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Profiling and Functional Analysis of Urinary Exosomal MicroRNAs in Pregnant Women with Systemic Lupus Erythematosus

Li, D.; Li, A.; Liu, L.; Liu, Y.

2025-08-01 immunology
10.1101/2025.07.29.667355 bioRxiv
Show abstract

BackgroundPregnancy in Systemic Lupus Erythematosus (pSLE) is high-risk, necessitating non-invasive biomarkers for monitoring and predicting complications. Urinary exosomes, containing miRNAs, offer a promising source reflecting systemic and renal states, yet their profile in late gestation pSLE is less studied. ObjectiveThis study aimed to investigate the profile of urinary exosomal miRNAs in pregnant women with SLE during late gestation compared to healthy pregnant controls and to explore their potential biological roles and pathways. MethodsUrinary exosomes were isolated from 6 pSLE patients and 5 controls. Exosomes were characterized, and miRNAs were sequenced. Bioinformatics analyses, including target gene prediction and functional enrichment (GO, KEGG), were performed. ResultsExosomes were successfully isolated. Sequencing identified 29 significantly downregulated miRNAs in pSLE exosomes. Enrichment analysis of these miRNAs and their target genes indicated involvement in metabolic and immune pathways, virus infection, and cellular senescence. Target genes also enriched in protein stability and transport, and high-level analysis showed broader roles in metabolic, immune, and cellular maintenance/stress processes. Targets of top differentially expressed miRNAs linked to mRNA regulation, AMPK, and senescence pathways. ConclusionOur findings demonstrate that urinary exosomal miRNAs are significantly altered in pregnant women with SLE during late gestation. The functional enrichment analysis suggests their target genes are involved in critical biological processes relevant to pSLE pathophysiology and pregnancy complications, including metabolism, immunity, viral response, senescence, and cellular maintenance. This highlights the potential of urinary exosomal miRNAs as non-invasive biomarkers for monitoring and predicting risks in this high-risk population.

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