Gut microbiota Lactobacillus johnsonii alleviates hyperuricemia by modulating intestinal urate and gut microbiota-derived butyrate

Abstract

Background:

Gut microbiota are important for uric acid (UA) metabolism in hyperuricemia (HUA); however, the underlying mechanisms of how the gut microbiota regulate intestinal UA metabolism remain unclear. This study aimed to explore the function of the intestine in HUA and to further reveal the possible mechanism.

Methods:

We conducted gut microbiota depletion to validate the role of gut microbiota in UA metabolism. A mouse model of HUA was established, and the gut microbiota and microbiome-derived metabolites were analyzed via 16S RNA gene sequencing and metabolomics analysis. The mechanism of the gut microbiota in HUA was elucidated by in vivo and in vitro experiments.

Results:

Antibiotic treatment elevated serum UA, disturbed purine metabolism, and decreased the relative abundance of Lactobacillus. HUA mice had a lower relative abundance of Lactobacillus johnsonii (L. johnsonii) and decreased gut butyrate concentration. Supplementation of L. johnsonii significantly reduces serum UA in hyperuricemia mice by preventing UA synthesis and promoting the excretion of gut purine metabolites. In addition, L. johnsonii enhanced intestinal UA excretion by heightening the urate transporter ABCG2 (adenosine triphosphate-binding cassette transporter, subfamily G, member 2) expression, and increasing the levels of butyrate, which upregulated ABCG2 expression via the Wnt5a/b/β-catenin signaling pathway.

Conclusion:

Our results suggest that gut microbiota and microbiota-derived metabolites directly regulate gut UA metabolism, highlighting potential applications in the treatment of diet-induced HUA by targeting gut microbiota and its metabolites.

Introduction

Hyperuricemia (HUA) is characterized by serum uric acid (SUA) levels exceeding 360 µmol/L in women and 420 µmol/L in men^[1,2] and has been proved to be the risk factor for gout, diabetes, hyperlipidemia, and hypertension.^[3] The rising prevalence of HUA poses a significant threat to human health,^[4,5] highlighting the importance of maintaining serum uric acid (UA) homeostasis for overall health.^[6]

Recently, it has been proved that gut microbiota play a crucial role in purine,^[7] amino acid,^[8] and lipid metabolism,^[9] contributing to HUA development and purine degradation.^[10] HUA animal model and cohort studies have found that gut dysbiosis was present in the HUA groups.^[11] A cohort study found HUA participants had a lower relative abundance of Coprococcus and altered amino acid and nucleotide metabolism compared with the control group.^[12] However, the direct link between gut microbiota and HUA remains underexplored.

Decreased UA excretion is another primary cause of HUA,^[13] with the gut responsible for 30% of UA excretion. Although 70% of UA is excreted by the kidneys, the intestine compensates by increasing UA excretion rate to maintain serum UA levels when renal function is impaired.^[14] Moreover, food-derived purines are absorbed or excreted by the gut. Despite this, research has predominantly focused on renal UA excretion and hepatic UA synthesis, often overlooking the intestine’s role in UA regulation. The mechanisms by which gut microbiota regulate UA, especially in the excretion of UA and diet-related purines, are not well understood.

In this study, we used an HUA mouse model along with gut microbiota and metabolomics analyses, obtained the target microbiota, and explored the effect of the target microbiota on the excretion of UA and purines from the gut and its underlying mechanism.

Methods

Animals

We obtained 7-week-old male C57BL/6J mice (urate oxidase [UOX]^+/+), UOX knockout (UOX^–/–) mice, and UOX knockout heterozygous (+/–) C57BL/6J (UOX^+/–) mice from the Institute of Metabolic Diseases, Qingdao University. The mice were housed in a light- and temperature-controlled room (12-h dark, 12-h light, 23 ± 1°C) with no restrictions on water or diet. All animal experimental procedures were approved by the animal ethics committee of Qingdao University Hospital (Approval No. AHQU-MAL20210430).

Antibiotic treatment

We randomly divided C57BL/6J mice into two groups: the control group (NC) and the antibiotics treatment group (NC-Ab). The NC group received sterile drinking water, whereas the NC-Ab group received drinking water containing antibiotics, including ampicillin (0.25 g/kg) (Sangon Biotech, Shanghai, China), neomycin (0.25 g/kg) (Sangon Biotech), and metronidazole (0.05 g/kg) (Solarbio, Beijing, China). The antibiotic and sterile drinking water were refreshed daily. After 2 weeks, all the mice were euthanized using phenobarbital. Blood samples were obtained for biochemical analyses, and tissues, including the liver, kidney, colon, and intestine, were obtained for histological analyses. Feces were collected for 16S rRNA sequencing.

Establishment of the HUA mouse model

In our previous study, the UA levels of UOX (+/–) mice are comparable to those of UOX (+/+) mice, but significantly lower than UOX (–/–) mice. When UOX (+/–) and UOX (+/+) mice were fed a high-yeast diet and given an intraperitoneal injection of potassium oxonate (PO) to establish the HUA model, the serum UA of UOX (+/–) mice was higher than that of UOX (+/+), and hematoxylin and eosin (H&E) staining of kidneys showed that there were no significant pathological changes in UOX (+/+) and UOX (+/–) groups.^[15] We thus chose UOX (+/−) mice to establish the HUA model in the present study. The 7-week-old male UOX (+/−) mice were randomly assigned to two groups: UA normal (control) and HUA groups. HUA group was fed a high-yeast diet and injected intraperitoneally with 250 mg·kg^–1·day^–1 PO (97%; Sigma-Aldrich, St. Louis, MO, Germany) at 9:00 a.m. The NC group received a standard diet and was injected intraperitoneally with a vehicle (0.5% carboxymethyl cellulose, CMC; Macklin, Shanghai, China). Fresh feces and orbital blood samples were obtained once a week and frozen in a refrigerator at –80°C for subsequent analysis of gut microbiota and UA levels.

Establishment of the HUA mouse model and gut microbiota management with L. johnsonii, L. murinus, and sodium butyrate

The 7-week-old male UOX (+/−) mice were randomly assigned to five groups: UA normal (control), HUA, HUA treated with Lactobacillus johnsonii (L. johnsonii) (HUA-Lac.), HUA treated with Lactobacillus murinus (L. murinus) (HUA-Lac. M), and sodium butyrate treatment (HUA-NaB) groups. All groups received water containing antibiotics (ampicillin 1 g/L, vancomycin 0.5 g/L, neomycin 1 g/L, and metronidazole 1 g/L) from day 1 to day 7, and the water was refreshed daily. After 7 days, the control group received normal drinking water and forage, and the other groups were given regular drinking water. From day 11, the HUA, HUA-Lac., HUA-Lac. M, and HUA-NaB groups were fed a high-yeast diet and injected intraperitoneally with 250 mg·kg^–1·day^–1 PO at 9:00 a.m. At 10:00 a.m., the HUA-Lac. group was gavaged with L. johnsonii (1 × 10⁹ CFU/mice) in 0.15 mL phosphate-buffered saline (PBS) (pH 7.4), the HUA-Lac.M group was gavaged with L. murinus (1 × 10⁹ CFU/mice) in 0.15 mL PBS, the HUA-NaB group was treated with 200 mg·kg^–1·day^–1 sodium butyrate (Yuanye Bio-Technology Co., Ltd, Shanghai, China), and the HUA group was gavaged with PBS. The control group received a standard diet and was gavaged with PBS, then injected intraperitoneally with a vehicle (0.5% carboxymethyl cellulose, CMC; Macklin, Shanghai, China). The weights of the mice in each group were recorded every 2 days. Fresh feces, 24-h urine, and orbital blood samples were obtained once a week and frozen in a refrigerator at –80°C for subsequent analysis of gut microbiota and UA levels. After 30 days, the mice were euthanized using phenobarbital. Blood samples were obtained and maintained at room temperature for 2 h, then centrifuged (3500 r/min, 15 min), and the serum was collected. Tissues (kidney, liver, colon, and intestine) and feces were collected from all mice for histological and metabolomics analyses. All samples collected were immediately placed in a refrigerator at –80°C.

Western blotting

The NCM460 cells (Shanghai Institute of Cell Biology, Shanghai, China) and intestinal tissues were lysed using a lysis buffer (Cat. R0010, Solarbio). Primary antibodies, including ABCG2 (adenosine triphosphate-binding cassette transporter, subfamily G, member 2) (1:1000; ab207732; Abcam, UK), Wnt 5a/b (1:1000; 2530s; Cell Signaling Technology, USA), β-catenin (1:1000; 8480s; Cell Signaling Technology), and GAPDH (glyceraldehyde 3-phosphate dehydrogenase, 1:1000; 8884; Cell Signaling Technology), were used.

Histomorphological analysis

Liver, renal, intestine, and colon samples from all mice were washed in PBS and immediately fixed in a 4% paraformaldehyde solution (Cat. P1110; Solarbio). Following this, they were embedded in paraffin, sectioned into 5-μm slices, stained using hematoxylin and eosin (Zhongshan Golden Bridge Biotechnology Co., Ltd. Beijing, China), and observed under a microscope at 200× magnification to evaluate morphological characteristics.

Immunohistochemistry

Intestinal tissues from all mice were harvested, fixed in 4% paraformaldehyde (Cat. P1110; Solarbio), embedded in paraffin blocks, and sectioned into 5‑μm slices. After deparaffinization, rehydration, antigen retrieval, and inhibition of endogenous peroxidase, sections were blocked with 5% bovine serum albumin (Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd.) for 30 min at 37°C and incubated overnight at 4°C with rabbit anti-ABCG2 (1:1000; 42078; Cell Signaling Technology, USA), then incubated with goat anti-rabbit immunoglobulin G (peroxidase-conjugated) secondary antibodies (1:500; Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd.), and the slides were maintained at room temperature for 1 h.

Detection of short-chain fatty acids in feces

We accurately weighed appropriate amounts of feces into 2 mL clear centrifugal tubes. We then added 50 µL of 15% phosphoric acid (CAS: 7664-38-2, ChemicalBook), 100 μL of 125 μg/mL isotonic acid (as the internal standard), and 400 μL of diethyl ether to the tube and homogenized for 1 min. Following this, the mixture was centrifuged, and the supernatants were filtered through a 0.02-μm membrane and transferred into a gas chromatography-mass spectrometer (Thermo Fisher Scientific, USA). Levels of acetic, propionic, butyric, isobutyric, valeric, and isovaleric acids were determined using standard curves with 10 short-chain fatty acids (SCFAs) standard concentrations.

16S rRNA gene sequencing and data analysis

DNA was extracted from the fecal samples using the QIAamp Fast DNA Stool Mini Kit (Qiagen, USA). The V3/V4 region of the 16S rRNA gene was amplified via polymerase chain reaction (PCR) using specific primers: forward: 5′-ACTCCTACGGGAGGCAGCA-3′ and reverse: 5′-GGACTACHVGGGTWTCTAAT-3′. PCR products were purified and quantified using a microplate reader (BioTek Flx800, BioTek Instruments, Inc., USA). The sequencing libraries were prepared using the TruSeq Nano DNA LT Library Prep Kit for Illumina (NEB, USA) according to the protocol provided with the kit and sequenced on an Illumina platform (MiSeq/NovaSeq) with paired-end reads. The raw sequencing data were processed and analyzed using the QIIME2 (2019.4) software (https://qiime2.org/) to evaluate the alpha and beta diversities of the samples based on the ggplot2 package. Differences in bacterial features among the groups were identified via MetagenomeSeq analysis and linear discriminant analysis (LDA).^[16]

Metabolomics analysis

Fifty milligrams of feces were placed in a clean tube, and 1000 µL of tissue extract (75% 9:1 methanol:chloroform, 25% H₂O) (CAS 67-56-1; Thermo Fisher Scientific) was added, along with three steel balls. Following this, the samples were ground at a frequency of 50 Hz for 60 s three times. Next, the samples were sonicated at room temperature for 30 min, placed in an ice bath for 30 min, and then centrifuged at 4°C and 12,000 r/min for 10 min. The supernatants were transferred to new 2 mL centrifuge tubes, concentrated, and dried. Furthermore, the samples were reconstituted with 200 µL of 50% acetonitrile solution containing 2-amino-3-(2-chloro-phenyl)-propionic acid (4 ppm) stored at 4°C. Finally, the supernatants were filtered through a 0.22-μm membrane and transferred to detection vials for liquid chromatography-mass spectrometry analysis.^[17] A pooled quality control sample consisting of equal aliquots of all samples was used to correct the mixed samples. The Proteo Wizard software package was used to convert the raw data into mzXML format using MSConvert (v3.0.8789, https://proteowizard.sourceforge.io/tools/msconvert.html), and feature detection, retention time correction, and alignment were performed using eXtensible computational mass spectrometry. All multivariate data analysis and modeling were performed using the Ropls software (Umetrics, Umea, Sweden). The data were scaled to mean centering. Principal component analysis, orthogonal partial least squares discriminant analysis (OPLS-DA), and partial least squares discriminant analysis models were established. All evaluated models were tested for overfitting using the permutation test method. The P value, variable importance projection produced by OPLS-DA, and fold change were applied to discover the contributable variable for classification. Metabolites with a P value <0.05 and variable importance projection value >1 were considered statistically significant and further analyzed for Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment.^[18]

Isolation and culture of L. johnsonii and L. murinus

Fresh mouse feces were collected, diluted with sterile PBS, spread onto Columbia blood agar plates (Qingdao Hope Bio-Technology Co., Ltd., China), and incubated under anaerobic conditions at 37°C for 24 h. The colonies were preliminarily screened based on the morphology of L. johnsonii and L. murinus and further verified by mass spectrometry (IVD MALDI Biotyper 2.3; Burker Scientific Technology Co., Ltd., China). Based on the mass spectrometry results, L. johnsonii and L. murinus were isolated and cultured on Columbia blood agar plates, and single colonies were observed. The colonies were stained via Gram staining and examined under a microscope for consistency with expected L. johnsonii and L. murinus morphology. Correctly identified single colonies were subjected to additional mass spectrometry analysis and, if confirmed, selected for proliferation culture for further analysis.

16S rRNA gene sequencing of L. johnsonii and L. murinus

DNA was extracted from L. johnsonii and L. murinus using a bacterial DNA kit (TIANGEN Biotechnology, China). We then amplified the 16S rRNA and sequenced the L. johnsonii and L. murinus strains. Next, the sequences of upper strains were submitted to the Basic Local Alignment Search Tool (BLAST) engine (https://blast.ncbi.nlm.nih.gov/Blast.cgi).

Cell culture

NCM460 cells were purchased from the Shanghai Institute of Cell Biology and cultured in Dulbecco’s Modified Eagle Medium (Solarbio) containing 10% fetal bovine serum (Gibco, Adelaide, Australia) at 37°C in 5% CO₂. Before treatment, cells were cultured in a serum-free medium for 24 h to maintain a growth arrest period. Following this, they were treated with sodium butyrate (0, 0.2, 0.5, 1.0, 2.0, and 4.0 mmol/L) for 72 h. Afterward, the cells were harvested for subsequent analysis.

Statistical analysis

The data of this study were analyzed using SPSS 19.0 and GraphPad Prism software (v7.0; GraphPad Software, Inc., San Diego, USA). Results are presented as mean ± standard error of the mean. Data that conformed to normal distributions were analyzed using one-way analysis of variance followed by Tukey’s post hoc test or two independent samples t-test for comparisons between the two groups as appropriate. P <0.05 was considered statistically significant.

Results

Gut microbiota involved in UA homeostasis

To clarify the function of gut microbiota in SUA levels, whether gut microbiota could directly affect UA metabolism, NC-Ab mice were given drinking water with antibiotic, and the NC mice drinking normal water as a control [Figure 1A]. After 2 weeks, the SUA levels of the NC-Ab group were significantly higher than the NC group (P <0.05) [Figure 1B]. For comparison, another group of mice received the antibiotic by intraperitoneal injection (NC-Ab.ip group). The results presented that the SUA levels of NC-Ab.ip group was comparable to those of control mice (NC group) [Figure 1C and D], suggesting that antibiotics work through the gut microbiota, rather than systemically. We further analyzed the gut microbiota of NC-Ab and NC groups by 16S rRNA gene sequencing, which revealed differences in alpha diversity between the two groups [Figure 1E]. Lactobacillus had a higher LDA score in the NC group compared to the NC-Ab group [Figure 1F]. MetagenomeSeq analysis confirmed reduced Lactobacillus richness in the NC-Ab group compared with that in the NC group [Figure 1G].

Figure 1.

Antibiotics elevated serum UA levels and changed the gut microbiota. (A) Diagram illustrating the procedure of oral antibiotics in normouricemic mice. (B) SUA levels in NC and NC-Ab groups at the end of the experiment (n = 6/group), data are shown as the mean ± SEM. *P <0.05, vs. NC group. (C) Diagram illustrating the procedure of intraperitoneal antibiotic administration in normouricemic mice. (D) SUA levels in control (NC) and intraperitoneal antibiotics (NC-Ab.ip.) groups at the end of the experiment (n = 5/group), data are shown as the mean ± SEM. (E) Oral antibiotics decreased the alpha diversity of gut microbiota. (F) A supervised comparison of the microbiota between the NC and NC-Ab groups by utilizing the LEfSe analysis. The log LDA score threshold is set to 3.0. (G) Manhattan plot between the NC and NC-Ab groups. *P <0.05, vs. NC group. ASV: Amplicon sequence variants; i.p.: Intraperitoneal injection; LDA: Linear discriminant analysis; NC: Control; NC-Ab: Control-antibiotic-fed; ns: Not significant; p.o.: Peros; SEM: Standard error of the mean; SUA: Serous uric acid; XOD: Xanthine oxidase.

Gut microbiota regulated SUA via gut purine metabolism rather than liver

Purine metabolism disorder is the key cause of HUA; we first detected the xanthine oxidase (XOD) activity in the intestine and found that the NC-Ab group had higher XOD activity than the NC group [Figure 2A]. Then, metabolomics analysis was used to detect the concentration of purines in feces and identified 1282 differential metabolites between NC-Ab and NC groups. Among these, 975 metabolites were downregulated, and 307 were upregulated in the NC-Ab group vs. the NC group [Figure 2B]. The NC-Ab group also exhibited lower levels of hypoxanthine, guanine, adenine, and allantoin compared with the NC group [Figure 2C–F]. Like the gut, the liver also participates in purine metabolism. We also measured the levels of purines in the liver; the data demonstrated that although different metabolites were found between the NC and NC-Ab groups, there were no significant differences in liver purine levels between the two groups [Figure 2G–N]. The results indicated that antibiotic treatment primarily affects intestinal purine metabolism rather than hepatic purine levels.

Figure 2.

Effects of antibiotic on purine metabolism. (A) XOD levels in NC and NC-Ab groups. (B) Significantly different metabolites in the gut between NC and NC-Ab groups. (C–F) Levels of hypoxanthine, guanine, adenine, and allantoin in the feces between NC and NC-Ab groups. (G) Significantly different metabolites in the liver between NC and NC-Ab groups. (H–N) Levels of inosine, adenosine, hypoxanthine, AMP, guanosine, IMP, and ADP in the liver between NC and NC-Ab groups. Data are shown as the mean ± SEM. *P <0.05, vs. NC group. ADP: Adenosine diphosphate; AMP: Adenosine monophosphate; IMP: Inosine monophosphate; NC: Control; NC-Ab: Control-antibiotic-fed; ns: Not significant; SEM: Standard error of the mean; XOD: Xanthine oxidase.

Gut microbiota and purine disturbances observed in HUA mice

An HUA mouse model was established by feeding a yeast-rich diet with intraperitoneal PO injection [Figure 3A]. The SUA concentration of the HUA group was significantly higher than the control group [Figure 3B], suggesting the successful establishment of the HUA model. We first detected fecal metabolites by nontargeted metabolomics; the quality control and assurance results suggested that the metabolomics data for fecal metabolism was available and trustworthy, as shown in Supplementary Figure 1A and B, http://links.lww.com/CM9/C422. OPLS-DA showed that the metabolites of the NC group were easily distinguished from those of the NC group [Supplementary Figure 2A, http://links.lww.com/CM9/C422]. The concentration of hypoxanthine, allantoin, 6-thioinosine-5′-monophosphate, and cyclic adenosine monophosphate was much lower in the HUA group, while the levels of guanine, deoxyguanosine, adenosine, and deoxyguanosine were higher than the control group [Supplementary Figure 2B–H, http://links.lww.com/CM9/C422].

Figure 3.

Dysbiosis gut microbiota in mice with hyperuricemia. (A) Diagram illustrating the experimental design. (B) Graph presenting the UA level in HUA (n = 7) and control (n = 6) groups every week, data are shown as the mean ± SEM. (C–G) 16S rRNA sequencing results obtained from the fresh feces showed the gut microbiota in the HUA and control groups. (C) Relative abundance of the intestinal microorganism at the phylum level. (D) Venn diagram comparing the HUA and control groups. (E) Manhattan plot based on MetagenomeSeq analysis of the HUA and control groups. (F) Unweighted UniFrac-based principal coordinate analysis of the HUA and control groups. (G) Graph shows a significant difference of ASVs between the HUA and control groups based on the MetagenomeSeq analysis. Log₂(FC) value of ASVs comparison between the HUA and control groups. Positive log₂(FC) value represented upregulation of ASVs in the control group compared with the HUA group, and negative log₂(FC) value represented upregulation of ASVs in the HUA group compared with the control group. *P <0.05 vs. control mice. ASV: Amplicon sequence variants; FC: Fold change; HUA: Hyperuricemia; LDA: Linear discriminant analysis; PO: Potassium oxonate; SEM: Standard error of the mean; UA: Uric acid.

Analysis of gut microbiota via 16S rRNA gene sequencing revealed notable alterations in the gut microbiota composition in the HUA group [Figure 3C]. The control and HUA groups accounted for 49.09% and 38.02% of the independent amplicon sequence variants (ASVs), respectively, with only 12.9% of all ASVs shared between the two groups [Figure 3D]. Unweighted UniFrac-based principal coordinate analysis (PCoA) revealed a clear separation between the microbiota compositions of the HUA and control groups [Figure 3F], indicating significant differences in their microbiota profiles.

As shown in Figure 1F, Lactobacillus had the highest LDA score after antibiotic treatment [Figure 1E]. Our previous study also found that Lactobacillus deficiency may be an important cause of HUA.^[19] Following this, we assessed the richness of microbiome species using MetagenomeSeq analysis, which revealed significant differences in the ASV/operating taxonomic unit levels between the HUA and NC groups [Figure 3E]. Twelve ASVs belonging to the Lactobacillus genus were enriched in the NC group, whereas seven ASVs from the same genus were enriched in the HUA group [Figure 3G]. Of the 12 ASVs in the NC group, eight belonged to the L. johnsonii species and three to the L. murinus species [Figure 3G]. In contrast, four out of the seven ASVs in the HUA group were of the L. intestinalis species, with the remaining ASVs belonging to L. taiwanensis, L. pontis, and L. sp. KC38 species [Figure 3G, Supplementary Table 1, http://links.lww.com/CM9/C422]. These data suggested the species of Lactobacillus was different in the HUA status versus normouricemia, and L. johnsonii and L. murinus were lacking in HUA mice.

Supplementation with L. johnsonii in HUA mice alleviated HUA

To clarify the function of L. johnsonii and L. murinus in HUA, we isolated an L. johnsonii strain from mice feces. The bacteria strain’s morphology was small, grayish, and raised, with alpha or gamma hemolytic rings [Figure 4A]. The cells were Gram-positive, blue, short, rod-like, rhabdoid, inerratic, single, in pairs, or stringing [Figure 4B]. We confirmed that the strain was L. Johnsonii using Brooke mass spectrometry [Figure 4C]. According to the phylogenetic analysis of 16S rRNA gene sequences, the strain was identical (100%) to the L. johnsonii strain G2A [Figure 4D]. The sequence identity of the strain with the eight ASVs of L. johnsonii was 91.74–99.07% [Supplementary Table 2, http://links.lww.com/CM9/C422], with the highest similarity to ASV-12459 (99.07%). We then colonized the L. johnsonii strain to HUA mice (HUA-Lac. group) [Figure 4E] and monitored serum UA concentrations. The data demonstrated that L. johnsonii supplementation resulted in a decreased serum UA level in the mice compared with that of the HUA mice who were fed with PBS (HUA group) [Figure 4F]. The UA levels were negatively correlated with the relative abundance of L. johnsonii [Supplementary Figure 2I, http://links.lww.com/CM9/C422]. We also found that L. johnsonii treatment reduced urea levels [Figure 4G and H] and alleviated mild structural kidney abnormalities, such as renal tubular dilatation [Figure 4I].

Figure 4.

The effect of Lactobacillus johnsonii (L. johnsonii) on HUA. (A) Morphology of L. johnsonii on a plate after 48 h of incubation at 37°C under anaerobic conditions (Original magnification ×1000). (B) Morphology of L. johnsonii under an electron microscope. (C) Identification of L. johnsonii via mass spectrometry MALDI-TOF. (D) L. johnsonii identified via 16S rRNA gene sequences and identification and percent identity using BLAST. The strain (1_2202545691G. seq. Contig1) was identical (100%) to the type strain of L. johnsonii strain G2A. (E) Diagram illustrating the procedure of L. johnsonii supplementation in HUA mice. (F) Serum UA concentration in the NC (n = 7), HUA (n = 6), and HUA-Lac. (n = 7) groups. (G) Serum creatinine level in the groups (n = 6). (H) Serum urea level in the groups. (I) Representative H&E staining of the renal tissue sections. Bar: 20 μm. *P <0.05. BLAST: Basic Local Alignment Search Tool; CREA: Serum creatinine; H&E: Hematoxylin and eosin; HUA: Hyperuricemia; HUA-Lac.: L. johnsonii-fed HUA mice; MALDI-TOF: Matrix-assisted laser desorption/ionization-time of flight mass; NC: Negative control; ns: Not significant; PO: Potassium oxonate; UA: Uric acid.

Conversely, a strain of L. murinus also was isolated and colonized to HUA mice. The data demonstrated that L. murinus supplementation could not lower SUA [Supplementary Figure 3, http://links.lww.com/CM9/C422]. These observations suggested that supplementation with L. Johnsonii, but not L. murinus, could alleviate HUA.

L. johnsonii inhibited XOD in HUA mice

XOD is a key enzyme that catalyzes the conversion of hypoxanthine and xanthine to UA^[20] [Figure 5A]. We detected the XOD activity in the three main organs involved in UA production to understand how L. johnsonii decreased serum UA. The results presented that the intestinal, renal, and hepatic XOD levels of the HUA groups were significantly higher [Figure 5B–D], suggesting increased UA synthesis was an initiator of HUA. Treatment with L. johnsonii markedly inhibited XOD activity in these organs [Figure 5B–D].

Figure 5.

Effect of Lactobacillus johnsonii (L. johnsonii) on XOD activity in mice with HUA. (A) Diagram of UA synthesis. (B–F) At the end of the experiment, the liver, kidney, and intestine were harvested to test the XOD activity and stain by H&E. (B) Hepatic XOD activity in the NC (n = 6), HUA (n = 5), and HUA-Lac. (n = 6) groups. (C) Renal XOD activity in the three groups. (D) Intestinal XOD activity in the three groups. (E) Representative H&E staining of the hepatic tissue sections. Bar: 20 μm. (F–H) Serum AST, ALT, and TG levels in the groups (n = 6). *P <0.05. ALT: Alanine transaminase; AST: Aspartate transaminase; H&E: Hematoxylin and eosin; HUA: Hyperuricemia; HUA-Lac.: L. johnsonii-fed HUA mice; TG: Triglyceride; XOD: Xanthine oxidase.

Additionally, levels of aspartate aminotransferase (ALT), alanine aminotransferase (AST), and triglyceride (TG) were decreased after L. johnsonii treatment [Figure 5F–H]. L. johnsonii treatment also reversed mild structural abnormalities in the liver, such as swollen hepatocytes with an irregular arrangement observed in the HUA group [Figure 5E].

L. johnsonii increased the excretion of purine metabolites from the intestine

OPLS-DA revealed that the metabolites of the HUA group and HUA-Lac. group were easily distinguished from those of the control group [Figure 6A], suggesting alterations in the metabolites of gut microbiota. We identified 3547 differential metabolites between the control and HUA groups and 1226 differential metabolites between the HUA and HUA-Lac. groups [Figure 6B]. The differential metabolites between groups are presented using a heat map based on hierarchical clustering [Figure 6C].

Figure 6.

Lactobacillus johnsonii (L. johnsonii) altered the metabolism of feces. (A) Graph of the OPLS-DA score (PC1). Y-axis represents the score of OC2 and X-axis represents the score of PC1. (B) Significantly different metabolites between the control and HUA groups and the HUA and HUA-Lac. groups. (C) Heat map of the different expressed metabolites (red: higher expression; blue: lower expression). (D–G) Concentration of hypoxanthine, allantoin, adenine, and guanine in the feces. (H–K) Concentration of nopaline, N5-phenyl-glutamine, beta-tyrosine, and phosphoserine in the feces. (L) Pathway enrichment analysis of differentially expressed metabolites in the three groups. (M) Network chart between the pathway and the metabolites. Blue dots represent pathway, and red dots represent the metabolites. n = 5 per group, *P <0.05. HUA: Hyperuricemia; HUA-Lac.: L. johnsonii-fed HUA mice; OC: Orthogonal component; OPLS-DA: Orthogonal projections to latent structure discriminant analysis; PC: Predictive component; ns: Not significant.

Hypoxanthine, adenine, and guanine were the degradation products of nucleotides that could be utilized as the material for UA synthesis, and allantoin was one of the degradation products of UA.^[21] We analyzed these metabolites in mouse feces and found the concentrations of hypoxanthine, allantoin, adenine, and guanine were much lower in mice with HUA compared with that in the control group [Figure 6D–G], indicating reduced excretion of UA precursors. Moreover, the lower concentration of allantoin indicated that the degradation of UA was also decreased in HUA. Comparatively, the concentrations of hypoxanthine, adenine, guanine, and allantoin were elevated after L. johnsonii supplementation with the increased excretion of UA synthetic material and UA degradation [Figure 6D–G].

Besides purine metabolism, amino acid metabolism also affected HUA.^[22] Processes enriched included phenylalanine metabolism, valine-leucine-isoleucine biosynthesis, beta-alanine, arginine-proline, and tryptophan metabolism by enrichment analysis [Figure 6L]. The expressions of nopaline, N5-phenyl-glutamine, beta-tyrosine, and phosphoserine were found to be decreased in the HUA group after assessing the concentration of the representative amino acid. However, these reductions were rectified by L. johnsonii supplementation [Figure 6H–K]. Network charts were used to present the pathway, and metabolites are shown in Figure 6M.

L. johnsonii increased intestinal UA excretion by increasing ABCG2 expression

Fecal and intestinal UA concentrations were much lower in the HUA group than in the control group [Figure 7A and B], although the colon seemed to have a normal architecture [Figure 7E]. Following treatment with L. johnsonii, the UA concentrations of the intestines and feces were considerably increased compared to that of the PBS-treated HUA group [Figure 7A and B], suggesting that UA excretion in the intestine was reduced in HUA, and L. Johnsonii supplementation could increase intestinal UA excretion.

Figure 7.

Lactobacillus johnsonii (L. johnsonii) increased intestinal UA excretion by increasing ABCG2 expression. (A) Intestinal UA concentration in the control, HUA, and HUA-Lac. groups. (B) Fecal UA level in the control, HUA, and HUA-Lac. groups. (C) Relative quantitative statistics on ABCG2 protein expression in the intestinal tissues. (D) Protein expression of ABCG2 in the intestinal tissues via immunohistochemistry and representative graphs of ABCG2 expression are presented. Bar: 20 μm. (E) Representative H&E staining of colonic sections. Bar: 20 μm. (F) Pathway enrichment analysis of differentially expressed metabolites via KEGG in control, HUA, and HUA-Lac. groups. *P <0.05. ABC: ATP-binding cassette; ABCG2: ATP-binding cassette transporter, subfamily G, member 2; CoA: Coenzyme A; GnRH: Gonadotropin-releasing hormone; HUA: Hyperuricemia; HUA-Lac.: L. johnsonii-fed HUA mice; H&E: Hematoxylin and eosin; ns: Not significant; UA: Uric acid.

To understand how L. Johnsonii increased intestinal UA excretion, we employed KEGG enrichment analysis and identified 10 metabolisms assigned to the adenosine triphosphate-binding cassette (ABC) transporters category [Figure 7F]. The protein expression of ABCG2, is the most important urate transporter. Our analysis showed that ABCG2 expression was considerably decreased in the HUA group compared with that in the control group [Figure 7C and D]. Moreover, L. johnsonii intervention markedly increased ABCG2 expression in the HUA-Lac. group [Figure 7C and D]. These results suggest that L. Johnsonii enhances intestinal UA excretion by increasing ABCG2 protein expression through metabolic pathways.

Butyrate increased ABCG2 expression by the Wnt5a/β-catenin pathway

Bile acids, indole, and SCFAs are the important metabolites of gut microbiota. We found that L. johnsonii did not affect the concentration of bile acids or indole [Figure 8A–F], suggesting minimal likelihood of L. Johnsonii regulating ABCG2 expression via bile acid or indole. However, UA levels were negatively correlated with butyrate concentration [Figure 8G], and butyric acid levels were lower in the HUA group compared to the control group [Figure 8H]. Moreover, L. johnsonii supplementation elevated butyric acid levels in the mice with HUA [Figure 8H], which was consistent with previous research in which L. johnsonii had been proven to produce high levels of butyric in vitro.^[23]

Figure 8.

Mechanism of lowering urate by Lactobacillus johnsonii (L. johnsonii). Concentrations of (A) deoxycholic acid, (B) taurohyodeoxycholate, (C) lithocholyltaurine, (D) taurochenodeoxycholic, (E) indole, and (F) 3-hydroxyanthranilic acid in the mouse feces. (G) Correlation analysis of serum UA and fecal butyrate. (H) Butyric acid concentration of the feces from the control, HUA, and HUA-Lac. groups. (I) ABCG2 expression mediated by sodium butyrate in NCM460 cells for 72 h, and representative graphs of ABCG2 expression are shown. (J) Relative quantitative statistics of ABCG2 protein expression in NCM460 cells. (K) Wnt5a/b5a/b, and β-catenin expression mediated by sodium butyrate in NCM460 cells for 72 h, and representative graphs are shown. (L–N) Relative quantitative statistics of Wnt5a/b, and β-catenin protein expression in NCM460 cells. *P <0.05 vs. NaB 0 mmol/L. All cell experiments were performed at least in triplicate. ABCG2: ATP-binding cassette transporter, subfamily G, member 2; HUA: Hyperuricemia; HUA-Lac.: L. johnsonii-fed HUA mice; NaB: Sodium butyrate; ns: Not significant; UA: Uric acid.

In human intestinal NCM460 cells treated with sodium butyrate, ABCG2 expression increased at concentrations of sodium butyrate above 1 mmol/L [Figure 8I and J]. Furthermore, higher concentrations of sodium butyrate led to decreased protein expression of Wnt5a/b and β-catenin [Figure 8K–N]. When the concentration of sodium butyrate was 4 mmol/L, the expression of ABCG2 was upregulated compared with that in the control [Figure 8J], while that of Wnt5a/b and β-catenin was downregulated [Figure 8M]. Therefore, L. johnsonii increased the concentration of butyric acid, which may promote the protein expression of ABCG2 in the intestine by inhibiting the Wnt5a/b/β-catenin signal pathway.

Effect of butyric acid in HUA mice

In vitro, we observed that butyric acid could mediate ABCG2 expression. We investigated the direct effects of butyric acid on HUA mice [Figure 9A]. Similar to the effect produced by L. johnsonii, butyric acid supplementation resulted in reduced serum UA levels while maintaining normal colonic structure [Figure 9B and C], suggesting that butyric acid can directly down-regulate UA levels. Additionally, the expression of ABCG2 and intestinal UA excretion were elevated [Figure 9E, F, and D, respectively].

Figure 9.

Effect of butyric acid on mice with HUA. (A) Diagram illustrating the procedure of butyric acid intervention in the mice with HUA. (B) Serum UA concentration in the NC, HUA, and HUA-NaB groups (n = 5 per group). (C) Representative H&E staining of colon sections in groups. Bar: 20 μm. (D) Fecal UA level in the three groups. (E) Protein expression of ABCG2 in mouse intestinal tissues via immunohistochemistry and representative graphs of ABCG2 expression are presented. Bar: 20 μm. (F) Relative quantitative statistics on ABCG2 protein expression in mouse intestinal tissues. (G–S) Nontargeted metabolomics detected the concentration of fecal metabolites in NC (n = 5), HUA (n = 5), HUA-NaB (n = 4) groups. Relative concentration of hypoxanthine (G), guanine (H), adenosine (I), adenine (J), allantoin (K), and inosine (L) in the feces. (M) Heat map of the different expressed metabolites. (N) Pathway enrichment analysis of differentially expressed metabolites in the three groups. Relative concentration of N5-phenyl-glutamine (O), phosphoserine (P), nopaline (Q), and beta-tyrosine (R), in the feces. (S) Network chart between the pathway and metabolites. *P <0.05. ABCG2: ATP-binding cassette transporter, subfamily G, member 2; HUA: Hyperuricemia; HUA-Lac.: L. johnsonii-fed HUA mice; HUA-NaB: Butyrate-fed HUA mice; H&E: Hematoxylin and eosin; NC: Control; ns: Not significant; UA: Uric acid.

Butyric acid supplementation also significantly elevated the excretion of hypoxanthine, adenine, guanine, and allantoin in the HUA-NaB group compared to that in the HUA group, which were consistent with the effects observed in the HUA-Lac. group [Figure 9G–L]. Heat maps [Figure 9M], metabolic pathways [Figure 7N], amino acid metabolism [Figure 9O–R], and network charts [Figure 9S] for butyric acid treatment groups (control, HUA, and HUA-NaB group) were similar to that of the L. johnsonii treatment groups (control, HUA, and HUA-Lac. group), indicating that butyric acid has a similar effect on HUA as L. johnsonii.

Discussion

This study investigated the role of the gut and its microbiome in the development of HUA. Although the imbalance of gut microbiota in patients with HUA and gout has been reported.^[24] Little research has been done on the function and underlying mechanism of gut microbiota in HUA. In this research, we found that HUA is associated with gut microbiome dysbiosis, specifically a significant reduction in L. johnsonii abundance. This dysbiosis leads to lower intestinal UA, purine excretion, and decreased butyrate level, all of which can be reversed by L. johnsonii supplementation. Antibiotic treatment further confirmed that the gut microbiome can directly influence serum UA via gut pathways.

Two key factors contribute to HUA–heritability^[25] and environmental^[26] influences (such as diet and gut microbiota)—both of which have been widely studied. However, most of these were correlation researches. For example, a study found that the relative abundance of Escherichia coli and Bacteroides is higher in patients with HUA, as a control, the number of probiotics such as Butyricicoccus and Bifidobacterium is higher in that of healthy people.^[7] Fecal microflora analysis of HUA mice also showed that the Lactobacillus was significantly lower than that of normal mice.^[27] Our study found that diet-induced HUA model mice had a lower abundance of L. johnsonii, and L. Johnsonii supplementation could reduce the serum UA in diet-induced HUA mice. According to Koch’s postulates, a set of scientific methods for identifying causative agents in infectious diseases,^[28] our data support that diet-induced HUA is influenced by gut microbiota. L. johnsonii was deficient in HUA mice, and its supplementation decreased their serum UA levels. To further support this conclusion, we disturbed the gut microbiota with antibiotics, which elevated serum UA levels and reduced the abundance of L. johnsonii.

Lactobacillus species are widely used probiotics,^[29,30] and species such as L. plantarum^[31] and L. brevis^[32] have been shown to alleviate HUA. L. johnsonii, which has been identified as a predominant bacteria in human intestinal biopsies, is thought to be beneficial to general health,^[33] including immunomodulation,^[34] pathogen inhibition, and cholesterol reduction. Our findings challenge the assumption that L. johnsonii universally benefits HUA. From our findings, L. johnsonii could reduce the serum UA of HUA mice. These results suggest that the treatment of HUA by gut microbiota is efficient.

Purine metabolism is the most important metabolic pathway in HUA, and dysbiosis of purine metabolism is a causal factor of HUA.^[35] While previous studies on purine metabolism have focused mainly on the liver, which is the primary organ for UA synthesis,^[36] few studies have explored purine metabolism in the intestine. Gastrointestinal health is a significant economic burden associated with digestive and other system diseases.^[37] The gut is responsible for the intake of essential substances, including proteins, carbohydrates, and purines. Elevated serum UA can result from excessive intake of high-purine foods (exogenous sources) or decreased excretion of purine metabolites (endogenous sources).^[38] Our study highlights the role of the intestine in purine metabolism during HUA, particularly regarding the excretion of purine metabolites and XOD activity. We found that gut microbiota directly regulated intestinal purine metabolism. For instance, L. johnsonii increased the concentration of purine metabolites in the feces of HUA mice, whereas antibiotic treatment reduced gut purine levels but did not affect liver purine levels. This suggests that gut microbiota interventions can lower SUA levels by increasing intestinal excretion of purine metabolites, making dysregulation of intestinal purine metabolism a significant factor in diet-induced HUA. While elevated hepatic XOD activity is a known cause of HUA, conventional urate-lowering drugs often target hepatic XOD. Our study introduces an innovative approach: gut microbiota regulates serum UA by promoting intestinal purine excretion without altering hepatic purine levels. This approach contrasts with XOD inhibitors like febuxostat, which have been reported to cause hepatic injury, evidenced by elevated ALT and AST levels. In contrast, L. johnsonii supplementation not only reduced serum UA but also showed hepatoprotective effects by reducing ALT and AST levels. Therefore, promoting intestinal purine excretion may be a new HUA therapeutic target.

Our study also found that amino acid metabolism is related to HUA. Specifically, phenylalanine and tryptophan biosynthesis pathways were enriched in HUA mice, consistent with findings from two independent cohort studies.^[8,12] L. johnsonii supplementation corrected reductions in representative amino acid metabolites in HUA mice. This suggests that disorders of amino acid metabolism in HUA can be corrected by L. johnsonii, although the mechanism is unclear and warrants further study.

Many patients with HUA experience renal insufficiencies such as chronic kidney disease, hypertension, and diabetes,^[39] which limit the use of many urate-lowering drugs.^[40] Given the strong repair capacity of the intestinal mucosal epithelium,^[41] targeting the gut for the treatment of HUA, such as by enhancing intestinal UA excretion, may be a feasible strategy. Our findings indicate that gut microbiota treatment can regulate intestinal UA excretion and protect kidney function.

ABCG2 is a high-capacity urate transporter located in the apical membrane of the intestine.^[42] It plays an independent role in intestinal urate excretion, and dysfunction of ABCG2 results in increased serum UA concentration.^[43,44] A previous study showed that UA content was reduced by 75.5% in oocytes that expressed ABCG2, and ABCG2 gene knockout mice had elevated serum UA concentrations in an in vivo study.^[45] Additionally, it has been reported that L. plantarum Q7 and L. paracasei X11 can upregulate ABCG2 expression in the kidney.^[46,47] Our study demonstrates that ABCG2 is downregulated in the intestines of HUA mice, but L. johnsonii can enhance UA excretion through the intestine by upregulating ABCG2 expression.

Although it is still unclear how L. johnsonii regulates ABCG2 expression in the intestine, our KEGG enrichment analysis of nontargeted metabolomics indicates that the ABC transporters category. This suggests that L. johnsonii may regulate ABCG2 through certain metabolites. We found that tryptophan metabolism was enriched, while the level of tryptophan metabolites, such as indole, did not significantly differ between the HUA and HUA-Lac. groups, indicating that L. johnsonii does not regulate ABCG2 through tryptophan metabolites. Steroid hormone biosynthesis, including bile acids, was also enriched. Bile acids play roles in energy,^[48], lipid,^[49] and cholesterol metabolism,^[50] as well as regulating insulin resistance, diabetes,^[51] and non-alcoholic fatty liver disease.^[52,53] Importantly, bile acids modulate ABCG2 activity.^[54] We assessed the level of these metabolites in the feces samples and found that bile acids were significantly different in HUA and control mice, while the bile acid level in feces did not normalize with L. johnsonii treatment. This suggests that bile acids are not the pathway through which L. johnsonii regulates ABCG2.

SCFAs, as indispensable products of gut microbiota,^[55] are molecules connecting the host and gut microorganisms.^[56] In particular, butyric acid is the most important member of the SCFAs,^[57] as it provides an energy source for enterocytes^[58] and maintains intestinal homeostasis.^[59] ABCG2 consists of an ATP-hydrolyzing nucleotide-binding domain, which provides energy for the transport process, and a transmembrane domain that binds and transports UA.^[60] From the protein structure of ABCG2, energy (ATP) is necessary. Butyric acid undergoes a sequence of biochemical reactions and ultimately regenerates ATP,^[61] which can be used by ABCG2; thus, butyrate may be closely related to ABCG2. We found that butyrate could upregulate the expression of ABCG2 in vitro, consistent with previous studies, although those studies typically focused on the drug resistance of ABCG2 rather than UA excretion.^[62] Moreover, it had been proven that L. johnsonii could produce high levels of butyrate in vitro,^[23] which was similar to our study in that L. johnsonii treatment resulted in increased fecal butyric acid levels. We also observed a negative correlation between the concentration of butyric acid and serum UA concentration. Therefore, we postulated that butyric acid may be an important metabolite involved in the regulation of UA by L. johnsonii. This hypothesis was verified by in vitro experiments, in which butyric acid increased ABCG2 expression and inhibited the Wnt5a/b signal pathway. In vivo, butyrate enhanced the intestinal excretion of UA by upregulating ABCG2 and regulating purine metabolites, as well as suppressing the synthesis of UA by inhibiting XOD activity, which was comparable to L. johnsonii intervention. Therefore, L. johnsonii-related butyric acid may have the potential to be another target for the treatment of HUA. While this study also had several limitations. The short study duration may not capture long-term effects or potential delayed responses to interventions. Using mouse models may not fully represent human physiology or the complex interactions between gut microbiota and HUA, and the validation in the population cohort is needed. The study may not account for variations in dietary intake among subjects, which could influence HUA and microbiota composition.

Our study demonstrates a potential mechanism by which gut microbiota affects HUA. Microbiome modification can effectively ameliorate HUA. This research suggests that targeting gut microbiota and its metabolites could be a viable strategy for treating HUA, although further validation in clinical HUA cohorts is needed.

Acknowledgments

We thank the Institute of Metabolic Diseases for providing the UOX knockout heterozygous (+/–) C57BL/6J mice. We also thank Editage for providing professional writing services.

Funding

This work was supported by grants from the Sichuan Province Science and Technology Support Program (No. 2024JDRC0042), the Health Commission of Sichuan Province Medical Science and Technology Program (No. 24CXTD12), the National Natural Science Foundation of China (No. 81970461), and the Key Science and Technology Program of Zigong (No. 2024-YCY-01-02).

Conflicts of interest

None.

Availability of data and materials

The microbiota raw data supporting the conclusions of this article are available in the NCBI Sequence Read Archive (SRA) repository under accession number PRJNA898992 (available on November 8, 2022).

Supplementary Material

cm9-139-118-s001.pdf ^{(1.1MB, pdf)}