Gut microbiota and uric acid excretion signaling in pathogenesis of hyperuricemia and acute gouty arthritis

Summary

Hyperuricemia (HUA) and acute gouty arthritis (AGA) represent distinct pathological stages of gout, with the intestine playing a critical role in uric acid (UA) excretion. By establishing HUA and AGA mouse models, we observed both induced renal and intestinal pathology, elevated inflammatory factors, reduced expression of barrier proteins (Occludin, Claudin-1, and Zonula occludens-1 [ZO-1]), increased permeability markers (DAO and D-LA), upregulation of GLUT9, downregulation of ABCG2, and more severe pathology in AGA. 16S rRNA sequencing revealed alterations in gut microbial composition, with 11 bacterial genera overlapping between the 2, including g_[Eubacterium]_siraeum_group, g_Harryflintia, g_Ruminococcus, etc. Application of gut microbiota from two models to intestinal epithelial cells (IECs) confirmed that these microbiota exacerbated damage in HUA/NCM460 and AGA/NCM460 cells. RNA sequencing identified the ErbB3/PI3K/AKT pathway as significantly activated in HUA, and the ADRB2/cAMP/PKA/CREB pathway in AGA, which were further validated in vivo. This study identifies specific gut microbiota in HUA/AGA and elucidates core pathways involved in pathogenesis, providing new insights into gut-targeted management strategies.

Graphical abstract

Highlights

• •HUA and AGA exhibit specific intestinal dysfunction and impaired uric acid excretion
• •Eleven overlapping gut microbiota were identified in both HUA and AGA conditions
• •Gut microbiota from HUA/AGA models exacerbate damage to intestinal epithelial cells
• •ErbB3-PI3K-AKT and ADRB2-cAMP-PKA-CREB are key pathways in HUA and AGA, respectively

Medical microbiology

Introduction

In recent years, rising living standards and dietary shifts have contributed to a gout prevalence reaching 6.8% in certain regions, with a continuing upward trend and a younger age of onset.^1,2 Clinically, gout progresses through four distinct pathological stages: hyperuricemia (HUA), monosodium urate (MSU) crystal deposition, MSU-induced acute gouty arthritis (AGA), and chronic tophaceous gout characterized by tophi formation.^3,4

HUA is the primary risk factor for AGA. Elevated serum uric acid (UA) concentrations can lead to a supersaturated state, resulting in MSU crystal deposition in joint cartilage and surrounding tissues.⁵ Persistent MSU accumulation induces infiltration of inflammatory cells, such as neutrophils, which secrete pro-inflammatory cytokines, perpetuating the inflammatory response and ultimately triggering AGA.⁶ Notably, chronic gout may lead to lifelong disability.⁷

The intestine plays a pivotal role in purine catabolism and UA metabolism, with over 30%–40% of UA excreted through the gut.⁸ Mounting evidence suggests that structural changes in gut microbiota are a significant contributor to elevated UA levels, and intestinal inflammatory damage is closely associated with gout flares.^9,10 Studies have shown that HUA rats exhibit reduced levels of beneficial gut bacteria such as Lactobacillus and Streptococcus, which are key bacteria involved in purine absorption and UA breakdown.¹¹ Conversely, during HUA progression, the increase in UA levels and impairment of intestinal barrier function correlate with an increase in pathogenic bacteria such as Oscillibacter and Ruminiclostridium.¹² Dysbiosis enables pathogenic bacteria to colonize the intestinal mechanical barrier, altering the expression of tight junction proteins such as Claudin-1, Occludin, and Zonula occludens-1 (ZO-1), thereby compromising epithelial cell integrity and reducing UA excretion.¹³ Concurrently, disruption of the intestinal mechanical barrier allows bacterial endotoxins, such as lipopolysaccharide, to enter the bloodstream, reaching the kidneys and inhibiting renal tubular epithelial cell proliferation while promoting apoptosis, leading to tubular damage, impaired UA excretion, and gout progression.¹⁴ Thus, modulating gut microbiota and maintaining intestinal barrier homeostasis may be essential for treating HUA and preventing AGA.

Phosphatidylinositol 3-kinase (PI3K) and protein kinase B (AKT) are upstream regulators of the NF-κB signaling pathway and play a key role in the activation and release of various inflammatory factors.¹⁵ Studies have shown that elevated UA levels can bind to cell membrane receptors and interact with them, thereby triggering the activation of PI3K and AKT, leading to oxidative stress, mitochondrial apoptosis, and renal inflammatory damage in HUA mice.^16,17 Research has demonstrated that inhibiting the PI3K-AKT signaling pathway can reduce UA levels and protect the kidneys.¹⁸ Cyclic adenosine monophosphate (cAMP) has been reported to be a potential regulator of inflammatory responses, participating in various cellular processes and closely associated with AGA.¹⁹ The adrenergic receptor ADRB2 can activate the cAMP/PKA signaling pathway. Increased cAMP further activates protein kinase A (PKA) to phosphorylate the transcription factor cAMP response element-binding protein (CREB), which is responsible for trans-activating its target genes, thereby promoting disease progression.²⁰

In the era of precision medicine, understanding stage-specific pathological mechanisms is crucial for developing targeted therapies for gout. Despite growing evidence linking gut microbiota dysbiosis to HUA and gout, several critical research gaps remain. First, most studies have focused on either HUA or AGA in isolation, leaving the dynamic changes in gut microbiota and intestinal barrier function throughout the progression from HUA to AGA poorly understood. Second, while certain bacterial taxa have been associated with UA metabolism, the specific genera that are consistently altered in both HUA and AGA, and their functional impacts on intestinal epithelial cells (IECs), remain unidentified. Third, the signaling pathways through which gut microbiota influence UA excretion and intestinal barrier integrity in different stages of gout are not well elucidated. In particular, the roles of the ErbB3-PI3K-AKT and ADRB2-cAMP-PKA-CREB pathways in gut-mediated UA metabolism have not been explored in the context of stage-specific gout pathology. Addressing these gaps is essential for developing targeted interventions that can prevent disease progression. Therefore, this study aims to elucidate both the commonalities and differences in gut microbiota profiles before and after gout onset, as well as the changes in key signaling pathways for UA excretion in IECs. These findings will enhance understanding of the molecular mechanisms underlying different gout stages, thereby guiding clinical prevention and treatment strategies.

Results

Changes in serum UA and kidney function in HUA and AGA mouse models

We successfully established HUA and AGA mouse models (Figure 1A). The pathological changes in serum UA and kidney function in different disease model states were assessed. Under HUA and AGA conditions, the levels of serum UA, XOD, and ADA in mice were all elevated (p < 0.001 vs. the control group) (Figure 1B). Histopathology of kidney tissues showed that both the HUA and AGA groups of mice exhibited glomerular sclerosis and interstitial inflammation (Figure 1C). HUA and AGA also led to increased levels of renal injury markers creatinine (CRE) and blood urea nitrogen (BUN) (p < 0.001 vs. the control group), with a greater increase in BUN in the AGA group (p < 0.001 vs. the HUA group) (Figure 1D). To assess whether the model causes liver injury that could affect UA metabolism, we measured serum ALT and AST levels as well as hepatic XOD activity. The results showed that hepatic purine metabolism flux was increased (elevated XOD) without significant liver damage in this model (Figure S1). Additionally, the levels of inflammatory factors were significantly increased in both the HUA and AGA groups (p < 0.001 vs. the control group), and the increase was more significant in the AGA group (p < 0.01 vs. the HUA group) (Figure 1E).

Figure 1.

Pathological changes in HUA and AGA mice

(A) The mouse experimental protocol.

(B) Serum UA, XOD, and ADA levels measured by ELISA (n = 4).

(C) Representative renal histopathology images from H&E staining (100× and 200×).

(D) Serum CRE and BUN levels across groups (n = 4).

(E) Inflammatory cytokine profiles (IL-1β, TNF-α, and IL-6) in serum measured by ELISA (n = 4).

Data are represented as mean ± SD. Statistical analysis was performed using Tukey’s test and one-way ANOVA: compared with the control group, ∗∗∗p < 0.001; compared with the HUA group, ∗∗p < 0.01 and ∗∗∗p < 0.001.

Changes in intestinal function and UA excretion under HUA and AGA conditions

Elevated UA levels have been demonstrated to exert deleterious effects on renal function, in addition to intestinal permeability and disruption of the intestinal barrier. This results in a reduction in the activity of UA efflux transporters and impaired intestinal UA excretion.²¹ Histopathological analysis revealed significant intestinal damage in mice with high UA levels, characterized by inflammatory cell infiltration, shortened/damaged villi, and thinned mucosa (Figure 2A). Concurrently, serum levels of intestinal permeability markers (DAO and D-LA) were elevated (p < 0.05 vs. the control group) (Figure 2B), while expression of key barrier proteins (Claudin-1, Occludin, and ZO-1) was reduced (p < 0.001 vs. the control group). The AGA group exhibited a heightened response to these pathological changes compared to the HUA group (p < 0.05). In addition, AGA significantly upregulated the expression of the intestinal UA absorption transporter GLUT9, while concomitantly downregulating the expression of the excretion transporter ABCG2 (p < 0.001 vs. the control group) (Figures 2C and 2D). Primer sequences for Claudin-1, Occludin, ZO-1, GLUT9 and ABCG2 are listed in Table 1.

Figure 2.

HUA and AGA cause intestinal damage and inhibit UA excretion

(A) H&E-stained jejunal tissue sections (100× and 200×).

(B) ELISA detection of serum DAO and D-LA levels (n = 4).

(C and D) mRNA and protein expression levels of intestinal barrier markers (Claudin-1, Occludin, and ZO-1) and UA transporters (ABCG2 and GLUT9) analyzed by qPCR and western blot (n = 4).

Data are represented as mean ± SD. Statistical analysis was performed using Tukey’s test and one-way ANOVA: compared to the control group, ∗p < 0.05 and ∗∗∗p < 0.001; compared to the HUA group, ∗p < 0.05 and ∗∗p < 0.01.

Table 1.

The primer sequences of mice

Genes	Primer sequence (5'→3')	Length (bp)
ABCG2	forward GTC AGG CTC TCT TCA CTG GC reverse TGG ATA GGG CCT TGG TGT TG	93 bp
GLUT9	forward CCT GAG TAT CGT GGG CAT TC reverse GCT GAG ATT GCT GGA AGA ACT	101 bp
Claudin-1	forward GTC AGG CTC TCT TCA CTG GC reverse TGG ATA GGG CCT TGG TGT TG	116 bp
Occludin	forward CCA ATG TCG AGG AGT GGG T reverse ACC GCT TGT CAT TCA CTT TGC	132 bp
ZO-1	forward ACC AGT AAG TCG TCC TGA TCC reverse TCG GCC AAA TCT TCT CAC TCC	128 bp

Changes in gut microbiota and metabolic products under HUA and AGA conditions

Alpha diversity analysis (including the abundance-based coverage estimator [ACE] index) indicated that the microbial richness of HUA and AGA mice was diminished compared to the control group (Figure 3A). Principal coordinate analysis (PCoA) revealed a clear separation between the HUA and AGA groups and the control group, while the HUA and AGA groups were more closely aligned (Figure 3B). Operational taxonomic unit (OTU) analysis further confirmed the reduced microbial richness in HUA and AGA mice (Figure 3C).

Figure 3.

The changes in the abundance of microbial communities of HUA and AGA mice

(A) ACE index boxplots of Enterobacteriaceae alpha diversity across groups.

(B) PCoA of intergroup differences.

(C) OTU cluster analysis between groups.

(D) The flora composition of each group at the level of phylum, class, order, family, genus, and species.

(E) Histogram of the abundance of the gut microbial community in mice.

We evaluated changes in the gut microbiota of mice at the phylum, class, order, family, genus, and species levels under HUA and AGA conditions. Compared with the control group, the microbial species composition differed in both HUA and AGA mice (Figures 3D and 3E). Under HUA conditions, 21 bacterial species were identified, including 12 Gram-positive species, such as g_Harryflintia, g_Sva1033, g_Clostridia_UCG-014, and g_Parvibacter, and 9 Gram-negative species including g_Rikenella, g_Alistipes, g_Proteus, and g_Akkermansia. Under AGA conditions, 32 bacterial species were identified, with 21 Gram-positive species (e.g., g_Candidatus_Soleaferrea, g_Hydrogenoanacrobacterium, g_Butyricicoccus, and g_Ruminococcaceae) and 11 Gram-negative species (e.g., g_Parasutterella, g_Pseudomonas, g_Stenotrophomonas, and g_Mucispirillum). Notably, 11 bacterial species were common to both HUA and AGA conditions, representing 52.38% of the HUA microbiota and 33.33% of the AGA microbiota, respectively. The abundances of 7 bacterial taxa were reduced, including g_[Eubacterium]_siraeum_group, g_Harryflintia, g_Ruminococcus, g_Lachnospiraceae_NK4B4_group, g_Lactococcus, g_Rikenella, and g_Alistipes, among which Gram-negative bacteria accounted for 28.57%; the abundances of 4 bacterial taxa were increased, including g_Parabacteroides, g_[Clostridium]_innocuum_group, g_Proteus, and g_Escherichia-Shigella, among which Gram-negative bacteria accounted for 75% (Table S1).

Effect of gut microbiota in HUA conditions on the HUA/NCM460 cell model in vitro

We established an HUA/NCM460 cell model to investigate the effects of the gut microbiota on IECs under HUA conditions (Figure 4A). We used the bacterial supernatant from HUA model mice to intervene in the HUA/NCM460 cell model. Microscopic analysis revealed that, compared with the control group, HUA/bacteria-treated cells exhibited cell membrane rupture, cytoplasmic leakage, and mild chromatin condensation (Figure 4B). Transmission electron microscopy (TEM) analysis revealed mild mitochondrial swelling and cristae rupture in the HUA/bacteria group (Figure 4C). Additionally, HUA/bacteria intervention increased intestinal mucosal permeability (p < 0.01 vs. the control group) (Figure 4D), increased the secretion of pro-inflammatory cytokines (p < 0.01 vs. the control group) (Figure 4E), downregulated the expression of mechanical barrier proteins, reduced ABCG2 expression, and upregulated GLUT9 expression (p < 0.01 vs. the control group) (Figures 4F and 4G), suggesting that HUA/bacteria promotes intestinal damage and inhibits UA excretion.

Figure 4.

Impact of HUA gut microbiota on HUA/NCM460 cells

(A) Experimental workflow.

(B) Morphological alterations in HUA/NCM460 cells under different bacterial interventions observed by inverted microscopy (100×).

(C) Ultrastructural changes of subcellular organelles in HUA/NCM460 cells visualized by TEM (5,000× and 10,000×).

(D) DAO and D-LA concentrations in cell supernatants measured by ELISA (n = 3).

(E) Inflammatory cytokine levels (IL-1β, TNF-α, and IL-6) in cell supernatants measured by ELISA (n = 3).

(F and G) mRNA levels and protein expression of Claudin-1, Occludin, ZO-1, ABCG2, and GLUT9 (n = 3). GAPDH served as an endogenous control.

Data are represented as mean ± SD. Statistical analysis was conducted using Tukey’s test and one-way ANOVA: compared with the HUA/NCM460+control/bacteria group, ∗∗p < 0.01 and ∗∗∗p < 0.001.

Effect of gut microbiota in the AGA conditions on the AGA/NCM460 cell model in vitro

We successfully established the AGA/NCM460 cell model, aiming to deeply explore the influence of the gut microbiota on IECs in the AGA environment (Figure 5A). In this study, we used the microbiota supernatant from AGA model mice to treat the AGA/NCM460 cell model. Microscopic observation revealed that compared to the control group, cells treated with AGA/bacteria exhibited pathological changes such as cell membrane rupture, cytoplasmic leakage, and chromatin condensation (Figure 5B). TEM observation revealed that mitochondria in the AGA/bacteria group exhibited swelling and cristae rupture (Figure 5C). Additionally, we found that AGA/bacteria intervention significantly increased intestinal mucosal permeability, while leading to the secretion levels of pro-inflammatory cytokines (p < 0.001 vs. the control group) (Figures 5D and 5E), downregulating the expression of mechanical barrier proteins, reducing ABCG2 expression, and upregulating GLUT9 expression (p < 0.05 vs. the control group) (Figures 5F and 5G). These findings indicate that AGA/bacteria can promote intestinal damage and inhibit the excretion of UA.

Figure 5.

Impact of AGA gut microbiota on AGA/NCM460 cells

(A) Experimental workflow.

(B) Morphological alterations in AGA/NCM460 cells under various bacterial interventions observed by inverted microscopy (100×).

(C) Ultrastructural changes of subcellular organelles in AGA/NCM460 cells visualized by TEM (5,000× and 10,000×).

(D) DAO and D-LA concentrations in cell supernatants measured by ELISA (n = 3).

(E)Inflammatory cytokine levels (IL-1β, IL-6, and TNF-α) in cell supernatants measured by ELISA (n = 3).

(F and G) mRNA levels and protein expression of ZO-1, Occludin, Claudin-1, ABCG2, and GLUT9 (n = 3). GAPDH served as an endogenous control.

Data are represented as means ± SD. Statistical analysis was conducted using Tukey’s test and one-way ANOVA: compared with the AGA/NCM460+control/bacteria group, ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001.

RNA sequencing analysis of key signaling pathways affecting UA excretion by gut microbiota under HUA and AGA conditions in vivo

To elucidate the mechanism by which the gut microbiota inhibits UA excretion under HUA and AGA conditions, we performed RNA sequencing (RNA-seq) on jejunal tissue and conducted cross-group comparative analysis. Significant differences in gene expression were observed between the control group vs. HUA group and control group vs. AGA group (Figures 6A–6D), with clear separation among the groups (Figure 6E). Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the “PI3K-AKT signaling pathway” “MAPK signaling pathway” and “calcium signaling pathway” were significantly enriched in both groups (Figures 6F and 6G). By analyzing the overlapping pathway maps between the control group vs. HUA group and the control group vs. AGA group, we identified the core signaling pathways regulated by the gut microbiota in both models. A total of 14 signaling pathways overlapped (Figure 6H), with the PI3K-AKT signaling pathway ranking first among the overlapping pathways, suggesting that the PI3K-AKT signaling pathway is involved in the entire process from HUA to AGA disease states (Figure 6I). Genetic interaction analysis of disease-related genes revealed that the ErbB3 gene was most highly associated in HUA mice (Figure 6J). Notably, in AGA mice, the cAMP signaling pathway showed a significant enrichment trend (p < 0.05), whereas in HUA mice, although the cAMP signaling pathway was also enriched, it did not reach statistical significance, suggesting that the cAMP signaling pathway may play a unique role in the disease progression of AGA (Figure 6K). Genetic interaction analysis revealed that the ADRB2, Prkacb, and Crebbp genes were most highly associated in AGA mice (Figure 6L). The abovementioned results suggest that the “ErbB3-PI3K-AKT signaling pathway” and “ADRB2-cAMP-PKA-CREB signaling pathway” play key roles in HUA and AGA, respectively.

Figure 6.

Transcriptome analysis reveals key regulatory mechanisms under HUA and AGA conditions

(A–D) Heatmap and volcano plot of differential genes.

(E) Non-metric multidimensional scaling (NMDS) analysis was used to examine the differences in gene expression profiles.

(F and G) Pathway enrichment analysis was conducted to identify the relevant KEGG pathways.

(H) Venn diagram analysis of the intersection of signaling pathways.

(I and J) Gene set enrichment analysis of ErbB3-PI3K-AKT signaling pathways.

(K and L) Gene set enrichment analysis of ADRB2-cAMP-PKA-CREB signaling pathways.

Validation of protein expression in key signaling pathways in HUA and AGA

To confirm the importance of the ErbB3-PI3K-AKT signaling pathway and the ADRB2-cAMP-PKA-CREB signaling pathway in regulating gut microbiota and promoting UA excretion in HUA and AGA, we examined key indicators of these pathways. The results showed that the expression of ErbB3, p-PI3K/PI3K, and p-AKT/AKT proteins was significantly higher in the HUA and AGA groups than in the control group (p < 0.01) (Figure 7A). In the AGA group, ADRB2, p-PKA/PKA, and p-CREB/CREB protein expression was significantly higher than in the HUA group (p < 0.01) (Figure 7B). Additionally, the cAMP content in the AGA group was elevated (p < 0.01) (Figure S2), which is consistent with the transcriptomic data.

Figure 7.

Protein expression of key signaling pathways in HUA and AGA

(A) Western blot was performed to detect the protein levels of ErbB3, p-PI3K/PI3K, and p-AKT/AKT in vivo (n = 3).

(B) Western blot was performed to detect the protein levels of ADRB2, p-PKA/PKA, and CREB in vitro (n = 3). GAPDH served as an endogenous control.

Data are represented as means ± SD. Statistical analysis was conducted using Tukey’s test and one-way ANOVA: compared with the control group, ∗p < 0.01, ∗∗p < 0.01, and ∗∗∗p < 0.001; compared with the HUA/bacteria group, ∗p < 0.01, ∗∗p < 0.01, and ∗∗∗p < 0.001.

Discussion

HUA is defined as a disorder of purine metabolism primarily caused by excessive UA production and/or insufficient excretion.²² AGA development is triggered by the persistent accumulation of MSU crystals resulting from elevated urate levels in HUA.²³ This process continuously recruits neutrophils and M1-type macrophages, thereby promoting an inflammatory response,^24,25 which is characterized by joint swelling, severe joint pain, and active inflammation.²⁶ Numerous studies have confirmed that HUA and AGA exhibit marked differences not only in their clinical manifestations but also in their underlying pathological characteristics.²⁷

In this study, we established HUA and AGA mouse models and observed differences in serum UA levels and renal pathological changes across different disease states. Both models exhibited elevated serum levels of UA, XOD, and ADA. Renal histological examination revealed glomerular sclerosis and interstitial inflammation. Additionally, levels of the renal injury markers CRE and BUN were increased, with a more pronounced elevation of BUN in the AGA group. Furthermore, inflammatory factor levels were also significantly higher, particularly in the AGA group.

The excretion of UA in mammals is predominantly achieved through two distinct pathways: the kidneys (60%–70%) and the intestines (30%–40%).^8,28 The intestinal pathway depends on the integrity of the intestinal barrier and the expression of UA transporters,^29,30 with the intestinal mucosal barrier being primarily maintained by tight junction proteins (Occludin, Claudins, and ZO-1).³¹ The present study revealed that both HUA and AGA mice exhibited intestinal damage, as evidenced by the downregulation of intestinal barrier proteins and alterations in mucosal injury markers (D-LA and DAO). Furthermore, both models showed reduced expression of the UA transporter ABCG2 and upregulation of the reabsorption protein GLUT9, with more pronounced changes observed in AGA mice. This finding indicates that impaired intestinal barrier function leads to inhibited UA excretion, with more severe damage occurring in AGA.

The gut microbiota can influence UA metabolism,³² and there are distinct differences in gut microbiota composition between HUA and AGA.^33,34 16S rRNA sequencing revealed alterations in both the abundance and diversity of gut microbiota in the two mouse models, showing significant differences compared to the control group. A total of 11 shared bacterial species were identified in both HUA and AGA mice, accounting for 52.38% and 33.33% of the microbiota in each group, respectively. Among these, the abundance of seven bacterial groups was decreased, including g_[Eubacterium]_siraeum_group, g_Harryflintia, g_Ruminococcus, g_Lachnospiraceae_NK4B4_group, g_Lactococcus, g_Rikenella, and g_Alistipes. Previous study indicates that HX39, which reduces inflammation, can modulate the intestinal flora composition in HUA mice, leading to a notable rise in the abundance of [Eubacterium]_siraeum_group.³⁵ Lactococcus can effectively reduce the serum UA levels in HUA rats by inhibiting XOD activity.³⁶ An analysis of the gut microbiota in gout patients with and without tophi revealed that Ruminococcus had the lowest species diversity.³⁷ Moreover, a clinical study reported that the members of the genus Alistipes in the control group were significantly more prevalent than that in HUA subjects.³⁸ These indicate that the abundance of these seven bacteria is positively correlated with a favorable UA metabolism state and is crucial for UA metabolism in both HUA and AGA.

Moreover, four bacterial species were observed to demonstrate an increase in abundance, including g_Parabacteroides, g_[Clostridium]_innocuum_group, g_Proteus, and g_Escherichia-Shigella. Of these, Gram-negative bacteria accounted for 75%. In another study, Clostridium and Parabacteroides in HUA mice were relatively more abundant than those in the controls.³⁹ The abundance of Proteus, which secretes XDH, increased in the gut microbiota of HUA rats.¹¹ A clinical study reported that Escherichia-Shigella were more abundant in tophi patients than controls.³⁹ These findings suggest that the occurrence of HUA and AGA is closely related to the increase of these four bacteria in vivo. It is worth noting that the intersection microbiota is present throughout the entire course of HUA and AGA diseases and plays an important role in the progression of gout.^40,41,42 Furthermore, we also found that 21 distinctive AGA-phase bacterial strains (Table S1) were absent in HUA. Thus, the 11 intersection species represent therapeutic targets for HUA management and gout prevention, while the 21 AGA-specific strains may serve as precision targets for acute gout intervention.

To further validate the impact of the gut microbiota on IECs, we established an in vitro cell model. In comparison with control microbiota, IECs intervened with HUA/bacteria and AGA/bacteria exhibited morphological changes, membrane rupture, and mitochondrial swelling, along with increased secretion of pro-inflammatory cytokines, downregulated expression of mechanical barrier proteins, increased intestinal mucosal permeability, decreased ABCG2 expression, and upregulated GLUT9 expression. These findings indicate that the microbiota from both disease states can promote IEC damage and inhibit intestinal UA excretion.

Through RNA-seq analysis of jejunal tissue from HUA and AGA mice, we investigated the core mechanisms underlying gut microbiota regulation. The results showed that the PI3K-AKT signaling pathway was significantly enriched in both HUA and AGA. In addition to the overlapping pathways, cAMP was the most significantly enriched pathway specifically in AGA. Genetic interaction analysis revealed that the ErbB3 gene was strongly associated with HUA, while the ADRB2, Prkacb and Crebbp genes were strongly associated with AGA. Western blot results further confirmed that the ErbB3-PI3K-AKT signaling pathway was activated in both HUA and AGA models, whereas the ADRB2-cAMP-PKA-CREB pathway was activated specifically in AGA. This suggests that the ErbB3-PI3K-AKT pathway is involved in the entire progression from HUA to AGA, while the ADRB2-cAMP-PKA-CREB pathway may play a unique role in AGA. The cAMP signaling pathway typically serves as a “protective pathway” regulating metabolism, immune homeostasis, and differentiation. For instance, activation of the cAMP/PKA/CREB pathway has been demonstrated to alleviate metabolic-associated fatty liver disease, enhance lipid metabolism, mitochondrial function, and reduce oxidative stress.⁴³ However, in the present study, the cAMP pathway was aberrantly activated in AGA, which is consistent with the findings of Shao et al., indicating that abnormal stress elevates cAMP levels, thereby exacerbating intestinal inflammation.⁴⁴ This finding reveals that the cAMP pathway may exhibit functional differences across distinct tissues, providing new insights for targeting this pathway in the treatment of AGA.

In summary, HUA and AGA it induces represent a complex disease process involving multiple systems and mechanisms, including renal dysfunction, inflammatory responses, impaired intestinal mucosal mechanical barriers, impaired intestinal UA excretion, dysbiosis of the gut microbiota, and abnormalities in signaling pathways. The present study established HUA and AGA mouse models, combined with in vitro experiments and RNA-seq technology, to elucidate changes in the gut microbiota and their impact on IEC function. The results revealed the core role of the “ErbB3-PI3K-AKT” and “ADRB2-cAMP-PKA-CREB” signaling pathways in HUA and AGA (Figure 8). This study has not only deepened our understanding of the pathogenesis of HUA and AGA but also provided new biomarkers and targets for disease treatment, particularly interventions targeting the gut microbiota and specific signaling pathways. Such findings may lead to new directions for the management of HUA and AGA.

Figure 8.

A schematic illustration of the working model

This study reveals alterations in the gut microbiota in HUA and AGA, as well as the core role of the ErbB3-PI3K-AKT and ADRB2-cAMP-PKA-CREB signaling pathways in the context of HUA and AGA.

Limitations of the study

This study has certain limitations. For instance, the intestinal tissue analysis primarily focused on the jejunum, without conducting histological analysis of the colon, detecting tight junction protein expression, or analyzing signaling pathways. In vitro experiments failed to identify the specific bacterial species and metabolites in the microbial supernatant that play key roles, and functional validation experiments such as small interfering RNA (siRNA) knockdown or pathway inhibitors were lacking, making it impossible to directly confirm the specific regulatory effects of the microbiota on signaling pathways. Additionally, although 11 key overlapping bacterial genera were identified, their functions were not subjected to predictive analysis. Finally, the findings based on the mouse model have yet to be validated in clinical samples from HUA/AGA patients. To address these limitations, Future studies will include colon-related experiments to systematically analyze the synergistic regulation of colonic and jejunal microbiota and changes in colonic barrier function, thereby constructing a complete intestinal regulatory network spanning the “colon-jejunum.” Untargeted or targeted metabolomic analysis (e.g., focusing on short-chain fatty acids, purine metabolites, etc.) will be performed on microbial culture supernatants to establish a causal chain of “microbiota-metabolite-host cell signaling pathway.” Functional experiments, including siRNA knockdown and pathway inhibitors, will be used to directly validate the effects of the microbiota on pathways such as ErbB3-PI3K-AKT and ADRB2-cAMP-PKA-CREB. Tools such as PICRUSt2 or Tax4Fun2 will be employed to predict the functions of the 11 key bacterial genera, annotating genes related to purine metabolism, inflammation regulation, and barrier repair. Furthermore, clinical samples will be utilized to validate the potential of differential bacterial genera and pathway molecules as biomarkers for disease diagnosis or prognosis.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Bin Huang (1196096001@qq.com).

Materials availability

This study did not generate new unique reagents. All materials generated during this study are available from the lead contact upon request.

Data and code availability

• •Data reported in this paper will be shared by the lead contact upon request. The 16S rRNA gene sequencing and RNA-seq data generated in this study have been deposited in the NCBI BioProject database under accession numbers PRJNA1327629 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1327629) and PRJNA1494029 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1494029), respectively, both of which are publicly accessible.
• •This paper does not report original code.
• •Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

This work was supported by the National Key R&D Program of China (no. 2022YFC3501200) and the National Natural Science Foundation of China (no. 81973891).

Author contributions

Conceptualization, J.G., Y.S., and B.H.; methodology, M.W., R.Y., and H.A.; investigation, M.W., R.Y., B.C., and Z.C.; writing and editing, M.W., R.Y., and H.A.; validation, F.Y. and P.C.; visualization, Q.C., Y.X., and M.L., supervision, J.G., Y.S., and B.H.; funding acquisition, J.G., Y.S., and B.H.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Rabbit anti-Claudin-1	Abcam	Cat# ab180158
Rabbit anti-ZO-1	Abcam	Cat# ab276131; RRID: AB_3083081
Rabbit anti-GLUT9	Abcam	Cat# ab223470; RRID: AB_3678822
Rabbit anti-Occludin	Cell Signaling	Cat# 91131; RRID: AB_2934013
Rabbit anti-ABCG2	Cell Signaling	Cat# 4477; RRID: AB_10544928
Rabbit anti-ErbB3	Cell Signaling	Cat# 12708T; RRID: AB_2721919
Rabbit anti-PI3K	Cell Signaling	Cat# 4292S; RRID: AB_329869
Rabbit anti-p-PI3K	Cell Signaling	Cat# 4228T; RRID: AB_659940
Rabbit anti-AKT	Cell Signaling	Cat# 9272S; RRID: AB_329827
Rabbit anti-p-AKT	Cell Signaling	Cat# 9271T; RRID: AB_329825
Rabbit anti-CREB	Cell Signaling	Cat# 9197T; RRID: AB_331277
Rabbit anti-p-CREB	Cell Signaling	Cat# 9198T; RRID: AB_2561044
Rabbit anti-ADRB2	Abcam	Cat# ab182136; RRID: AB_2747383
Rabbit anti-PKA	Abcam	Cat# ab75991; RRID: AB_1524202
Rabbit anti-p-PKA	Thermo Fisher Scientific	Cat# 44-988A; RRID: AB_2609316
Rabbit anti-GAPDH	Wuhan Proteintech Group	Cat# 10494-1-AP; RRID: AB_2263076
HRP-conjugated goat anti-mouse IgG (H + L)	Wuhan Proteintech Group	Cat# RGAM001; RRID: AB_3068333
HRP-conjugated goat anti-rabbit IgG (H + L)	Wuhan Proteintech Group	Cat# RGAR001; RRID: AB_3073505
Biological samples
Mouse (KM) serum	This paper	N/A
Mouse (KM) kidney tissues	This paper	N/A
Mouse (KM) jejunum tissues	This paper	N/A
Mouse (KM) fecal samples	This paper	N/A
Chemicals, peptides, and recombinant proteins
Hypoxanthine powder	Bellman Biotechnology Co., Ltd, China	Cat# S18025
Potassium oxonate powder	Bellman Biotechnology Co., Ltd, China	Cat# S17112
Enhanced RIPA lysis buffer	Wuhan Boster Biological Technology, Ltd., China	Cat# 18G26C02
Phosphatase inhibitors	Wuhan Boster Biological Technology, Ltd., China	Cat# 17B21C83
Protease inhibitors	Wuhan Boster Biological Technology, Ltd., China	Cat# 17E10A92
Two-step reverse transcription quantitative polymerase chain reaction (RT-qPCR) premix	Nanjing Novozymes Biotechnology Co., Ltd., China	Cat# 017E2210DA
Critical commercial assays
Uric Acid (UA) Test Kit	Nanjing Jiancheng Bioengineering Institute, China	Cat# 20200711
Alanine Transaminase (ALT) Test Kit	Nanjing Jiancheng Bioengineering Institute, China	Cat# C009-2-1
Aspartate Transaminase (AST) Test Kit	Nanjing Jiancheng Bioengineering Institute, China	Cat# C010-2-1
Mouse Xanthine Oxidase (XOD) Test Kit	Nanjing Jiancheng Bioengineering Institute, China	Cat# A002-1-1
Mouse Adenosine Deaminase (ADA) ELISA Kit	Jiangsu Enzyme-Free Industry Co., Ltd., China	Cat# 210316
Mouse cAMP ELISA Kit	Nanjing Boyan Biotechnology Co., Ltd., China	Cat# BY-JZF0169
BCA Protein Assay Kit	Wuhan Boster Biological Technology, Ltd., China	Cat# 18G24C46
Trizol Reagent Kit	Beijing Aidlab Biotechnology Co., Ltd., China	Cat# 332336AX
Reverse transcription Kit	Nanjing Novozymes Biotechnology Co., Ltd., China	Cat# L/N7E602G2
High-sensitivity dye-based qPCR detection Kit	Nanjing Novozymes Biotechnology Co., Ltd., China	Cat# 7E560E1
Deposited data
The 16S rRNA sequencing data	the National Center for Biotechnology Information	https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1327629
The RNA-seq data	the National Center for Biotechnology Information	https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1494029
Experimental models: Cell lines
NCM460 cell line	Aibivi Biotechnology Co., Ltd.	Cat# AW-CCH306
Experimental models: Organisms/strains
Mouse: KM (Kunming)	Beijing Huafu Biotechnology Co., Ltd	License#: SCXY (Beijing) 2019-0008
Oligonucleotides
the List of primer	Shangya Bio (Fujian)	Table 1
Software and algorithms
GraphPad-Prism10 (v10.1.2)	GraphPad	https://www.grapbpad.com/
SPSS software version23.0	SPSS	https://www.ibmspss.cn/download.html
ImageJ software	ImageJ	https://imagej.net/software/fji/

Experimental model and study participant details

Animals

Eighteen male KM mice, aged 2 months and weighing 22 ± 2 g, were obtained from Beijing Huafu Biotechnology Co., Ltd. (license number: SCXY (Beijing) 2019-0008). Mice were housed at the Fujian University of Traditional Chinese Medicine Animal Experiment Center (animal use license: SYXK Fujian 20190007; ethics approval number: 2022217). Housing conditions included regular feeding with ad libitum water access, a controlled temperature of 25 ± 2°C, relative humidity of 40%–70%, and a 12h/12h light-dark cycle. The mice were provided with a standard diet and free access to water. The mice were randomly divided into Control, HUA, and AGA group (n=6 per group).

Cell lines

NCM460 normal human colonic epithelial cells were obtained from Abiowel Biotechnology Co., Ltd. All cell lines were authenticated by short tandem repeat (STR) profiling and tested for mycoplasma contamination, and all results were negative. Cells were maintained in RPMI-1640 medium (or the appropriate medium used in your lab) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin at 37°C in a humidified 5% CO₂ incubator. Cells were passaged every 2–3 days upon reaching 80–90% confluence using 0.25% trypsin-EDTA. For experiments, cells in logarithmic growth phase were seeded into multi-well plates at indicated densities and cultured under standard conditions prior to interventions.

Method details

Animal model establishment

The HUA model

HUA mice (n=6) were fed a high-fat/high-sugar diet and received daily intragastric administration of 300 mg/kg hypoxanthine and 250 mg/kg potassium oxonate (0.1 mL/10 g body weight) for 21 days.^45,46 Fecal samples were collected 1 h after the final gavage. Following a 12h fasting period, mice were anesthetized (1.5% isoflurane) and blood collected via orbital extraction. Serum was separated (3000 rpm, 30 min) and stored at -20°C. Kidney and jejunum tissues were either fixed in 4% PFA for H&E staining or stored at -80°C for RNA-seq or Western blot analysis.

The AGA model

The AGA model was established in two sequential stages to reflect the pathological progression from hyperuricemia to acute gouty arthritis. In the first stage, mice (n=6) were fed a high-fat/sugar diet and received daily gavage of 300 mg/kg hypoxanthine and 250 mg/kg potassium oxonate suspension (0.1 mL/10g body weight) for 21 days to induce sustained HUA, consistent with the HUA model protocol. Subsequently (the second stage), 25 μL of 50 mg/mL MSU suspension was injected into the right knee joint cavity of the HUA-induced mice to establish gout.^47,48 Control mice received 25 μL saline. Twelve hours post-injection, fecal samples were collected for 16S rRNA sequencing. Under isoflurane anesthesia, blood was collected via orbital bleeding. Serum (3000 rpm, 30 min) was stored at -20°C. Kidney and jejunum tissues were fixed in 4% PFA for H&E staining or stored at -80°C for RNA-seq or Western blot.

H&E staining

Mouse intestinal and renal tissues were fixed in 4% paraformaldehyde solution for 24 h, routinely dehydrated, paraffin - embedded, and sectioned at a thickness of 4 μm. After staining with H&E, pathological changes in the intestinal and renal tissues were examined under a light microscope.

16S rRNA sequencing

Total DNA was extracted from fecal samples of each group for 16S rRNA sequencing. Following DNA extraction, PCR amplification, purification, and quality assessment by Biomarker Technologies Corporation, high-throughput sequencing of bacterial rRNA genes was performed using an Illumina HiSeq 2500 platform (Illumina, USA). High-quality sequencing reads were obtained through splicing, filtering, and data evaluation, and processed reads were clustered into operational taxonomic units for species annotation and diversity analysis (BioProject: PRJNA1327629).

Intact gut microbiota extraction

Fecal samples from the Control, HUA and AGA group, previously stored at -80°C in cryotubes, were rapidly thawed in an anaerobic chamber filled with CO₂. Following a ratio of two fecal pellets to 5 mL culture medium, samples were homogenized in cryotubes using a 1 mL syringe (without needle) until no solid particles remained. The homogenate was filtered through a mesh and washed with 10 mL phosphate buffered saline (PBS), then centrifuged at 8000 rpm for 5 min, and the supernatant was discarded. The resulting microbiota pellet was stored at -80°C.

Screening and in vitro culture of gut microbiota

Based on prior methodological studies,^36,49 four culture conditions were compared: Control group (uncultured gut microbiota), Yeast extract Casitone Fatty Acid (YCFA) medium group, a type 1 modified YCFA medium group (G-YCFA1), and a type 2 modified YCFA medium group (G-YCFA2), with triplicates per group. In the anaerobic chamber (H45, Don Whitely Scientific, Inc., UK), the extracted intact bacterial flora was placed in the respective media, placed in an anaerobic gas generating bag, and cultured for 24 h in a constant temperature shaker. Post-cultivation, 16S rRNA sequencing was performed to evaluate microbiota composition. Finally, we selected modified YCFA2 (G-YCFA2) as the optimal in vitro culture medium for murine gut microbiota.

Establishment of the HUA/NCM460 model

Based on the previously established intestinal epithelial cell model of UA-induced injury, adenosine and XOD were selected as inducers. NCM460 cells in the logarithmic growth phase were seeded at 1×10⁵ per well in 24-well plates. After 2 days of culture, the medium was replaced with fresh medium containing 2.0 mmol/L adenosine for 18 h induction. Subsequently, 0.005 U/mL XOD was added for further incubation of 8 h. A significant increase in UA production in the cell supernatant was confirmed by liquid chromatography, verifying the successful establishment of the HUA cell model.

Establishment of the AGA/NCM460 model

NCM460 cells at approximately 80% confluence in logarithmic growth phase were digested, counted, and seeded at a density of 1×10⁵ cells/mL (1 mL/well) in 24-well plates or at 5000 cells/well (100 μL/well) in 96-well plates. Following 24 h of adhesion, the culture medium was replaced with serum-free medium and incubated overnight. For 24-well plates, MSU suspensions at concentrations of 0, 4, 8, 12, 16, and 20 mg/dL were added to replicate wells and cultured for 6, 12, 18, or 24 h. Cell supernatants were centrifuged, collected in sterile 1.5 mL tubes, and stored at -20°C for subsequent interleukin (IL)-1β detection. For 96-well plates, MSU suspensions at identical concentrations were added, and cell viability was assessed using CCK-8 assays following 6, 12, 18, or 24 h of intervention.

Intervention with gut microbiota culture supernatants on HUA/NCM460 and AGA/NCM460 models

According to the methods described in section “2.6”, gut microbiota from Control, HUA, and AGA group mice were collected, resuspended in 2 mL of PBS, and then cultured in anaerobic gas packs containing medium for 24 hours. After centrifugation (8,000 r/min, 5 min), the supernatants were collected, filtered through a 0.22 μm sterile membrane, and labeled for later use. NCM460 cells in the logarithmic growth phase were cultured until adherence, and HUA/NCM460 or AGA/NCM460 cell models were established. The models were randomly divided into: HUA/NCM460+Con/bacteria vs. HUA/NCM460+HUA/bacteria and AGA/NCM460+Con/bacteria vs. AGA/NCM460+ AGA/bacteria. All groups were treated with the corresponding 10% gut microbiota culture supernatant. of Control, HUA, or AGA mice. After 72 hours of intervention, the effects on intestinal epithelial cell morphology and mucosal barrier homeostasis were evaluated.

Transmission electron microscopy (TEM) observation of NCM460 cell ultrastructure

Cells post-intervention were washed thrice with PBS, fixed with pre-warmed EM fixative (5 min, dark), and gently scraped. After centrifugation (2000 rpm, 3 min), pellets were resuspended in fresh fixative for 30 min (RT, dark), rinsed with phosphate buffer (×3), fixed in 1% OsO₄ (4°C, 2–3 h), washed with ddH₂O (×3), dehydrated via ethanol gradient, transitioned with propylene oxide, embedded in resin, polymerized at 60°C, ultrathin-sectioned, double-stained, and imaged by transmission electron microscopy.

ELISA detection

ELISA detection for UA, XOD, ADA, BUN, CRE, IL-1β, TNF-α, IL-6, DAO, D-LA (Jiangsu Enzyme-Free Industry, China), and cAMP (Nanjing Boyan Biotechnology Co., Ltd., China) was performed per manufacturer’s protocols. Standard solutions (5-point dilution) and samples were loaded in sextuplicate. After 37°C incubation (30 min) with primary antibodies, plates were washed (5 cycles: 200 μL wash buffer/30 s, pat-dried). Enzyme conjugate was added, incubated, washed, then developed with chromogenic reagent (10 min). Reactions were stopped, and absorbance (450 nm) measured (BioTek microplate reader). Concentrations were calculated from standard curves.

Real-time quantitative polymerase chain reaction (RT-qPCR) analysis

Total RNA was extracted from jejunal tissue and NCM460 cells using Trizol. Genomic DNA was removed in a 10 μL reaction system containing 150 ng RNA, 2.0 μL 5× gDNA elimination buffer, 1.0 μL gDNA eliminator, and nuclease-free water, incubated at 42°C for 2 min. cDNA was synthesized following kit instructions. RT-qPCR reactions (20 μL) comprised 10 μL 2× SYBR Green, 0.4 μL each forward/reverse primer (10 μmol/L), 1.0 μL cDNA, and nuclease-free water. Amplification conditions were: 94°C for 5 min (initial denaturation); 40 cycles of 95°C for 3 s (denaturation) and 60°C for 30 s (annealing/extension). Data were analyzed using the 2^-ΔΔCt method with primers specified in Table 1. The primers used in this experiment were synthesized and validated by Shangya Bio (Fujian).

RNA-seq transcriptomics

RNA extraction, library construction, quality control, and sequencing were conducted by GeneDenovo Biotechnology Co., Ltd. Total RNA from jejunal tissue and cell supernatants was extracted with Trizol, integrity verified via Agilent 2100. HISAT2 aligned reads to the reference genome; Stringtie assembled transcripts and quantified expression (RNA-Seq by expectation-maximization). DESeq2 identified differentially expressed genes (DEGs) (|log₂FC| ≥ 1, FDR < 0.05). DEGs underwent Gene Ontology (GO) term mapping (http://www.geneontology.org/) and hypergeometric testing for functional enrichment (vs. background genes), with parallel pathway enrichment analysis.

Western Blot

Proteins from mouse jejunal tissue and NCM460 cells were extracted using RIPA lysis buffer with 10 μL PMSF, and quantified by BCA assay kit (18G24C46, Wuhan Boster Biological Technology, Ltd.). After SDS-PAGE (10% separating gel/5% stacking gel) at 70 V (stacking) and 110 V (separating), proteins were transferred to PVDF membranes (200 mA, 90 min). Membranes were blocked with 5% skim milk/TBST (2 h, RT), then incubated overnight at 4°C with primary antibodies from Proteintech: ABCG2 (1:1000), GLUT9 (1:1000), Claudin-1 (1:1000), Occludin (1:1000), ZO-1 (1:1000), ErbB3 (1:1000), PI3K (1:1000), p-PI3K (1:1000), AKT (1:1000), p-AKT (1:1000), ADRB2 (1:1000), PKA (1:1000), p-PKA (1:1000), CREB (1:1000), p-CREB (1:1000) and GAPDH (1:5000). After TBST washes, membranes were incubated with goat anti-mouse/rabbit IgG secondary antibody (1:10000) at 37°C for 1 h. Bands were developed with ultrasensitive ECL reagent (18G28C01, Wuhan Boster Biological Technology, Ltd.) for 2 min, imaged on an Odyssey system (LI-COR), and quantified using ImageJ software (NIH).

Quantification and statistical analysis

All statistical analyses were conducted using SPSS software (v23.0, SPSS Inc., Chicago, IL, USA). One-way analysis of variance with Tukey’s multiple comparison test was used to analyze differences among multiple groups. Two-group comparisons were performed using unpaired two-tailed t-tests. Spearman’s correlation coefficient was employed to evaluate relationships between parameters. Significant differences were indicated as: ∗, p < 0.05; ∗∗, p < 0.01; ∗∗∗, p < 0.001. Histograms were generated using GraphPad Prism (v10.1.2, La Jolla, CA, USA). Data were presented as mean ± SD (standard deviation), as listed in the figure legends.