bioRxivMicrobiology
The bladder microbiome, metabolome, cytokines, and phenotypes in patients with systemic lupus erythematosus
F. Liu, J. Du, Q. Zhai, J. Hu, A. W. Miller, T. Ren, Y. Feng, P. Jiang, L. Hu, J. Sheng, C. Gu, R. Yan, L. Lv, A. J. Wolfe, N. Feng
Abstract
Background and aimsEmerging studies reveal a unique bacterial community in the human bladder, with alteration of composition associated to disease states. Systemic lupus erythematosus (SLE) is a complex autoimmune disease that is characterized by frequent impairment of the kidney. Here, we explored the bladder microbiome, metabolome, and cytokine profiles in SLE patients, as well as correlations between microbiome and metabolome, cytokines, and disease profiles. Methods and materialsWe recruited a cohort of 50 SLE patients and 50 individually matched asymptomatic controls. We used transurethral catheterization to collect urine samples, 16S rRNA gene sequencing to profile bladder microbiomes, and LC-MS/MS to perform untargeted metabolomic profiling. ResultsCompared to controls, SLE patients possessed a unique bladder microbial community and increased alpha diversity. These differences were accompanied by differences in urinary metabolomes, cytokines, and patients disease profiles. The SLE-enriched genera, including Bacteroides, were positively correlated with several SLE-enriched metabolites, including olopatadine. The SLE-depleted genera, such as Pseudomonas, were negatively correlated to SLE-depleted cytokines, including IL-8. Alteration of the bladder microbiome was associated with disease profile. For example, the genera Megamonas and Phocaeicola were negatively correlated with serum complement C3, and Streptococcus was positively correlated with IgG. ConclusionsOur present study reveals associations between the bladder microbiome and the urinary metabolome, cytokines, and disease phenotypes. Our results could help identify biomarkers for SLE.
- organism: human
- assay: metabolomics
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: statistical model
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