A stable model of chronic hyperuricemia was established.
During the 12 weeks modeling period, blood was collected to assess serum UA levels in mice. Figure 1 shows that mice in the model group exhibited significantly higher serum UA levels than in the control group at week 4 (p < 0.001). The model maintained elevated UA levels (p < 0.001) throughout the experiment, stabilizing the serum UA level at approximately 200 mol/L. These findings provide for the successful establishment of a high-UA model.
16S rRNA gene sequencing results
Overall, 15,118,792 raw reads were obtained from 120 fecal samples, averaging 125,990 raw reads per sample, with an average read length of 450 bp. After undergoing quality filtering, denoising, and chimera removal, the accumulated number of ASVs was 1,623,645. This ultimately resulted in 309,02 ASVs, averaging 2,575 ASVs per sample. The rarefaction curves showed a gradual stabilization of the observed species number, suggesting that the sequencing data were reliable and that the sample had a uniform species composition. These findings sufficiently reflect the diversity of the sample, allowing for subsequent analyses to be performed.
High uric acid causes changes in the gastrointestinal tract (GIT) microbial diversity.
Figure 2A shows that the continuous induction of a high UA model significantly reduced the Shannon diversity index of the rat gastrointestinal tract flora at the level of the entire intestinal segment (p < 0.01). Using Bray–Curtis, beta diversity analysis of the whole intestinal flora showed a significant difference between the two groups, despite a small separation between their clusters (p < 0.05) (Fig. 3A). Further analysis of the changes in intestinal flora alpha diversity in different intestinal segments revealed that the Shannon diversity indices of the cecal and colonic microbiota were reduced in the model group than in the control group (p < 0.001) (Fig. 2E, F). No significant differences were observed in the Shannon diversity index of the microbial flora between the two groups in the duodenum, jejunum, and ileum (Fig. 2B, C, D). However, in the subsequent PCoA analysis, the cecum and colon samples from the model group were clearly separated from those of the control group (p < 0.05) (Fig. 3E, F).
Predominant Bacteria with Classification from Phylum to Genus Revealed in the GIT of the high uric acid-induced rats.
Based on the diverse results obtained, we focused our analysis on the composition of the intestinal flora in the colon and cecum. Figure 4 and Fig. S5 show the results of microbiological analysis of the cecum are shown in Fig. 4 and Fig. S5. Approximately 99.77% of the ASVs from the cecum were accurately classified into 13 phyla and 197 genera. Figure 4 shows three phyla and 25 genera with relative abundances > 1%. In contrast to the previous intestinal segments (supplementary results), the top three dominant phyla in normal and hyperuricemic rats were Firmicutes, Bacteroidota, and Actinobacteriota. Feeding the rats with potassium oxonate resulted in limited changes in the cecal microflora composition. Compared with the ileum (supplementary results), the following changes occurred in the cecum. Firmicutes increased from 54.28–73.86%, Bacteroidota increased from 1.76–22.67%, while Actinobacteriota (from 30.64–2.00%), and Fusobacteriota (from 7.06–0%) decreased most significantly. In the cecum of healthy rats, the main genera observed at the genus level were Lachnospiraceae_undentified (17.32%, 6.58–26.17%), Prevotellaceae_UCG-003 (6.90%, 0–52.58%), and Lachnospiraceae_NK4A136_group (6.83%, 1.33–19.27%). However, in rats from the HUA group, the abundance of Prevotella (from 0.52–9.83%) and Romboutsia (from 4.71–8.28%) increased, along with Lachnospiraceae_undentified (24.18%, 0.82–55.93%), forming the three dominant genera. Additionally, we observed an increase in the abundance of Prevotella, Lactobacillus (from 0.61–2.12%), and Romboutsia compared to the control. Conversely, in the model group, there was a significant decrease in the abundance of Muribaculaceae (from 6.27–2.21%), Rikenellaceae_RC9_gut_group (from 3.28–0.69%), and Bacteroides (from 2.16–0.71%). Compared to the model group, the following changes in the hindgut flora were observed in the control group (Supplementary Results): a significant decrease in the relative abundances of Romboutsia (from 50.40–8.28%), whereas Prevotella (from 0.47–9.83%), Prevotellaceae_UCG-003 (from 0.19–5.70%), and Lachnospiraceae_NK4A136_group (from 0.61–4.66%) increased significantly. Further, while the advantages at the phylum level were essentially the same, LEfSe analysis at the genus level revealed three genera that had a greater impact on the differences between the two groups: Lactobacillus, Eubacterium nodatum, and Blautia.
Based on the results presented in Fig. 4 and Fig. S5, the composition of the colonic flora was as follows: Overall, 98.43% of ASVs were classified into 17 phyla and 237 genera, as the figure shows three phyla and 24 genera. Firmicutes, Bacteroidota, and Actinobacteriota at the phylum level were the dominant phyla in the normal and hyperuricemic rat groups. Notably, the abundance of Bacteroidota (from 30.38–33.92%) in the high UA group was significantly higher than that in the healthy group. However, the other phyla that exhibited change were mainly low-abundance phyla. Bacteroidota (from 22.67–33.92%) and Actinobacteriota (from 2.00–2.66%) in the ileum of the high UA group were significantly increased (Supplementary results), while the Firmicutes (from 73.86–61.84%) were significantly reduced than in the cecum. At the genus level, the dominant genera in the HUA group were Lachnospiraceae_undentified (14.95%, 2.85–45.86%), Prevotella (14.55%, 0.25–66.29%), and Alloprevotella (7.38%, 0.12–51.26%). This was in contrast to the dominant genera observed in the healthy group, which were Lachnospiraceae_undentified (13.19%, 2.70–29.80%), Prevotellaceae_UCG-003 (7.24%, 0.27–50.76%), and Muribaculaceae (6.51%, 0.92–9.75%). We observed a significant decrease in Prevotellaceae_UCG-003 (from 7.24–4.91%), Muribaculaceae (from 6.51–3.40%), Bacteroides (from 4.51–0.86%), Rikenellaceae_RC9_gut_group (from 3.48–0.68%), Lachnospiraceae_NK4A136_group (from 4.38–2.76%), and a significant increase in Lactobacillus (from 0.80–2.40%) and Blautia (from 1.78–3.38%) in model group than in the control group. In addition, Lactobacillus, Eubacteriumnodatum, and FamilyXIIIAD3011 were the top three genera significantly enriched in the HUA group and had a greater impact on the differences between the two groups.
Interaction effect of each intestinal segment with key strains
Figure 5A-D depicts the microbial patterns observed in the different small intestinal segments (duodenum, jejunum, and ileum), which exhibited similar characteristics. In the control group, the indicator species belonged to the phyla Firmicutes and Actinobacteria, while in the model group, the indicator species were Firmicutes, Actinobacteria, and Fusobacteria. The indicator species in the colonic segment was simpler in structure than in the small intestine, with the majority belonging to Firmicutes in the control group and Firmicutes, Bacteroidota, and Actinobacteria in the model group. Notably, no shared indicator species existed in the duodenum, jejunum, ileum, or colon. The number of indicator species varied significantly across the intestinal segments and groups, indicating unique flora characteristics specific to each segment.
Based on the results of RDA (Fig. 5E-5H), we identified significant differences in the key bacterial genera obtained through screening and clustering of the indicated species in different intestinal segments. In the duodenum, the key genera in the control group were Candidatus_Sacharimonas and Chloroplast, while, in the model group, Enteractinococcus and Fusobacterium were the key genera. The control group exhibited key genera in the ileum, including Candidatus_Sacharimonas, Blautia, and Enteractinococcus, whereas the model group had Gemella and Rothia. In the jejunum, the key genera were Enteractinococcus in the control group and Candidatus_Sacharimonas and Chloroplast in the model group. Romboutsia and Fusobacterium, unlike the small intestinal segments, only key genera in the control group namely, Clostridia_UCG-014, Lactobacillus, and [Eubacterium]nodatumgroup, were present in the colon. Finally, we present the key species screened from each intestinal segment in the whole intestine, which were present in a co-occurrence network of the whole intestine (Fig. 6I-L), and their respective spatial distribution characteristics were identified.
RDA was used to rank the indicator species and samples of each intestinal segment. The interaction of the intestinal flora is very important for the stability of healthy biological communities. According to the results of RDA (Fig. 5E-H), we found significant differences in the key bacterial genera derived from the screening and clustering of indicator species across different intestinal segments. In the duodenum, the key genera in the control group were Candidatus_Sacharimonas and Chloroplast, while in the model group, Enteractinococcus and Fusobacterium were the key genera. In the ileum, the key genera in the control group were Candidatus_Sacharimonas, Blautia, and Enteractinococcus, while in the model group, Gemella and Rothia were the key genera. Enteractinococcus was the key genera in the jejunum, while Candidatus_Sacharimonas and Chloroplast were the key in the model group. Romboutsia and Fusobacterium, unlike the small intestinal segments, only key genera in the control group were present in the colon: Clostridia_UCG-014, Lactobacillus, and [Eubacterium]nodatumgroup. Lactobacillus exhibited a negative correlation with high UA group samples, and the attribute values of each sample were similar. Finally, we present the screened key species identified from each intestinal segment, which were present in a co-occurrence network of the whole intestine (Fig. 5I-L), and their respective spatial distribution characteristics were found to exist.
Lactobacillus johnsonii YH1136 reduces uric acid in rats with chronic hyperuricemia induced by Potassium oxyzincate.
Figure 7 depicts measuring blood UA levels in rats at different time points. From the fourth week onward, the model group exhibited a significantly higher level of UA than the control group (p < 0.001). YH1136 treatment group exhibited significantly reduced serum UA levels in rats than in the control group (p < 0.001). Furthermore, no significant difference was observed in UA levels between the YH1136 and control groups (p > 0.05). This positive effect of YH1136 persisted throughout the experimental duration.
YH1136 can reduce uric acid production by the liver and improve liver pathology in hyperuricemia.
YH1136 administration reduced XOD production by the liver (Fig. 8). The mRNA levels were significantly higher in the model group than in the control (p < 0.001) and YH1136 groups (p < 0.001). In contrast, the YH1136 group exhibited significantly lower mRNA levels of hepatic XOD than those in the control group (p < 0. 01) (Fig. 8A). Hepatic XOD activity in the model group was significantly higher than that in the control group (p < 0.05) and YH1136 groups (p < 0.01), while no significant difference was observed between the control and YH1136 groups (p > 0.05) (Fig. 8B). A similar trend was also observed in the serum XOD activity (Fig. 8C).
In addition, the liver tissues collected were subjected to HE staining, revealing that HUA induced severe hepatic steatosis with significant granular degeneration in rats, resulting in liver damage. In contrast, YH1136 treatment significantly reduced liver damage. These findings reveal the alleviating effect of YH1136 on HUA and suggest its potential therapeutic value for fatty liver disease associated with hyperuricemia (Fig. 8D).
YH1136 promotes kidney uric acid excretion and reduces kidney uric acid accumulation.
In the kidneys, no significant differences were observed in XOD mRNA expression among the groups (p > 0.05)( Fig. 9A). In addition, we detected a series of UA transporters in the kidneys. The mRNA expression of the GLUT9 (p < 0.01), OAT1 (p < 0.001), and OAT4(p < 0.05) transporters was significantly increased in the model group than in the control group. However, no significant differences were observed between the YH1136 and model groups (p > 0.05) (Fig. 9C-E). Figure 9B shows that the mRNA level of the ABCG2 transporter in the model group was significantly lower than that in the control group (p < 0.05), whereas the level of ABCG2 in the YH1136-treated group was significantly higher than that in the model group (p < 0.01).
However, it was not significantly different from that in the control group (p > 0.05). The immunohistochemical results suggested the same alteration at the protein level, whereas YH1136 reversed the above manifestation, increasing ABCG2 expression (Fig. 10).
YH1136 alleviates kidney inflammation and reduces hyperuricemic kidney injury
Since previous results showed a reduction in renal UA accumulation in the YH1136 group, we further investigated the modulatory effect of YH1136 on hyperuricemic nephropathy. First, serum BUN and CRE levels were significantly elevated in the model group (p < 0.01) (Fig. 11A-B), indicating impaired kidney function. In the YH1136 group, the above two indicators were not significantly different with the control group (p > 0.05) but were significantly lower than those in the model group (p < 0.05, p < 0.01) (Fig. 10A-B). We then detected the common inflammatory factors. The model group exhibited significant increases in IL-1β and IL-6 in the kidney than in the control group (p < 0.01 and p < 0.05). However, no significant difference was observed between the control and YH1136 groups (p > 0.05). These results were consistent with mRNA and were also observed with protein content level (Fig. 10C-F). In the kidney, the mRNA (p < 0.01) and protein levels (p < 0.001) of TNF-α were significantly higher in the model group than in the control group. The YH1136 group exhibited a significant decrease in elevated TNF-α levels due to HUA (p < 0.01) but still differed from the control group (p < 0.05). At the mRNA level, YH1136 reduced the expression of TNF-α to a lower level below that in control (p < 0.05) ( Fig. 10G-H). The mRNA level of the anti-inflammatory factor IL-10 was significantly suppressed in the model group than in the control group, whereas the mRNA level of the pro-inflammatory factor IL-18 was significantly elevated in the model group (p < 0.05). However, YH1136 did not show any significant regulatory effects on these two indices (p > 0.05) (Fig. 10I-J).
We also evaluated renal oxidative stress injury. GSH was significantly elevated in the model group than in the control group (p < 0.001), whereas the YH1136 group effectively alleviated this change (p < 0.05) (Fig. 10K). Furthermore, MDA levels were significantly reduced in the YH1136 group than in the model group (p < 0.05) and did not differ significantly from those in the control group (p > 0.05) (Fig. 10L).
HE staining showed that HUA could cause glomerular atrophy and massive interstitial congestion (Fig. 10A). Masson’s staining revealed severe interstitial fibrosis in the model group (Fig. 10B). However, after YH1136 administration, the lesions were significantly reduced (Fig. 10A, B). These findings suggested that the kidney plays a crucial role in the therapeutic effect of YH1136 on hyperuricemia.