Frontiers in cellular and infection microbiology · 2024
Urinary microbiome profiling as a non-invasive tool for identifying biomarkers in systemic lupus erythematosus and lupus nephritis.
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Shi Bo, Chen Fei, Gong Jianmin, Khan Adeel, Qian Xiang, Xu Zhipeng, Yang Ping
Аннотация
INTRODUCTION: Bacteriome alterations have been implicated in the pathogenesis of systemic lupus erythematosus (SLE). However, the relationship between SLE and the urinary microbiome remains underexplored. This study aimed to characterize the urinary microbiome of SLE patients using 16S rRNA sequencing and to investigate its correlations with clinical parameters through integrative analyses.
METHODS: Urine sediment samples were collected from individuals with SLE and lupus nephritis (LN) (n = 20), SLE without LN (n = 22), and healthy controls (HCs) (n = 23). DNA was extracted and subjected to 16S rRNA sequencing to profile the urinary microbiome. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic efficacy of urinary microbiota, while Spearman's correlation analysis was employed to identify links between specific microbial taxa and clinical parameters. Functional predictions of bacterial roles were performed using Picrust2.
RESULTS: The urinary microbiota diagnostic model exhibited excellent performance in distinguishing SLE patients from HCs. Spearman's analysis revealed significant correlations between the urinary microbiome and clinical parameters. Specifically, Sphingomonas and Lachnospiraceae genera showed positive correlations with vitamin D levels, cylinderuria, and proteinuria, while Pedobacter, Aquabacterium, Delftia, and Achromobacter displayed negative correlations with proteinuria and albumin-to-creatinine ratio (ACR). Functional predictions indicated that the urinary microbiome might influence immune regulation through modulation of signaling pathways and metabolic processes.
DISCUSSION: Our study is the first to reveal dysbiosis in the urinary microbiome of patients with SLE. Certain bacterial taxa in the urinary microbiome were identified as potential diagnostic biomarkers for SLE. Furthermore, the functional implications of these bacterial communities suggest their involvement in immune modulation, highlighting the potential for further investigation into their roles in SLE pathogenesis and diagnosis.
METHODS: Urine sediment samples were collected from individuals with SLE and lupus nephritis (LN) (n = 20), SLE without LN (n = 22), and healthy controls (HCs) (n = 23). DNA was extracted and subjected to 16S rRNA sequencing to profile the urinary microbiome. Receiver operating characteristic (ROC) curve analysis was conducted to evaluate the diagnostic efficacy of urinary microbiota, while Spearman's correlation analysis was employed to identify links between specific microbial taxa and clinical parameters. Functional predictions of bacterial roles were performed using Picrust2.
RESULTS: The urinary microbiota diagnostic model exhibited excellent performance in distinguishing SLE patients from HCs. Spearman's analysis revealed significant correlations between the urinary microbiome and clinical parameters. Specifically, Sphingomonas and Lachnospiraceae genera showed positive correlations with vitamin D levels, cylinderuria, and proteinuria, while Pedobacter, Aquabacterium, Delftia, and Achromobacter displayed negative correlations with proteinuria and albumin-to-creatinine ratio (ACR). Functional predictions indicated that the urinary microbiome might influence immune regulation through modulation of signaling pathways and metabolic processes.
DISCUSSION: Our study is the first to reveal dysbiosis in the urinary microbiome of patients with SLE. Certain bacterial taxa in the urinary microbiome were identified as potential diagnostic biomarkers for SLE. Furthermore, the functional implications of these bacterial communities suggest their involvement in immune modulation, highlighting the potential for further investigation into their roles in SLE pathogenesis and diagnosis.