Skip to main content
NCBI home page
Journal of Zhejiang University. Science. B logoLink to Journal of Zhejiang University. Science. B
. 2023 Aug 15;24(8):734–748. doi: 10.1631/jzus.B2200439

Lactobacillus gasseri LA39 promotes hepatic primary bile acid biosynthesis and intestinal secondary bile acid biotransformation

格氏乳酸杆菌LA39促进了肝脏初级胆汁酸的生物合成和肠道次级胆汁酸的生物转化

Jun HU 1,2,3, Qiliang HOU 1,2,3, Wenyong ZHENG 1,2,3, Tao YANG 1,2,3, Xianghua YAN 1,2,3,✉
PMCID: PMC10423968  PMID: 37551559

Abstract

A growing body of evidence has linked the gut microbiota to liver metabolism. The manipulation of intestinal microflora has been considered as a promising avenue to promote liver health. However, the effects of Lactobacillus gasseri LA39, a potential probiotic, on liver metabolism remain unclear. Accumulating studies have investigated the proteomic profile for mining the host biological events affected by microbes, and used the germ-free (GF) mouse model to evaluate host-microbe interaction. Here, we explored the effects of L. gasseri LA39 gavage on the protein expression profiles of the liver of GF mice. Our results showed that a total of 128 proteins were upregulated, whereas a total of 123 proteins were downregulated by treatment with L. gasseri LA39. Further bioinformatics analyses suggested that the primary bile acid (BA) biosynthesis pathway in the liver was activated by L. gasseri LA39. Three differentially expressed proteins (cytochrome P450 family 27 subfamily A member 1 (CYP27A1), cytochrome P450 family 7 subfamily B member 1 (CYP7B1), and cytochrome P450 family 8 subfamily B member 1 (CYP8B1)) involved in the primary BA biosynthesis pathway were further validated by western blot assay. In addition, targeted metabolomic analyses demonstrated that serum and fecal β‍-muricholic acid (a primary BA), dehydrolithocholic acid (a secondary BA), and glycolithocholic acid-3-sulfate (a secondary BA) were significantly increased by L. gasseri LA39. Thus, our data revealed that L. gasseri LA39 activates the hepatic primary BA biosynthesis and promotes the intestinal secondary BA biotransformation. Based on these findings, we suggest that L. gasseri LA39 confers an important function in the gut‒liver axis through regulating BA metabolism.

Keywords: Lactobacillus gasseri LA39, Liver, Isobaric tags for relative and absolute quantitation (iTRAQ), Bile acid, Germ-free mice

1. Introduction

An increasing number of studies have established a relationship between gut microbiota and host physiology (Tremaroli and Bäckhed, 2012; Sommer and Bäckhed, 2013; Marchesi et al., 2016). The manipulation of gut microbiota has become a promising avenue to mediate host health (Lemon et al., 2012; Bajaj et al., 2022). As a vital organ, important metabolic functions are conferred on the liver, including nutrition, synthesis, and detoxification (Thomson and Knolle, 2010). Recent studies have revealed the important functions of the gut‍–‍liver axis in mammals (Silveira et al., 2022). Gut pathogen overload may play a role in non-alcoholic fatty liver disease (NAFLD) and liver inflammation (Scott, 2017; Ringseis et al., 2020). However, the liver also has a regulatory role in gut microbiota through the production of metabolites, such as bile acid (BA) (Brandl et al., 2017). The use of probiotics has been considered as a potential strategy to prevent various hepatic diseases (Maslennikov et al., 2021). Thus, mining the gut microbes that regulate host liver metabolism and deciphering the underlying mechanism can provide important insights into the gut–liver axis.

Several studies have suggested that members of the Lactobacillus genus exert potential beneficial functions on host health (Bernardeau et al., 2006; Lebeer et al., 2008; Kleerebezem and Vaughan, 2009; Selle and Klaenhammer, 2013; van Baarlen et al., 2013; Liu et al., 2021). This genus belongs to the typical probiotics that play important roles in nutrition metabolism (Ramakrishna, 2013), host immune responses (Ubeda and Pamer, 2012; Rooks and Garrett, 2016), and intestinal barrier maintenance (Natividad and Verdu, 2013). Our previous studies have demonstrated that Lactobacillus gasseri LA39 possesses important roles in promoting intestinal epithelial oxidative phosphorylation (Hu et al., 2018a) and preventing diarrhea (Hu et al., 2018b). A previous study showed that L. gasseri LA39 produced gassericin A bacteriocin, which is resistant to several pathogens (Kawai et al., 1994, 2001). However, the effect of L. gasseri LA39 on host liver metabolism has not been studied. Thus, dissecting the function of this strain in host liver metabolism can contribute to uncovering the mechanism of interplay between the host and L. gasseri LA39.

Germ-free (GF) mice are free from microbes (Bhattarai and Kashyap, 2016) and have been widely employed to investigate the interaction between host and microbiota (Yi and Li, 2012; Grover and Kashyap, 2014). In this work, considering the important function of the gut–liver axis in mammals (Tripathi et al., 2018; Tilg et al., 2022) and the good applicability of proteomics in mining the host potential biological events affected by microbes (Wright, 2018; Zhou et al., 2019; Zoued et al., 2021; Yan et al., 2022), we analyzed the hepatic proteomic profiles of GF mice after L. gasseri LA39 gavage. Specifically, we transferred the L. gasseri LA39 to GF mice by oral gavage and then explored the effect of L. gasseri LA39 on the hepatic protein expression profiles using the strategy of isobaric tags for relative and absolute quantification (iTRAQ). Our data showed that the primary BA biosynthesis pathway in the liver was activated by treatment with L. gasseri LA39. Additional targeted metabolomic analyses indicated that serum and fecal β‍-muricholic acid (a primary BA), dehydrolithocholic acid (a secondary BA), and glycolithocholic acid-3-sulfate (a secondary BA) were significantly increased after oral gavage of L. gasseri LA39. These results revealed the functions of L. gasseri LA39 in host BA metabolism and provide insights into its roles in the gut–liver axis.

2. Materials and methods

2.1. Bacteria

The facultative anaerobe L. gasseri LA39 strain was isolated from the feces of piglets as previously described (Hu et al., 2018b) and cultured in de Man, Rogosa, and Sharpe (MRS) medium at 37 ℃ under anaerobic conditions. An optical microscope combined with methylene blue staining was used to count the number of L. gasseri LA39.

2.2. Experimental animals

A total of 12 GF male mice (eight weeks of age) with similar body weight were mixed and then randomly divided into two groups (control (Ctrl) group and L. gasseri LA39 (LG) group, n=6 each). The mean body weight in the Ctrl group and the LG group was (36.07±1.87) g and (35.57±1.29) g, respectively. There was no significant difference in mean body weight between the two groups (P=0.8299, Student’s t-test). Mice in the LG group were orally gavaged with 200 μL phosphate-buffered saline (PBS) suspension containing the L. gasseri LA39 (1×108 CFU/mL, where CFU is colony forming units) every other day, while mice in the Ctrl group were orally gavaged with 200 μL sterile PBS. After two weeks, all mice were sacrificed and the liver, serum, feces, and intestinal samples were collected.

2.3. Procedures for proteomics based on iTRAQ

The procedures for the iTRAQ analysis were described previously in detail (Hu et al., 2018a; Nie et al., 2019). Briefly, lysis buffer combined with magnetic beads and TissueLyser was used to extract the hepatic proteins. After centrifugation, the supernatant was successively treated with dithiothreitol and iodoacetamide. Trypsin gold was used to digest the protein solution. The peptides were then desalted and dissolved in 0.5 mol/L tetraethylammonium bromide and labeled with iTRAQ reagent kits. The peptides were fractionated on the LC-20AB system (Shimadzu, Kyoto, Japan) and then vacuum-dried. Subsequently, the obtained peptides were redissolved and then treated using the LC-20AD system (Shimadzu). Finally, the peptides were ionized through a nano-electrospray ionization (nano-ESI) resource and loaded into a Q-Exactive mass spectrometer for data detection.

2.4. Proteomic data analyses

The raw data of iTRAQ were changed into the Mascot generic format (MGF). Mascot software (Matrix Science, London, UK) was used to run against the Uniprot (Mus musculus) database for protein identification. The quantification of iTRAQ data was conducted using the iQuant software (BGI, Shenzhen, China). Proteins with fold change of more than 1.2 or less than 0.83 and q-value of less than 0.05 in at least two replicates were defined as the differentially expressed proteins (DEPs), as described previously (Zhu et al., 2009; Huang et al., 2015; Hu et al., 2018a). WoLF PSORT software (Computational Biology Research Center, Tokyo, Japan) was employed to predict the subcellular localization of proteins. The Cluster of Orthologous Groups (COG) functional analysis was performed based on the COG database. The STRING database was used to analyze the protein‍–‍protein interaction (PPI). The Blast2GO software (BioBam, Valencia, Spain) was utilized for Gene Ontology (GO) analysis, including biological process and molecular function classifications. The pathway analysis was performed based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database.

2.5. Western blot

The total proteins of the livers of GF mice were extracted with the radioimmunoprecipitation assay (RIPA) protein lysis buffer (Sangon Biotech, C500005, Shanghai, China). The detailed procedures for western blot were previously described by Hu et al. (2017). The antibodies used were as follows: cytochrome P450 family 27 subfamily A member 1 (CYP27A1, Proteintech, 14739-1-AP, Chicago, USA), cytochrome P450 family 7 subfamily B member 1 (CYP7B1, Proteintech, 24889-1-AP), cytochrome P450 family 8 subfamily B member 1 (CYP8B1, Absin, abs135064, Shanghai, China), β‍-actin antibody (Sigma-Aldrich, A5441, St. Louis, USA), and horseradish peroxidase (HRP)-conjugated secondary antibodies (Cell signaling technology, 7074S and 7076S, Danvers, MA, USA).

2.6. Targeted metabolomic analyses of bile acids

The serum and fecal BAs were detected based on the AB SCIEX QTRAP 6500 liquid chromatography-tandem mass spectrometry (LC-MS/MS) platform. Briefly, 200 μL of methanol and 10 μL of internal standard mixed solution (1 μg/mL) were added into 50 μL of serum samples or 20 mg of fecal samples. The proteins were precipitated by refrigeration at -20 ℃ for 10 min. After centrifugation (12 000 r/min) for 10 min at 4 ℃, the supernatant was obtained and then evaporated to dryness. The extracts were reconstituted in 100 μL of 50% (volume fraction) methanol for further LC-MS/MS. The high-performance liquid chromatography (HPLC) conditions mainly included the following parameters: column, C18 (100 mm×2.1 mm, i.‍d., 1.8 μm); solvent system, double-distilled water (ddH2O) with 0.01% (volume fraction) acetic acid and 5 mmol/L ammonium acetate (A), acetonitrile with 0.01% acetic acid (B); flow rate, 0.35 mL/min; column temperature, 40 ℃; sample size, 3 μL. The ESI-MS/MS conditions included the following main parameters: ESI source temperature, 550 ℃; voltage, -4500 V; curtain gas, 35 psi (1 psi=6.895 kPa). Multiple reaction monitoring was conducted to quantify the BAs. The MS data were processed using the Analyst software (AB SCIEX, Framingham, MA, USA) and MultiQuant software (AB SCIEX). The standard curves of BAs were drawn based on the concentration ratio (external standard/internal standard) and peak area ratio (external standard/internal standard). The contents of BAs were calculated based on the standard curves. A total of 50 BA standards were purchased from Toronto Research Chemicals (TRC, Canada), Zzstandard (Shanghai, China), ANPEL (Shanghai, China), IsoReag (Shanghai, China), CMASS (Shanghai, China), and Boc Sciences (Shanghai, China) (the detailed information was shown in Table S1). Orthogonal partial least squares-discriminant analysis (OPLS-DA) was carried out using the package “MetaboAnalystR” in R software. The heatmap and violin plot were drawn by R software.

2.7. Hematoxylin-eosin staining

Hematoxylin-eosin (HE) staining was used to analyze the intestinal morphologic characteristics. The detailed experiments and analyses were performed according to previously described protocols (Hu et al., 2017). The villi heights and crypts depths in the duodenum, jejunum, and ileum were individually measured. Representative images at 40×, 100×, and 200× magnifications for intestinal tissues were obtained using a light microscope.

2.8. Statistical analyses

For Student’s t-tests, a P-value of <0.05 was defined as statistically significant. For two-way analysis of variance (ANOVA), a P-value of <0.05 was defined as statistically significant. The DEPs were defined with the fold change of more than 1.2 or less than 0.83 and a q-value of less than 0.05 in at least two replicates, as described previously (Zhu et al., 2009; Huang et al., 2015; Hu et al., 2018a). The differential BA metabolites between the two groups were identified using the criteria of variable importance in projection (VIP)≥1 by OPLS-DA, absolute log2(fold change)≥1, and P-value<0.05 by Student’s t-test.

3. Results

3.1. Identification of DEPs in the liver of GF mice induced by L. gasseri LA39

A growing body of evidence has revealed the important role of the gut–liver axis in mammals (Tripathi et al., 2018; Tilg et al., 2022), and proteomics have been commonly utilized in dissecting host‍–‍microbe interactions (Wright, 2018; Zhou et al., 2019; Zoued et al., 2021; Yan et al., 2022). GF mice provide a useful model to investigate the interplay between the host and microbiota (Yi and Li, 2012; Grover and Kashyap, 2014). To evaluate the roles of L. gasseri LA39 in the liver of GF mice, we administered L. gasseri LA39 to the GF mice by oral gavage. The iTRAQ strategy was used to analyze the profiles of hepatic protein expression in GF mice (Fig. 1a). From the results of three biological replicates, a total of 4761, 4793, and 4850 proteins were quantified in each replicate (Fig. 1b). In total, 128 proteins were upregulated and 123 proteins were downregulated by treatment with L. gasseri LA39 (Fig. 1c). The heatmap of DEPs was shown in Fig. 1d and the detailed information for DEPs was shown in Data S1.

Fig. 1. Proteomics analysis of protein expression profiles in the liver of germ-free (GF) mice using the isobaric tags for relative and absolute quantitation (iTRAQ) strategy. (a) Schematic workflow of proteomics analysis based on the iTRAQ strategy (Rep, replicate; Ctrl, control; LG, Lactobacillus gasseri LA39; LC-ESI-MS/MS, liquid chromatography-tandem mass spectrometry). (b) Venn diagram of quantified proteins for three biological replicates. (c) Scatter diagram of the differentially expressed proteins (DEPs). (d) Heatmap of the DEPs. The colors represent the mean iTRAQ ratio (LG/Ctrl, fold change) of three replicates. Proteins with fold change of more than 1.2 or less than 0.83 and q-value of less than 0.05 in at least two replicates were defined as DEPs.

Fig. 1

Open in a new tab

3.2. Subcellular localization, COG function annotation, and PPI network analyses of the DEPs induced by L. gasseri LA39

We next used the WoLF PSORT software to analyze the subcellular localization of DEPs induced by L. gasseri LA39 and assessed their organelle specificity. The results showed that the DEPs were mainly localized in the cytosol, extracellular matrix, mitochondria, endoplasmic reticulum, nucleus, and plasma membrane (Fig. 2a). The results of COG function annotation showed that the DEPs were mainly annotated these COG functions, such as “secondary metabolites biosynthesis, transport and catabolism,” “lipid transport and metabolism,” “signal transduction mechanisms,” and “posttranslational modification, protein turnover, chaperones” (Fig. 2b). The PPI network analysis based on the STRING database indicated that, among these 251 DEPs, a total of 70 proteins (including 37 upregulated proteins and 33 downregulated proteins) were shown in the Cytoscape (Fig. 3). The PPI network data suggested that these 70 DEPs might confer biological functions through interacting with each other to form complexes.

Fig. 2. Subcellular localization (a) and Cluster of Orthologous Groups (COG) function annotation (b) analyses of differentially expressed proteins (DEPs) in the liver of germ-free (GF) mice induced by Lactobacillus gasseri LA39.

Fig. 2

Open in a new tab

Fig. 3. Protein‍‒‍protein interaction (PPI) networks of differentially expressed proteins (DEPs) in the liver of germ-free (GF) mice induced by Lactobacillus gasseri LA39. The red and blue nodes represent the upregulated and downregulated proteins, respectively. The protein names were shown in the nodes.

Fig. 3

Open in a new tab

3.3. Biological process and molecular function analyses of the DEPs induced by L. gasseri LA39

Subsequently, we performed GO function annotation to dissect the potential role of DEPs induced by L. gasseri LA39. The biological processes of the DEPs were mainly enriched in the terms involved in lipid metabolism, such as “arachidonic acid metabolic process,” “long-chain fatty acid metabolic process,” “unsaturated fatty acid metabolic process,” “icosanoid metabolic process,” “fatty acid derivative metabolic process,” and “fatty acid metabolic process” (Fig. 4a). The molecular functions of DEPs were mainly enriched in terms such as “pheromone activity,” “steroid hydroxylase activity,” “arachidonic acid monooxygenase activity,” and “oxidoreductase activity” (Fig. 4b). These results of GO annotation suggested that hepatic lipid metabolism in GF mice may be regulated by treatment with L. gasseri LA39.

Fig. 4. Biological process and molecular function enrichment analyses of differentially expressed proteins (DEPs) in the liver of germ-free (GF) mice induced by Lactobacillus gasseri LA39. (a) The top 20 enriched biological processes for DEPs. (b) The top 20 enriched molecular functions for DEPs.

Fig. 4

Open in a new tab

3.4. Activation of primary bile acid biosynthesis pathway in the liver of GF mice by L. gasseri LA39

We further analyzed the KEGG pathways to evaluate the potential functions of L. gasseri LA39 on cell metabolism in the liver of GF mice. The top 20 enriched KEGG pathways, such as “primary bile acid biosynthesis,” “drug metabolism cytochrome P450,” “retinol metabolism,” “phenylalanine metabolism,” “arachidonic acid metabolism,” and “complement and coagulation cascades” were shown in Fig. 5a. Interestingly, “primary bile acid biosynthesis” was the most enriched pathway with the DEPs (Fig. 5a). A total of five proteins (including CYP27A1, CYP7B1, CYP8B1, α‍-methylacyl-CoA racemase (AMACR), and sterol carrier protein 2 (SCP2)) involved in the primary BA biosynthesis pathway were upregulated by treatment with L. gasseri LA39 (Fig. 5b). At the same time, a total of two proteins (including CYP46A1 and aldo-keto reductase family 1 member D1 (AKR1D1)) involved in the primary BA biosynthesis pathway were downregulated by treatment with L. gasseri LA39 (Fig. 5b).

Fig. 5. Primary bile acid (BA) biosynthesis pathway in the liver of germ-free (GF) mice activated by Lactobacillus gasseri LA39. (a) The top 20 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways for differentially expressed proteins (DEPs). (b) DEPs in the primary BA biosynthesis pathway. The red box shows that the corresponding proteins were upregulated by L. gasseri LA39, and the green box indicates that they were downregulated by L. gasseri LA39. (c) Representative western blots of several proteins, including cytochrome P450 family 27 subfamily A member 1 (CYP27A1), CYP7B1, CYP8B1, and β‍-actin (Ctrl, control; LG, L. gasseri LA39). (d‍‒‍f) Quantitation of CYP27A1 (d), CYP7B1 (e), and CYP8B1 (f) levels normalized to β‍-actin levels. All data are presented as mean±standard error of the mean (SEM), (n=3). * P<0.05 evaluated using Student's t-test.

Fig. 5

Open in a new tab

We next used western blot assay to further evaluate the levels of three representative DEPs (including CYP27A1, CYP7B1, and CYP8B1). Our results demonstrated that the protein levels of CYP27A1, CYP7B1 and CYP8B1 were significantly increased by treatment with L. gasseri LA39 and these data were consistent with the results of iTRAQ analyses (Figs. 5c‍–‍5f). Thus, these data suggested that the primary BA biosynthesis pathway in the liver of GF mice was activated by L. gasseri LA39.

3.5. Promotion of hepatic primary bile acid biosynthesis and intestinal secondary bile acid biotransformation by L. gasseri LA39

We used BAs-targeted metabolomics to investigate the serum BA profiles in GF mice to further confirm that the primary BA biosynthesis in the liver of GF mice was activated by L. gasseri LA39. The results of OPLS-DA showed an obvious distinction between the Ctrl group and the LG group, suggesting that treatment with L. gasseri LA39 had an obvious effect on the profiles of serum BAs in GF mice (Fig. 6a). Further heatmap analysis indicated that a total of 17 BAs were identified in the serum of GF mice (Fig. 6b). Among them, the level of serum β‍-muricholic acid (a primary BA) was significantly increased under treatment with L. gasseri LA39 (Fig. 6c). Thus, our data revealed that the primary BA biosynthesis in the liver of GF mice was promoted in response to treatment with L. gasseri LA39.

Fig. 6. Targeted metabolomic analysis of serum and fecal bile acid (BA) profiles in germ-free (GF) mice. (a) The orthogonal partial least squares-discriminant analysis (OPLS-DA) of serum BA profiles (Ctrl, control; LG, Lactobacillus gasseri LA39). (b) Heatmap analysis of serum BAs identified by targeted metabolomic analysis. (c‍‒‍e) Comparison analyses of serum β‍-muricholic acid (c), dehydrolithocholic acid (d), and glycolithocholic acid-3-sulfate (e) levels between the Ctrl and LG groups (n=6). (f‍‒‍h) Comparison analyses of fecal β‍-muricholic acid (f), dehydrolithocholic acid (g), and glycolithocholic acid-3-sulfate (h) levels between the Ctrl and LG groups (n=6). The differential metabolites between the two groups were identified using the criteria of variable importance in projection (VIP)≥1 by OPLS-DA, absolute log2(fold change)≥1, and * P<0.05 by Student's t-test.

Fig. 6

Open in a new tab

Interestingly, the results of targeted metabolomics demonstrated that the levels of two secondary BAs (including dehydrolithocholic acid and glycolithocholic acid-3-sulfate) in the serum were also significantly upregulated by treatment with L. gasseri LA39 (Figs. 6d and 6e), suggesting that secondary BA biotransformation was enhanced by L. gasseri LA39. It is known that the processes of biotransformation from primary BA to secondary BAs are carried out exclusively by gut microbes in the gastrointestinal tract (Nie et al., 2015; Wahlström et al., 2016), and a large proportion of BAs (90%‍–‍95%) in the digestive tract are reabsorbed into the portal vein blood (Cai et al., 2022). Thus, the results of serum secondary BA profiles suggested that L. gasseri LA39 also conferred the functions of secondary BA biotransformation on the intestinal tract.

We further examined the levels of BAs in the fecal samples of mice using targeted metabolomic analyses. The results indicated that the levels of β-muricholic acid, dehydrolithocholic acid, and glycolithocholic acid-3-sulfate in the feces of mice were increased by the administration of L. gasseri LA39 (Figs. 6f‍–‍6h), which was consistent with the alteration of serum BAs by L. gasseri LA39. There were large individual variations in the serum and fecal BA levels within the LG group (Fig. 6). Therefore, we speculated that the host responses to L. gasseri LA39 treatment may be different among GF mice. Previous studies also showed considerable individual variation in the serum BA levels within the treatment group (Liu et al., 2018; Zhang et al., 2018; Xie et al., 2021), further suggesting the presence of individual differences in the response of BA metabolism to stimulation. This diversity in the experimental animals may have a limited impact on the results due to that significance tests have been performed. Our results further showed that the villus height and the ratio of villus height to crypt depth in the ileum of mice were increased by L. gasseri LA39 administration, suggesting the potential improvement of this strain in intestinal health status (Fig. S1). Taken together, these findings revealed that the oral administration of L. gasseri LA39 promotes hepatic primary BA biosynthesis and intestinal secondary BA biotransformation.

4. Discussion

In the present study, we demonstrated the import-ant function of L. gasseri LA39 in promoting hepatic primary BA biosynthesis. A growing number of studies have reported the important function of the gut‍–liver axis in mammals (Silveira et al., 2022). The liver affects gut microbiota via producing primary BAs that may confer antimicrobial activity (Brandl et al., 2017). However, it was recently indicated that pathogen overload may promote the pathogenesis of NAFLD and hepatic inflammation (Scott, 2017; Ringseis et al., 2020). GF mice are free from all forms of microbial life and have been considered as an ideal model to investigate the interaction between host and microbiota (Bhattarai and Kashyap, 2016). Microbial strains can be introduced into GF mice to study their function on the host metabolism (Yi and Li, 2012; Grover and Kashyap, 2014; Al-Asmakh and Zadjali, 2015; Bhattarai and Kashyap, 2016). In our study, L. gasseri LA39, a promising probiotic, was given to GF mice by oral administration. Proteomics analysis revealed that 128 proteins were upregulated and 123 proteins were downregulated in the liver in response to treatment with L. gasseri LA39, suggesting that liver metabolism may be mediated by this bacterium. Our data provide fundamental knowledge about the potential functions of L. gasseri LA39 on host liver metabolism, and thus contribute to assessing its beneficial roles.

Recently, the iTRAQ strategy has been widely used to study the alteration of protein expression profiles (Treumann and Thiede, 2010). This approach provides accurate and high-throughput information on protein concentration ratios (DeSouza et al., 2005). Our previous studies have implemented the iTRAQ strategy to evaluate the profiles of porcine intestinal epithelial protein expression in response to dietary leucine supplementation (Hu et al., 2017), L. gasseri LA39 treatment (Hu et al., 2018a), and Lactobacillus frumenti treatment (Nie et al., 2019). In the current study, a total of 251 DEPs were identified in the liver of GF mice between the Ctrl group and the LG group. Thus, the iTRAQ strategy contributes to mining the host biological events that are preferentially altered by gut microbial species.

Our proteomics data revealed that the primary BA biosynthesis pathway in the liver was activated by treatment with L. gasseri LA39. BAs-targeted metabolomic analysis further demonstrated that serum β-muricholic acid, a primary BA, was increased by treatment with L. gasseri LA39. A previous study has linked the tauro-‍β‍-muricholic acid levels with the abundance of Lactobacillus genus in the gut and bile salt hydrolase (BSH) activity (Li et al., 2013). A recent study reported that Lactobacillus rhamnosus GG inhibited BA synthesis in the liver (Liu et al., 2020). Moreover, one study showed that probiotic mixture VSL#3 promoted the hepatic BA biosynthesis through inhibiting the farnesoid X receptor (FXR)‍–‍fibroblast growth factor 15 (FGF15) axis (Degirolamo et al., 2014). The probiotic mixture VSL#3 also increased the expression levels of two key proteins (including CYP7A1 and CYP8B1) involved in primary BA biosynthesis in the liver (Degirolamo et al., 2014). Based on our findings, the expression levels of five proteins (including CYP27A1, CYP7B1, CYP8B1, AMACR, and SCP2) in the liver were significantly increased by L. gasseri LA39 administration. However, the levels of CYP46A1 and AKR1D1 were decreased by treatment with L. gasseri LA39, and this result may be caused by the negative feedback mechanism of BA biosynthesis (Nie et al., 2015). These data suggested that the regulatory roles of gut microbes in the hepatic primary BA biosynthesis may be different among microbial species.

As is known, the transformation from primary BA to secondary BA depends on the gut microbiota (Wahlström et al., 2016; Guzior and Quinn, 2021; Cai et al., 2022). Our results indicated that the levels of two secondary BAs (including dehydrolithocholic acid and glycolithocholic acid-3-sulfate) in the serum and feces were significantly increased under treatment with L. gasseri LA39. Recently, it was also demonstrated that Lactobacillus species contributed to the biotransformation from primary BA to secondary BA (Wong et al., 2022). These genes have also been identified, such as BSHs that were associated with the secondary BA biotransformation in Lactobacillus species (de Boever et al., 2000; Foley et al., 2021; Kusada et al., 2021). Although the processes of biotransformation from primary BA to secondary BA are carried out by gut microbes, a large proportion of BAs (90%‍–‍95%) in the intestinal tract are reabsorbed into the portal vein blood (Cai et al., 2022). Thus, the profiles of serum secondary BAs can also indicate secondary BA biotransformation in the intestinal tract. In our experiment, L. gasseri LA39 facilitated secondary BA biotransformation in the gut, providing important insights into gut microbiota-mediated BA metabolism.

Growing evidence suggests that the enterohepatic FXR‍–‍FGF15 axis is involved in hepatic BA biosynthesis mediated by gut microbes (Degirolamo et al., 2014; Liu et al., 2020). Thus, the changes induced by L. gasseri LA39 in hepatic primary BA biosynthesis in this study may not have been induced by enhanced intestinal oxidative phosphorylation. Our previous study showed that L. gasseri LA39 promoted oxidative phosphorylation in cultured intestinal epithelial cells in vitro (Hu et al., 2018a), suggesting that the enhancement of oxidative phosphorylation induced by L. gasseri LA39 may not be mediated by hepatic primary BA biosynthesis. Given that the transformation of primary BAs to secondary BAs in the intestinal tract depends on the gut microbiota through microbial enzymes, such as BSH and 7α/β-dehydroxylases (Cai et al., 2022), L. gasseri LA39-induced changes in the intestinal secondary BA biotransformation in this study might not have been the result of enhanced intestinal oxidative phosphorylation. Future studies should also be performed to evaluate the effects of administering microbial species on host BA metabolism not only in GF animals but also in conventional animals.

Accumulating data have suggested that dysregulated BA metabolism is an etiological factor for several diseases (Cai et al., 2022). A previous study indicated that secondary BAs have important roles in inhibiting Clostridium difficile growth, thus preventing intestinal C. difficile infection (CDI) (Buffie et al., 2015). The activation of Takeda G protein-coupled receptor 5 (TGR5) through secondary BA ligands contributes to blocking liver inflammation and fibrosis progression (Zhang et al., 2022). Thus, the manipulation of L. gasseri LA39 that produces secondary BAs may prevent intestinal CDI and liver fibrosis progression. It has been suggested that FXR activation through primary BAs may be a protective strategy to inflammatory bowel disease (IBD) (Gadaleta et al., 2011; Ogilvie and Jones, 2012). Therefore, L. gasseri LA39 may be a potential probiotic used to prevent IBD via promoting the biosynthesis of primary BAs, as demonstrated in our study.

5. Conclusions

In sum, the present study revealed that L. gasseri LA39 promotes hepatic primary BA biosynthesis and intestinal secondary BA biotransformation. Our findings provide a basis for understanding gut microbiota–BA interplay, and suggest that L. gasseri LA39 may become a promising probiotic used to prevent diseases associated with BA metabolism.

Supplementary information

Table S1; Fig. S1; Data S1

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 31925037, 31730090, and 32102499), the National Postdoctoral Program for Innovative Talents of China (No. BX20190133), the Postdoctoral Science Foundation of China (No. 2019M662671), and the Natural Science Foundation of Hubei Province (Nos. 2022CFB358 and 2021CFA018).

Author contributions

Jun HU designed the study, conducted the experiments, and wrote the manuscript. Qiliang HOU, Wenyong ZHENG, and Tao YANG conducted the experiments. Xianghua YAN designed the study and wrote the manuscript. All authors have read and approved the final manuscript, and therefore, have full access to all the data in the study and take responsibility for the integrity and security of the data.

Compliance with ethics guidelines

Jun HU, Qiliang HOU, Wenyong ZHENG, Tao YANG, and Xianghua YAN declare that they have no conflict of interest.

All mice procedures were carried out following the guidelines approved by Institutional Animal Care and Use Committee of Huazhong Agricultural University, Wuhan, China (approval No. HZAUMO-2018-067).

References

  1. Al-Asmakh M, Zadjali F, 2015. Use of germ-free animal models in microbiota-related research. J Microbiol Biotechnol, 25(10): 1583-1588. 10.4014/jmb.1501.01039 [DOI] [PubMed] [Google Scholar]
  2. Bajaj JS, Ng SC, Schnabl B, 2022. Promises of microbiome-based therapies. J Hepatol, 76(6): 1379-1391. 10.1016/j.jhep.2021.12.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bernardeau M, Guguen M, Vernoux JP, 2006. Beneficial lactobacilli in food and feed: long-term use, biodiversity and proposals for specific and realistic safety assessments. FEMS Microbiol Rev, 30(4): 487-513. 10.1111/j.1574-6976.2006.00020.x [DOI] [PubMed] [Google Scholar]
  4. Bhattarai Y, Kashyap PC, 2016. Germ-free mice model for studying host-microbial interactions. In: Proetzel G, Wiles M (Eds.), Mouse Models for Drug Discovery. Humana Press, New York, p.123-135. 10.1007/978-1-4939-3661-8_8 [DOI] [PubMed] [Google Scholar]
  5. Brandl K, Kumar V, Eckmann L, 2017. Gut-liver axis at the frontier of host-microbial interactions. Am J Physiol Gastrointest Liver Physiol, 312(5): G413-G419. 10.1152/ajpgi.00361.2016 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Buffie CG, Bucci V, Stein RR, et al. , 2015. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile . Nature, 517(7533): 205-208. 10.1038/nature13828 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cai J, Sun LL, Gonzalez FJ, 2022. Gut microbiota-derived bile acids in intestinal immunity, inflammation, and tumorigenesis. Cell Host Microbe, 30(3): 289-300. 10.1016/j.chom.2022.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. de Boever P, Wouters R, Verschaeve L, et al. , 2000. Protective effect of the bile salt hydrolase-active Lactobacillus reuteri against bile salt cytotoxicity. Appl Microbiol Biotechnol, 53(6): 709-714. 10.1007/s002530000330 [DOI] [PubMed] [Google Scholar]
  9. Degirolamo C, Rainaldi S, Bovenga F, et al. , 2014. Microbiota modification with probiotics induces hepatic bile acid synthesis via downregulation of the Fxr-Fgf15 axis in mice. Cell Rep, 7(1): 12-18. 10.1016/j.celrep.2014.02.032 [DOI] [PubMed] [Google Scholar]
  10. DeSouza L, Diehl G, Rodrigues MJ, et al. , 2005. Search for cancer markers from endometrial tissues using differentially labeled tags iTRAQ and cICAT with multidimensional liquid chromatography and tandem mass spectrometry. J Proteome Res, 4(2): 377-386. 10.1021/pr049821j [DOI] [PubMed] [Google Scholar]
  11. Foley MH, O'Flaherty S, Allen G, et al. , 2021. Lactobacillus bile salt hydrolase substrate specificity governs bacterial fitness and host colonization. Proc Natl Acad Sci USA, 118(6): e2017709118. 10.1073/pnas.2017709118 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Gadaleta RM, van Erpecum KJ, Oldenburg B, et al. , 2011. Farnesoid X receptor activation inhibits inflammation and preserves the intestinal barrier in inflammatory bowel disease. Gut, 60(4): 463-472. 10.1136/gut.2010.212159 [DOI] [PubMed] [Google Scholar]
  13. Grover M, Kashyap PC, 2014. Germ-free mice as a model to study effect of gut microbiota on host physiology. Neurogastroenterol Motil, 26(6): 745-748. 10.1111/nmo.12366 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Guzior DV, Quinn RA, 2021. Review: microbial transformations of human bile acids. Microbiome, 9: 140. 10.1186/s40168-021-01101-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Hu J, Nie YF, Chen SF, et al. , 2017. Leucine reduces reactive oxygen species levels via an energy metabolism switch by activation of the mTOR-HIF-‍‍1α pathway in porcine intestinal epithelial cells. Int J Biochem Cell Biol, 89: 42-56. 10.1016/j.biocel.2017.05.026 [DOI] [PubMed] [Google Scholar]
  16. Hu J, Ma LB, Zheng WY, et al. , 2018a. Lactobacillus gasseri LA39 activates the oxidative phosphorylation pathway in porcine intestinal epithelial cells. Front Microbiol, 9: 3025. 10.3389/fmicb.2018.03025 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hu J, Ma LB, Nie YF, et al. , 2018b. A microbiota-derived bacteriocin targets the host to confer diarrhea resistance in early-weaned piglets. Cell Host Microbe, 24(6): 817-832.e8. 10.1016/j.chom.2018.11.006 [DOI] [PubMed] [Google Scholar]
  18. Huang HY, Zhang WT, Jiang WY, et al. , 2015. RhoGDIβ inhibits bone morphogenetic protein 4 (BMP4)‍-induced adipocyte lineage commitment and favors smooth muscle-like cell differentiation. J Biol Chem, 290(17): ‍11119-11129. 10.1074/jbc.M114.608075 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Kawai Y, Saito T, Toba T, et al. , 1994. Isolation and characterization of a highly hydrophobic new bacteriocin (gassericin A) from Lactobacillus gasseri LA39. Biosci Biotechnol Biochem, 58(7): 1218-1221. 10.1271/bbb.58.1218 [DOI] [PubMed] [Google Scholar]
  20. Kawai Y, Ishii Y, Uemura K, et al. , 2001. Lactobacillus reuteri LA6 and Lactobacillus gasseri LA39 isolated from faeces of the same human infant produce identical cyclic bacteriocin. Food Microbiol, 18(4): 407-415. 10.1006/fmic.2001.0412 [DOI] [Google Scholar]
  21. Kleerebezem M, Vaughan EE, 2009. Probiotic and gut lactobacilli and bifidobacteria: molecular approaches to study diversity and activity. Annu Rev Microbiol, 63: 269-290. 10.1146/annurev.micro.091208.073341 [DOI] [PubMed] [Google Scholar]
  22. Kusada H, Morinaga K, Tamaki H, 2021. Identification of bile salt hydrolase and bile salt resistance in a probiotic bacterium Lactobacillus gasseri JCM1131T . Microorganisms, 9(5): 1011. 10.3390/microorganisms9051011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Lebeer S, Vanderleyden J, de Keersmaecker SCJ, 2008. Genes and molecules of lactobacillisupporting probiotic action. Microbiol Mol Biol Rev, 72(4): 728-764. 10.1128/MMBR.00017-08 [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Lemon KP, Armitage GC, Relman DA, et al. , 2012. Microbiota-targeted therapies: an ecological perspective. Sci Transl Med, 4(137): 137rv5. 10.1126/scitranslmed.3004183 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Li F, Jiang CT, Krausz KW, et al. , 2013. Microbiome remodelling leads to inhibition of intestinal farnesoid X receptor signalling and decreased obesity. Nat Commun, 4: 2384. 10.1038/ncomms3384 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Liu YH, Chen KF, Li FY, et al. , 2020. Probiotic Lactobacillus rhamnosus GG prevents liver fibrosis through inhibiting hepatic bile acid synthesis and enhancing bile acid excretion in mice. Hepatology, 71(6): 2050-2066. 10.1002/hep.30975 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Liu ZJ, Xu C, Tian R, et al. , 2021. Screening beneficial bacteriostatic lactic acid bacteria in the intestine and studies of bacteriostatic substances. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 22(7): 533-547. 10.1631/jzus.B2000602 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Liu ZM, Zhang ZF, Huang M, et al. , 2018. Taurocholic acid is an active promoting factor, not just a biomarker of progression of liver cirrhosis: evidence from a human metabolomic study and in vitro experiments. BMC Gastroenterol, 18: 112. 10.1186/s12876-018-0842-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Marchesi JR, Adams DH, Fava F, et al. , 2016. The gut microbiota and host health: a new clinical frontier. Gut, 65(2): 330-339. 10.1136/gutjnl-2015-309990 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Maslennikov R, Ivashkin V, Efremova I, et al. , 2021. Probiotics in hepatology: an update. World J Hepatol, 13(9): ‍1154-1166. 10.4254/wjh.v13.i9.1154 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Natividad JMM, Verdu EF, 2013. Modulation of intestinal barrier by intestinal microbiota: pathological and therapeutic implications. Pharmacol Res, 69(1): 42-51. 10.1016/j.phrs.2012.10.007 [DOI] [PubMed] [Google Scholar]
  32. Nie YF, Hu J, Yan XH, 2015. Cross-talk between bile acids and intestinal microbiota in host metabolism and health. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 16(6): ‍436-446. 10.1631/jzus.B1400327 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Nie YF, Hu J, Hou QL, et al. , 2019. Lactobacillus frumenti improves antioxidant capacity via nitric oxide synthase 1 in intestinal epithelial cells. FASEB J, 33(10): ‍10705-10716. 10.1096/fj.201900253RR [DOI] [PubMed] [Google Scholar]
  34. Ogilvie LA, Jones BV, 2012. Dysbiosis modulates capacity for bile acid modification in the gut microbiomes of patients with inflammatory bowel disease: a mechanism and marker of disease? Gut, 61(11): 1642-1643. 10.1136/gutjnl-2012-302137 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Ramakrishna BS, 2013. Role of the gut microbiota in human nutrition and metabolism. J Gastroenterol Hepatol, 28(S4): 9-17. 10.1111/jgh.12294 [DOI] [PubMed] [Google Scholar]
  36. Ringseis R, Gessner DK, Eder K, 2020. The gut‒liver axis in the control of energy metabolism and food intake in animals. Annu Rev Anim Biosci, 8: 295-319. 10.1146/annurev-animal-021419-083852 [DOI] [PubMed] [Google Scholar]
  37. Rooks MG, Garrett WS, 2016. Gut microbiota, metabolites and host immunity. Nat Rev Immunol, 16(6): 341-352. 10.1038/nri.2016.42 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Scott A, 2017. Gut‍‒‍liver axis: menace in the microbiota. Nature, 551(7681): S94-S95. 10.1038/d41586-017-06924-3 [DOI] [PubMed] [Google Scholar]
  39. Selle K, Klaenhammer TR, 2013. Genomic and phenotypic evidence for probiotic influences of Lactobacillus gasseri on human health. FEMS Microbiol Rev, 37(6): 915-935. 10.1111/1574-6976.12021 [DOI] [PubMed] [Google Scholar]
  40. Silveira MAD, Bilodeau S, Greten TF, et al. , 2022. The gut‒liver axis: host microbiota interactions shape hepatocarcinogenesis. Trends Cancer, 8(7): 583-597. 10.1016/j.trecan.2022.02.009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Sommer F, Bäckhed F, 2013. The gut microbiota—masters of host development and physiology. Nat Rev Microbiol, 11(4): 227-238. 10.1038/nrmicro2974 [DOI] [PubMed] [Google Scholar]
  42. Thomson AW, Knolle PA, 2010. Antigen-presenting cell function in the tolerogenic liver environment. Nat Rev Immunol, 10(11): 753-766. 10.1038/nri2858 [DOI] [PubMed] [Google Scholar]
  43. Tilg H, Adolph TE, Trauner M, 2022. Gut-liver axis: pathophysiological concepts and clinical implications. Cell Metab, 34(11): 1700-1718. 10.1016/j.cmet.2022.09.017 [DOI] [PubMed] [Google Scholar]
  44. Tremaroli V, Bäckhed F, 2012. Functional interactions between the gut microbiota and host metabolism. Nature, 489(7415): 242-249. 10.1038/nature11552 [DOI] [PubMed] [Google Scholar]
  45. Treumann A, Thiede B, 2010. Isobaric protein and peptide quantification: perspectives and issues. Expert Rev Proteomics, 7(5): 647-653. 10.1586/epr.10.29 [DOI] [PubMed] [Google Scholar]
  46. Tripathi A, Debelius J, Brenner DA, et al. , 2018. The gut‒liver axis and the intersection with the microbiome. Nat Rev Gastroenterol Hepatol, 15(7): 397-411. 10.1038/s41575-018-0011-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Ubeda C, Pamer EG, 2012. Antibiotics, microbiota, and immune defense. Trends Immunol, 33(9): 459-466. 10.1016/j.it.2012.05.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. van Baarlen P, Wells JM, Kleerebezem M, 2013. Regulation of intestinal homeostasis and immunity with probiotic lactobacilli. Trends Immunol, 34(5): 208-215. 10.1016/j.it.2013.01.005 [DOI] [PubMed] [Google Scholar]
  49. Wahlström A, Sayin SI, Marschall HU, et al. , 2016. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab, 24(1): 41-50. 10.1016/j.cmet.2016.05.005 [DOI] [PubMed] [Google Scholar]
  50. Wong WY, Chan BD, Sham TT, et al. , 2022. Lactobacillus casei strain Shirota ameliorates dextran sulfate sodium-induced colitis in mice by increasing taurine-conjugated bile acids and inhibiting NF‍-‍κB signaling via stabilization of IκBα. Front Nutr, 9: 816836. 10.3389/fnut.2022.816836 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Wright MH, 2018. Chemical proteomics of host-microbe interactions. Proteomics, 18(18): 1700333. 10.1002/pmic.201700333 [DOI] [PubMed] [Google Scholar]
  52. Xie ZY, Zhang LJ, Chen EM, et al. , 2021. Targeted metabolomics analysis of bile acids in patients with idiosyncratic drug-induced liver injury. Metabolites, 11(12): 852. 10.3390/metabo11120852 [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yan ZZ, Chen BX, Yang YQ, et al. , 2022. Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions. Nat Microbiol, 7(9): 1361-1375. 10.1038/s41564-022-01196-8 [DOI] [PubMed] [Google Scholar]
  54. Yi P, Li LJ, 2012. The germfree murine animal: an important animal model for research on the relationship between gut microbiota and the host. Vet Microbiol, 157(1-2): 1-7. 10.1016/j.vetmic.2011.10.024 [DOI] [PubMed] [Google Scholar]
  55. Zhang QQ, Huang WQ, Gao YQ, et al. , 2018. Metabolomics reveals the efficacy of caspase inhibition for saikosaponin D-induced hepatotoxicity. Front Pharmacol, 9: 732. 10.3389/fphar.2018.00732 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zhang YL, Li ZJ, Gou HZ, et al. , 2022. The gut microbiota-bile acid axis: a potential therapeutic target for liver fibrosis. Front Cell Infect Microbiol, 12: 945368. 10.3389/fcimb.2022.945368 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zhou WY, Sailani MR, Contrepois K, et al. , 2019. Longitudinal multi-omics of host-microbe dynamics in prediabetes. Nature, 569(7758): 663-671. 10.1038/s41586-019-1236-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  58. Zhu MM, Dai SJ, McClung S, et al. , 2009. Functional differentiation of Brassica napus guard cells and mesophyll cells revealed by comparative proteomics. Mol Cell Proteomics, 8(4): 752-766. 10.1074/mcp.M800343-MCP200 [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Zoued A, Zhang HL, Zhang T, et al. , 2021. Proteomic analysis of the host-pathogen interface in experimental cholera. Nat Chem Biol, 17(11): 1199-1208. 10.1038/s41589-021-00894-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1; Fig. S1; Data S1
JZhejiangUnivSciB-24-8-734-s001.zip (2.3MB, zip)

Articles from Journal of Zhejiang University. Science. B are provided here courtesy of Zhejiang University Press

RESOURCES