Characteristics of the Urinary Microbiome From Patients With Gout: A Prospective Study

Abstract

The role of host microbes in the pathogenesis of several diseases has been established, and altered microbiomes have been related to diseases. However, the variability of the urinary microbiome in individuals with gout has not been evaluated to date. Therefore, we conducted the present prospective study to characterize the urinary microbiome and its potential relation to gout. Urine samples from 30 patients with gout and 30 healthy controls were analyzed by Illumina MiSeq sequencing of the 16S rRNA hypervariable regions, and the microbiomes were compared according to alpha-diversity indices, complexity (beta diversity) with principal component analysis, and composition with linear discriminant analysis effect size. The most significantly different taxa at the phylum and genus levels were identified, and their potential as biomarkers for discriminating gout patients was assessed based on receiver operating characteristic (ROC) curve analysis. Compared with the healthy controls, there was a dramatic decrease in microbial richness and diversity in the urine of gout patients. The phylum Firmicutes and its derivatives (Lactobacillus_iners, Family_XI, and Finegoldia), the phylum Actinobacteria and its derivatives (unidentified_Actinobacteria, Corynebacteriales, Corynebacteriale, Corynebacterium_1, and Corynebacterium_tuberculostearicum), and the genera Prevotella and Corynebacterium_1 were significantly enriched in the urine of gout patients. ROC analysis indicated that the top five altered microbial genera could be reliable markers for distinguishing gout patients from healthy individuals. These findings demonstrate that there are specific alterations in the microbial diversity of gout patients. Thus, further studies on the causal relationship between gout and the urinary microbiome will offer new prospects for diagnosing, preventing, and treating gout.

Keywords: 16S rRNA; biomarker; gout; high-throughput sequencing; microbiome; urine.

Figure 1

Variation in alpha-diversity indices between gout patients (red) and healthy controls (green). Six indices of alpha-diversity were analyzed. The results demonstrate that ACE, Chao1, Shannon, Simpson, and observed-species of the urinary microbiota from the gout group were significantly lower than those of the healthy group (A–C,E,F), while no statistical difference in Good's coverage was found (D).

Figure 2

(A) The beta-diversity indices between gout patients (red) and healthy controls (green) are shown in PCoA analysis. The ordination plot shows a clear difference between the two groups. (B) Venn diagrams show the percentage of shared OTUs between gout patients and healthy controls. The results show that a total of 1606 OTUs were shared between two groups, which accounted for 84.34% of the total OTUs in healthy controls and 78.84% of the total OTUs in gout patients.

Figure 3

Microbiota composition at the phylum and genus levels. (A) Relative abundances of the 10 dominant bacterial phyla found across the two groups shown as histograms. A t-test was used to detect the difference between the two groups. (B–D) Phyla with significantly different relative abundances between two groups, *P <0.05. (E) Genus with significantly different relative abundances between two groups. Different genera were assigned only to those presenting minimum variation at a significance level of P <0.05.

Figure 4

A cladogram representative of the structure of the urinary microbiota and the predominant bacteria. (A) Histograms of LDA score; red and green represent gout samples and healthy controls, respectively. (B) Cladogram showing differentially abundant taxa of urinary microbiota in gout patients and healthy controls.

Figure 5

Receiver operating characteristic (ROC) curves demonstrating the performance of significantly altered microbial genera for the test (A) and training (B) validation subjects using leave-one-out cross-validation.