 0.001 vs control group, ^dP < 0.05 vs α-naphthylisothiocyanate group, ^eP < 0.01 vs α-naphthylisothiocyanate group, and ^fP < 0.001 vs α-naphthylisothiocyanate group. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; ALT: Alanine transaminase; AST: Aspartate aminotransferase; TBIL: Total bilirubin; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor α.

Effect of Emo on metabolites in mice with ANIT-induced cholestasis

Metabolomic analysis based on ¹H NMR: An untargeted metabolomics with NMR-based approach was used to screen differential metabolites of plasma and liver among the Cont, ANIT and Emo treatment groups. The PCA plot of plasma showed a clear separation with no overlap among the three groups, while there was only a small separation in the PCA of liver (Figure 2A). To further explore the difference in metabolites between these two groups, supervised OPLS-DA was applied (Figure 2B and C left). The results showed distinct separation between the plasma and liver sample groups, suggesting high metabolite differentiation and confirming the PCA results. A permutation test was conducted for verification to avoid the transition fit of the OPLS-DA model (Supplementary Figure 1). The permutation test indicated that the OPLS-DA model had no overfitting. Hence, the model was suitable for use in subsequent analyses. The differential metabolites between each two groups (ANIT vs Cont and Emo-ANIT vs ANIT) were clearly displayed in the related OPLS-DA loading plots (Figure 2B and C right). Red color corresponds to high correlation (r > 0.6), while blue indicates no correlation (r < 0.2). Compared with the Cont group, 18 metabolites showed increased levels in the plasma of the ANIT group, such as lactate, taurine and acetoacetate. Decreased levels of lactate and increased levels of glucose were observed in the Emo treatment group compared to ANIT group. For liver tissue, compared with the Cont group, the ANIT group had seven significantly differential metabolites with higher concentrations, and one with lower concentration. After treatment with Emo, several metabolites such as BAs, taurine, choline, proline and lysine, were observed in decreased levels. The ¹H-NMR data for differential metabolites between the two groups are shown in Supplementary Tables 1-4. The potential biomarkers associated with Emo against CLI are listed in Table 1.

Figure 2 Analyses of differential metabolites in plasma and liver by nuclear magnetic resonance-based metabolomics. A: Principal component analysis score plots; B: Orthogonal partial least square discriminate analysis score plot and loading plot of plasma; C: Orthogonal partial least square discriminate analysis score plot and loading plot of liver. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; PCA: Principal component analysis; OPLS-DA: Orthogonal partial least square discriminate analysis.

Table 1 Potential biomarkers associated with emodin against intrahepatic cholestasis.

Biological matrices	Metabolites	ANIT vs Cont	Emo-ANIT vs ANIT	Detected
Plasma	Lactate1	Increased	Decreased	NMR, MS
	CA1	Increased	Decreased	MS
	β-MCA1	Increased	Decreased	MS
	T-α + β-MCA1	Increased	Decreased	MS
	TCA1	Increased	Decreased	MS
	TCDCA1	Increased	Decreased	MS
	LysoPC (18:1)2	Increased	Decreased	MS
	LysoPC (20:4)2	Increased	Decreased	MS
	LysoPC (22:6)2	Increased	Decreased	MS
	Phytosphingosine2	Increased	Decreased	MS
	L-1,2,3,4-tetrahydro-beta-carboline-3-carboxylic acid2	Increased	Decreased	MS
	Glutamate1	Increased	Decreased	MS
	Pyruvic acid1	Decreased	Increased	MS
	2-C-methyl-D-erythritol 4-phosphate2	Decreased	Increased	MS
	Phenol sulphate2	Decreased	Increased	MS
	Methacholine1	Decreased	Increased	MS
	Glucose1	Decreased	Increased	MS
	Hexanoylglycine2	Decreased	Increased	MS
	Indoxyl sulfate2	Decreased	Increased	MS
	Betaine1	Decreased	Increased	MS
Liver	BAs (mixed)	Increased	Decreased	NMR
	Lysine	Increased	Decreased	NMR
	Proline	Increased	Decreased	NMR
	β-MCA1	Increased	Decreased	MS
	T-α + β-MCA1	Increased	Decreased	MS
	TCA1	Increased	Decreased	MS
	TCDCA1	Increased	Decreased	MS
	Choline phosphate2	Increased	Decreased	MS
	N-Acetyl-L-glutamic acid1	Increased	Decreased	MS
	Ornithine1	Decreased	Increased	MS
	Arginine1	Decreased	Increased	MS
	Deoxyadenosine monophosphate1	Decreased	Increased	MS
	5-thymidylic acid1	Decreased	Increased	MS

¹Compounds are compared with standards.

²Metabolites are compared with online database.

NMR: Nuclear magnetic resonance; MS: Mass spectrometry; Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; CA: Cholic acid; MCA: Muricholic acid; TCA: Taurocholic acid; TCDCA: Taurochenodeoxycholic acid; BAs: Bile acids; T-α + β-MCA: Tauro-α + β-muricholic acid; LysoPC: Lysophosphatidylcholine.

Metabolomic analysis based on ultra-HPLC/quadrupole-Orbitrap MS: Ultra-HPLC/quadrupole-Orbitrap MS analysis in positive and negative ion modes was conducted on plasma and liver tissue. Raw data were pretreated using CD software, and a peak list was generated for multivariate statistical analysis. In the unsupervised PCA (Figure 3A), the clustering of the quality control samples showed that the LC/MS was stable during analysis. Different datasets were placed at a certain distance from each other. To maximize the discrimination and identify differentiated metabolites, the supervised OPLS-DA model was used and a test with 200 permutations was performed. As shown in Figure 3B and C, the OPLS-DA results showed an obvious separation between the two groups. No overfitting was observed based on the permutation results. To identify potential metabolic biomarkers, variable importance in projection value > 1 and P < 0.05 were chosen as the criteria. Thirty-six and 48 differential compounds were identified in the plasma and liver between the Cont and ANIT groups, respectively. Twenty-nine and 17 significantly differential metabolites were found in the plasma and liver between the ANIT and Emo-ANIT groups, respectively (Supplementary Tables 5-8). These differential metabolites are displayed in the S-plots (Figure 3B and C right most panels). Finally, 29 metabolites were screened out as the anti-CLI potential biomarkers, including lactate, CA and β-MCA (Table 1).

Figure 3 Analyses of differential metabolites in plasma and liver by ultra-high-performance liquid chromatography/quadrupole-Orbitrap-mass spectrometry-based metabolomics. A: Principal component analysis score plots; B: Orthogonal partial least square discriminate analysis, permutation test and S-plot of plasma; C: Orthogonal partial least square discriminate analysis, permutation test and S-plot of liver. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; PCA: Principal component analysis; OPLS-DA: Orthogonal partial least square discriminate analysis; QC: Quality control.

Metabolic pathway analysis: Based on our selected differential metabolites, the related metabolic pathway analysis was performed on MetaboAnalyst 6.0. The main influenced metabolic pathways were “arginine biosynthesis”, “arginine and proline metabolism”, “aminoacyl-tRNA biosynthesis”, “glycolysis/gluconeogenesis”, “primary bile acid biosynthesis”, “pyruvate metabolism”, “glycine, serine and threonine metabolism”, and “glycerophospholipid metabolism” (Figure 4). These pathways were likely to be involved in the mechanism of Emo intervention in CLI. According to these differential metabolites and pathways in plasma and liver, a metabolic network was constructed (Figure 5).

Figure 4 Bubble plot of metabolic pathways associated with the differential metabolites. A: Arginine biosynthesis; B: Arginine and proline metabolism; C: Aminoacyl-tRNA biosynthesis; D: Glycolysis/gluconeogenesis; E: Primary bile acid biosynthesis; F: Pyruvate metabolism; G: Glycine, serine and threonine metabolism; H: Glycerophospholipid metabolism.

Figure 5 The most predominant disturbed metabolic pathways and the biochemical linkages among the biomarker metabolites. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; GI: Gluconeogenesis; LPC: Lysophosphatidylcholine.

Effect of Emo on BAs in mice with ANIT-induced cholestasis

Effect on BAs levels in the liver: The above metabolomics results found that the protective role of Emo in mouse liver injury was related to BA biosynthesis; among which, taurine, CA, β-MCA, TCA, tauro-α-MCA, tauro-β-MCA and TCDCA participated in BA metabolism. This indicated the importance of BA level analysis. Therefore, we focused on the changes in BAs in ANIT-induced CLI model mice after Emo administration. The extracted ion chromatograms are shown in Supplementary Figure 2. We observed significant differences in the levels of liver total BAs (Σ BAs), primary BAs (1° BAs), secondary BAs (2° BAs), taurine-conjugated BAs (T-BAs), and unconjugated BAs (U-BAs) between the Cont and ANIT groups. Compared with the Cont group, the levels of Σ BAs, 1° BAs, T-BAs and U-BAs were significantly increased after ANIT administration, manifested mainly by significant increases in CA, β-MCA, UDCA, hyocholic acid (HCA), HDCA, murideoxycholic acid, tauro-β-muricholic acid (TMCA), TCA, TCDCA, taurohyocholic acid (THCA), TisoDCA, and TUDCA (Figure 6). The level of 2° BAs was markedly decreased, manifested mainly by a decrease in DCA and isoDCA (Figure 6A and C). Emo administration could reverse the increase of these BAs induced by ANIT, mainly manifested in a significant decrease in the levels of CA, β-MCA, UDCA, HCA, HDCA, murideoxycholic acid, TMCA, TCA, TCDCA, THCA and TUDCA. Quantification of BAs in the liver indicated that Emo alleviated CLI by inhibiting the generation of 1° BAs (mainly T-BAs) in the liver.

Figure 6 Effect of emodin on liver bile acids in mice with α-naphthylisothiocyanate-induced cholestatic liver injury. A: Changes of total bile acids (BAs), primary BAs, secondary BAs, taurine-conjugated BAs and unconjugated BAs (U-BAs); B: Changes of U-BAs with high content; C: Changes of U-BAs with low content; D: Changes of taurine-conjugated BAs. Data were analyzed via two-tailed Student’s t-test, n = 8. ^aP < 0.05 vs control group, ^bP < 0.01 vs control group, ^cP < 0.001 vs control group, ^dP < 0.05 vs α-naphthylisothiocyanate group, and ^eP < 0.01 vs α-naphthylisothiocyanate group. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; Σ BAs: Total bile acids; 1° BAs: Primary bile acids; 2° BAs: Secondary bile acids; T-BAs: Taurine-conjugated bile acids; U-BAs: Unconjugated bile acids; CA: Cholic acid; MCA: Muricholic acid; DCA: Deoxycholic acid; CDCA: Chenodeoxycholic acid; UDCA: Ursodeoxycholic acid; HCA: Hyocholic acid; MDCA: Murideoxycholic acid; HDCA: Hyodesoxycholic acid; TDCA: Taurodeoxycholic acid; TMCA: Tauro-β-muricholic acid; TCA: Taurocholic acid; TCDCA: Taurochenodeoxycholic acid; THCA: Taurohyocholic acid; THDCA: Taurochenodeoxycholic acid; TUDCA: Tauroursodeoxycholic acid.

Effect on BA levels in the distal ileum: The BAs synthesized by the liver are secreted into the bile and released into the small intestine. Up to 95% of BAs are reabsorbed in the ileum, and the absorption process is higher in its distal region[22]. Therefore, we measured the concentration of BAs in the distal ileum through a targeted metabolomics method. The levels of Σ BAs, 1° BAs, 2° BAs, T-BAs and U-BAs were significantly decreased by ANIT (Figure 7A). Emo ameliorated the changes induced by ANIT. In ANIT mice, the results showed that CA, α-MCA, β-MCA, DCA, chenodeoxycholic acid, HCA, UDCA, TMCA, TCA, THCA, taurochenodeoxycholic acid, TisoDCA, tauromurideoxycholic acid (TMDCA) and TUDCA were significantly decreased. Emo partially reversed the BA profile of ANIT mice, including CA, β-MCA, HCA, UDCA, TMCA, TCA, THCA, TisoDCA, TMDCA and TUDCA (Figure 7B and C). Subsequently, this experiment continued to analyze the ratio of 2° BAs to 1° BAs and the ratio of U-BAs to T-BAs in the ileum. Compared with the Cont group, the ANIT group showed a significant decrease in the ratios of 2° BAs/1° BAs and U-BAs/T-BAs. After treatment with Emo, the proportion of U-BAs markedly increased, and the proportion of 2° BAs also increased, but there was no significant difference (Figure 7D and E).

Figure 7 Effect of emodin on distal ileum bile acids in mice with α-naphthylisothiocyanate-induced cholestasis liver injury. A: Changes of total bile acids (BAs), primary BAs, secondary BAs, taurine-conjugated BAs (T-BAs) and unconjugated BAs (U-BAs); B: Changes of U-BAs; C: Changes of T-BAs; D: Ratio of primary BAs to secondary BAs in the ileum; E: Ratio of T-BAs to U-BAs in the ileum. Data were analyzed via two-tailed Student’s t-test, n = 8. ^aP < 0.05 vs control group, ^bP < 0.01 vs control group, ^cP < 0.05 vs α-naphthylisothiocyanate group, and ^dP < 0.01 vs α-naphthylisothiocyanate group. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; Σ BAs: Total bile acids; 1° BAs: Primary bile acids; 2° BAs: Secondary bile acids; T-BAs: Taurine-conjugated bile acids; U-BAs: Unconjugated bile acids; CA: Cholic acid; MCA: Muricholic acid; DCA: Deoxycholic acid; CDCA: Chenodeoxycholic acid; HCA: Hyocholic acid; HDCA: Hyodesoxycholic acid; UDCA: Ursodeoxycholic acid; MDCA: Murideoxycholic acid; TMCA: Tauro-β-muricholic acid; TCA: Taurocholic acid; TCDCA: Taurochenodeoxycholic acid; THCA: Taurohyocholic acid; TMDCA: Tauromurideoxycholic acid; TUDCA: Tauroursodeoxycholic acid; TDCA: Taurodeoxycholic acid; THDCA: Taurochenodeoxycholic acid.

Effect on BAs levels in the serum: When intrahepatic bile stasis occurs, bile secretion is impaired, and BAs cannot be excreted effectively, which increases the level of BAs in the blood. In the clinic, serum BA levels are used as a sensitive and reliable index of hepatobiliary diseases[23]. Therefore, we performed quantification of BAs in the serum. The levels of Σ BAs, 1° BAs, T-BAs and U-BAs were significantly increased in the serum of ANIT-induced cholestasis mice (Supplementary Figure 3A). Specifically, as shown in Supplementary Figure 3B and C, the levels of CA, β-MCA, HCA, and other T-BAs except taurodeoxycholic acid were markedly increased in the model group. ANIT intervention resulted in a significantly decreased level of DCA (2° BA), and decreased proportion of 2° BAs (Supplementary Figure 3B and D), which may be closely related to the gut microbiota. Treatment with Emo obviously reversed the serum levels of CA, β-MCA, HCA, TMCA, TCA, THCA, TisoDCA, TMDCA, TUDCA and TCDCA. In addition, there were obvious improvements in the altered DCA level and 2° BAs/1° BAs ratio.

Effect of Emo on intestinal flora in mice with ANIT-induced cholestasis

Alpha diversity analysis indicated that Shannon index did not exhibit any significant difference among three groups. Moreover, the Chao index of ANIT group was significantly reduced compared with the Cont group, and significantly increased after Emo treatment, suggesting that Emo at least partially restored species richness perturbed by ANIT (Figure 8A). Species composition analysis was conducted (Figure 8B and C). The sample community structure analysis showed that there were six dominant bacterial phyla, Firmicutes, Bacteroidota, Actinobacteriota, Desulfobacterota, Proteobacteria and Campilobacterota. At the genus level, the 11 most dominant bacterial genus were Staphylococcus, norank_f_Muribaculaceae, Faecalibaculum, Desulfovibrio, Bifidobacterium, Lachnoclostridium, Enterococcus, Escherichia-Shigella, Lactobacillus and Erysipelatoclostridium. PCoA revealed the differences among the three groups, indicating that the flora composition was different (Figure 8D). To further screen for potential key gut microbiota, we used intergroup difference testing. There was a significant increase in the abundance of Enterococcus, Escherichia-Shigella and Helicobacter, and a marked decrease in the abundance of Lachnoclostridium in the ANIT group compared to the Cont group (Figure 8E-H). Emo reversed these compositional changes. These results suggest that Emo modified the gut microbiota composition in intrahepatic cholestatic mice, increasing the abundance of beneficial bacteria and reducing the abundance of harmful bacteria.

Figure 8 Effect of emodin on gut microbiota in mice with α-naphthylisothiocyanate-induced cholestasis liver injury. A: Alpha diversity (Chao and Shannon indices) of bacteria in control, α-naphthylisothiocyanate-treated and emodin-treated mice; B: Community distribution at the phylum level; C: Community distribution at the genus level; D: Principal co-ordinates analysis of different groups at the genus level; E-H: Proportion of differential microbiota. Data were analyzed via the Wilcoxon rank-sum test. ^aP < 0.01 vs control group, ^bP < 0.001 vs control group, ^cP < 0.05 vs α-naphthylisothiocyanate group, and ^dP < 0.01 vs α-naphthylisothiocyanate group. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; PCoA: Principal co-ordinates analysis.

Correlation analysis of different metabolites and flora

Spearman correlation analysis between intestinal microbiota and BAs was performed. Red represents a positive correlation between the two features (Figure 9). The redder the color, the higher the positive correlation coefficient. Blue represents a negative correlation. The bluer the colour, the higher the negative correlation coefficient. In Cont and ANIT groups, Enterococcus, Escherichia-Shigella and Helicobacter were in direct proportion to some BAs in plasma and liver, and significantly negatively correlated with some BAs in the ileum. Lachnoclostridium was positively correlated with various BAs. These included β-MCA, CA, HCA, TCA, THCA, TisoDCA, TMCA, TMDCA and UDCA in the ileum, as well as DCA in plasma. Lachnoclostridium and BAs (HCA and TCDCA in plasma) also demonstrated a significant negative correlation (Figure 9A). In the ANIT and Emo-ANIT groups, Enterococcus, Escherichia-Shigella and Helicobacter exhibited significant positive correlations with some BAs in plasma and liver, except for DCA in plasma. There was a significant negative correlation between the three intestinal bacteria mentioned above and some BAs in the ileum. In contrast, Lachnoclostridium and some BAs (β-MCA, CA, HCA, THCA and UDCA) in the ileum showed a strong positive correlation. However, it exhibited a negative correlation with β-MCA, CA and HCA in plasma (Figure 9B).

Figure 9 Spearman’s correlation analyses between the relative abundance of gut microbiota at the genus level and bile acids. A: The correlations between gut microbiota and bile acids between the control and α-naphthylisothiocyanate (ANIT) group; B: The correlations between gut microbiota and bile acids between the ANIT and emodin-ANIT group. Data were analyzed via two-tailed test of significance. ^aP < 0.05 vs control group, ^bP < 0.01 vs control group, ^cP < 0.001 vs control group, ^dP < 0.05 vs α-naphthylisothiocyanate group, ^eP < 0.01 vs α-naphthylisothiocyanate group, and ^fP < 0.001 vs α-naphthylisothiocyanate group.

Emo alleviated CLI in a microbiota-dependent manner

We used FMT to determine whether gut microbiota altered by Emo had therapeutic benefits for CLI. Fecal microbiota from ANIT- or Emo-treated mice were transplanted into ANIT recipients (Supplementary Figures 4 and 5). The grouping and treatment experimental design for FMT are described in Figure 10A. Morphological observation showed that the gallbladder size of cholestatic mice was markedly increased. After FMT from Emo-fed donor mice, the gallbladder returned to normal size (Figure 10B). H&E analysis of liver sections revealed that F-Emo-ANIT ameliorated inflammatory infiltration and cell swelling in cholestatic mice (Figure 10C). F-Emo-ANIT resulted in significantly lower levels of ALT, AST and TBIL than did F-ANIT (Figure 10D).

Figure 10  Microbial communities from emodin-treated mice could ameliorate α-naphthylisothiocyanate-induced cholestatic liver injury. A: Experimental design scheme; B: Observation of liver and gallbladder tissue morphology; C: Representative images of hematoxylin and eosin staining liver sections, magnification 200 ×, n = 3 (scale bar: 50 μm); D: Serum alanine transaminase, aspartate aminotransferase and total bilirubin levels. Data were analyzed via two-tailed Student’s t-test, n = 6. ^aP < 0.01 vs control group, ^bP < 0.001 vs control group, ^cP < 0.05 vs α-naphthylisothiocyanate group, and ^dP < 0.01 vs α-naphthylisothiocyanate group. ALT: Alanine transaminase; AST: Aspartate aminotransferase; TBIL: Total bilirubin; Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; ABX: Antibiotics.

To confirm that FMT modulated the gut microbiota, 16S rRNA sequencing was conducted on collected fecal samples obtained from recipient mice. Bacterial taxonomic profiling indicated that at the phylum level, Firmicutes, Bacteroidota and Proteobacteria were dominant (Figure 11A). At the genus level, an increase in the abundance of Escherichia-Shigella, and a decrease in the abundance of probiotics Blautia and f_Lachnospiraceae_Unclassified were observed in the F-ANIT group as compared to the F-Cont group, and FMT from Emo-fed donor mice reversed these compositional changes (Figure 11B). According to PCoA, FMT also significantly altered bacterial beta diversity (Figure 11C). STAMP differential analysis and box plots showed that the relative abundance of Escherichia-Shigella, Klebsiella, Erysipelatoclostridium and Enterococcus was significantly enhanced in the F-ANIT group (P < 0.05), and the above bacteria were significantly downregulated after Emo + ANIT → ANIT treatment (Figure 11D-H). Notably, the trend of changes in the microbiota of Escherichia-Shigella and Enterococcus in different groups was similar to that of Emo-treated mice. These data support the crucial role of the gut microbiota in the alleviation of CLI by Emo, and suggest that Emo ameliorated the cholestasis phenotype in ANIT mice by regulating the microbiota.

Figure 11  Fecal microbiota transplantation of emodin-fed mice modulated gut microbiota. A: Bacterial community composition at the phylum level; B: Bacterial community composition at the genus level; C: Principal co-ordinates analysis of the gut microbiota in each group at the genus level; D: STAMP analysis uncovered the differences between F-control and F-α-naphthylisothiocyanate (ANIT) groups, F-ANIT and F-emodin-ANIT groups (the P value < 0.05 for declaring significance); E-H: Relative abundance of differential microbiota. Data were analyzed via two-sided Welch’s t-test, n = 5. ^aP < 0.05 vs control group, ^bP < 0.05 vs α-naphthylisothiocyanate group. Cont: Control; Emo: Emodin; ANIT: Α-naphthylisothiocyanate; PCoA: Principal co-ordinates analysis.

DISCUSSION

ANIT, as a classical hepatotoxicant, is widely used to prepare cholestatic models in rodents, simulating intrahepatic cholestasis in humans[24]. The mechanism of ANIT hepatotoxicity may involve the binding of ANIT to glutathione (GSH) in hepatic parenchyma, followed by secretion of the ANIT-GSH conjugate into bile and dissociation into GSH and free ANIT. Free ANIT injures biliary cells, leading to reduced bile flow, hepatic accumulation of BAs, and hepatocyte damage, which in turn causes intrahepatic cholestasis in rodents[25]. Thus, the biliary phenotype in ANIT-treated rodents resembles more the pathological findings observed in human primary biliary cholangitis[26]. Based on the recognized applicability of the ANIT model, our study provides novel insights into exploring the anti-CLI mechanism of Emo and its potential treatment strategies for CLI. Nevertheless, given the differences between humans and mice, no single mouse model can strictly reproduce all the features of human cholestatic liver diseases[26]. Thus, our data mainly provide mechanistic and preclinical efficacy data rather than direct clinical proof. Clinically, the most commonly used biomarkers for the diagnosis of liver injury are ALT, AST and TBIL[27]. We showed that, after administration of Emo, the serum concentration of these biochemical indicators tended to decrease compared with in the model group. Given that CLI is characterized by sterile inflammation, measurement of inflammatory factors will provide a more comprehensive assessment of the anti-inflammatory effects of Emo. The increases of pro-inflammatory cytokines levels (IL-6 and TNF-α) in serum stimulated by ANIT were all lowered by Emo. Pathological analysis also suggested that Emo attenuated the degree of liver injury. These results confirmed that Emo could reduce serum cholestasis markers, improve liver function and inhibit liver injury caused by cholestasis.

Metabolomics, as an emerging systems-biology technology, aims to study the dynamic changes of endogenous small-molecule metabolites in biological samples before and after stimulation or disturbance. It can be used to search for potential biomarkers and metabolic pathways[28]. Currently, many analytical techniques are commonly used in metabolomics research, including NMR, LC/MS, and gas chromatography/MS. NMR and LC/MS are the most widely used technical platforms due to their simple sample preparation and high sensitivity[29]. In our study, plasma and liver tissue samples for sequential NMR and LC/MS analysis were prepared using a previously established method[30]. The metabolic changes in the plasma and liver resulting from Emo treatment in a CLI mouse model were analyzed. We found that the significantly differential endogenous metabolites between the Emo-ANIT and ANIT groups were associated mainly with organic acids (including amino acids), BAs and lipids. These metabolites were involved in metabolic pathways such as BA metabolism. Consistent with our results, other studies have suggested that CLI is accompanied by dysfunction in BA homeostasis[31]. Therefore, we conducted specific BA metabolomics profiles to illustrate the therapeutic effects of Emo on ANIT-induced CLI. We showed that BA concentrations in the liver, distal ileum and serum were significantly altered by liver injury and Emo treatment. The levels of 2° BAs (DCA and isoDCA) in liver were significantly decreased after ANIT administration. Research has shown that gut microbiota disorders have been found in cholestatic liver diseases[32]. When the gut microbiota is disrupted, bile salt hydrolase (BSH) activity tends to decrease. This is because BSH is produced by specific microbial species (e.g., Bacteroides, Bifidobacterium and Lactobacillus), which is sensitive to changes in microbial balance[33,34]. The critical first step in the metabolism of BAs by the gut microbiome to generate 2° BAs is catalyzed by BSH. When BSH activity is reduced, the level of 2° BAs in the intestine may decrease, accompanied by disruption of BA reabsorption, leading to a decrease in 2° BAs in the liver[35,36]. Emo increased the levels of BAs in the distal ileum that were significantly decreased in the ANIT group. ANIT-induced CLI shows impairment of bile flow, which leads to accumulation of BAs in hepatocytes, thereby reducing the excretion of BAs from the liver into the small intestine[26]. Our results suggested that Emo promoted excretion of BAs from the liver to small intestine, which is consistent with the above explanation. ANIT decreased the proportion of ileum 2° BAs and U-BAs. Under Emo treatment, this phenomenon was reversed, suggesting that Emo enhances BSH activity, thereby promoting biotransformation of 1° BAs. The concentration of most BAs in serum was significantly increased after ANIT administration, while DCA and the ratio of 2° BAs to 1° BAs were markedly reduced. Notably, Emo intervention reversed these changes. This finding is consistent with the viewpoint described above.

Emo has low bioavailability and may have greater opportunity to interact with gut microbiota; therefore, we speculated that Emo may modulate the composition of the gut microbiota, thus exerting hepatoprotective effects. Our results showed that Emo decreased Enterococcus, Escherichia-Shigella and Helicobacter, and increased Lachnoclostridium, at the genus level. Enterococcus is enriched in the intestines of patients with PSC[37]. Studies have suggested that Escherichia-Shigella is also abundant in patients with intrahepatic cholestasis of pregnancy[38]. Helicobacter is a harmful bacterium that is abundant in the microbiota of mice with severe CLI[6]. Lachnoclostridium is a beneficial genus that has anti-inflammatory potential[39]. It is also reduced mice with CLI induced by bile duct ligation[40]. Our results indicated that Emo alleviated the gut microbiota dysbiosis, upregulated the abundance of beneficial bacteria, and downregulated the abundance of harmful bacteria. However, the role of Emo-enriched Lachnoclostridium in improving CLI requires further study.

In recent years, increasing evidence has shown that there are reciprocal relationships between the host-associated microbiota and metabolites in pathology and physiology[41]. To investigate the extent to which the gut microbiome was associated with BAs in the host, Spearman’s correlation analysis was performed to determine the covariation between the differential intestinal flora and altered BAs. The changes in the gut microbiome were significantly correlated with BAs levels in serum, liver and distal ileum. This suggests that the regulatory effect of Emo on gut microbial communities alters the metabolism of BAs in cholestatic mice. We speculate that the interaction between these two factors may be an important target for driving or preventing CLI. However, whether these altered bacteria can affect BA metabolism as a target for preventing CLI needs further exploration.

To verify the therapeutic effect of Emo based on gut microbiota, an FMT experiment was conducted. FMT from Emo-fed mice ameliorated ANIT-induced CLI, and modulated gut microbiota composition. We observed downregulation of harmful bacteria Escherichia-Shigella (the highest proportion of abundance) and Enterococcus levels after FMT. The FMT experiment revealed that the beneficial regulatory effects of Emo on the CLI mice were, at least partially, attributed to gut microbes. Hence, Emo treatment offers a new and promising strategy for the treatment of CLI.

Previous studies have shown that Emo rescues CLI mainly by modulating farnesoid X receptor/bile salt export pump and sirtuin 1/farnesoid X receptor signaling pathways, thus regulating BA metabolism, reducing BA load in hepatocytes, and promoting the canalicular export of accumulated bile[16,42]. Other work has highlighted the central role of the gut microbiota-BA axis in cholestatic liver disease and liver fibrosis progression[43], and has demonstrated that modulating gut microbiota or BAs can ameliorate CLI[6,10]. In this context, our study systematically integrated BA profiling in serum, liver and distal ileum with gut microbiota analysis in ANITinduced CLI treated with Emo, providing a more comprehensive view of how Emo affected the gut-liver axis than previous studies focused on hepatic signaling pathways. By combining microbiome analysis with FMT, we provided functional evidence that Emo-altered gut microbiota mediated, at least in part, its hepatoprotective effect. Our correlation analysis between specific bacterial genera and BA profiles across multiple compartments supported the emerging concept that targeting the gut microbiota-BA axis may represent a promising therapeutic strategy in cholestatic liver disease.

Our study had some limitations. Although we identified the bacterial genera affected by Emo, we did not conduct indepth mechanistic studies to determine whether the key genera regulate BA metabolism and can serve as therapeutic targets for CLI. Therefore, one of our future research directions will be to perform functional validation on candidate microbiota by testing whether the modulation of selected bacteria (probiotics) can reproduce the protective effects of Emo in CLI, and clarifying the potential underlying mechanism. Our study only provided a preliminary exploration of the protective mechanism of Emo in CLI. Research on the target mechanisms of Emo was insufficient. More comprehensive multiomics approaches[44], including transcriptomics and proteomics, integrated with probe-based techniques[45,46] such as conventional activity-based probes and novel PROTAC-based chemical probes, can be used to systematically identify the upstream regulators and direct molecular targets of natural products. Based on this strategy, we have already conducted proteomics research to screen differentially expressed proteins and predict specific target proteins modulated by Emo, which will be validated by western boltting to deepen our understanding of the molecular mechanisms of Emo in CLI. Given that BAs are key components of bile and their levels directly depend on BA synthesis, metabolism, efflux and uptake, disturbances at any of these steps can lead to abnormal BA metabolism. Therefore, we will focus our research on the proteins involved in the BA metabolism pathway. The proteomics data is still being analyzed and will be presented in a subsequent, more mechanism-oriented manuscript.

CONCLUSION

Emo could alleviate CLI through modulation of BA metabolism and gut microbiota. These findings provide new insights into exploring the anti-CLI mechanism of Emo and potential treatment strategies.