Exploring the Plasma and Urine Extracellular Vesicles in Sepsis-Induced AKI

Background: Half of critical-ill patients with sepsis develop acute kidney injury (AKI), while these patients are associated with poor outcomes. Early recognition of AKI in sepsis is important for optimal treatment and avoiding further kidney injury. The serum creatinine is a late biomarker due to its delayed elevation post-kidney damage. Therefore, we aimed to investigate the plasma and urine extracellular vesicles (EVs) to identify potential biomarkers for early detection of sepsis-induced AKI (S-AKI) by proteomics analysis. Methods: In this study, 30 sepsis patients were examined, including 19 with S-AKI and 11 with sepsis only. EVs from plasma and urine were isolated using size exclusion chromatography (SEC) and confirmed by using western blot, nano-particle tracking analysis (NTA), and transmission electron microscopy (TEM). EVs were precipitated with acetone and digested with urea, dithiothreitol (DTT), iodoacetamide (IAA), and trypsin before liquid chromatography-mass spectrometry (LC-MS) analysis. Metabolomics analysis was conducted on the soluble portion post-acetone precipitation. MaxQuant processed MS data for identification and label-free quantification. Volcano plot analysis via MetaboAnalyst 6.0 identified differentially expressed proteins (DEPs). Gene enrichment was performed using FunRich software (v3.1.3), and pathway analysis was executed using the Kyoto Encyclopedia of Genes and Genomes (KEGG). Results: We discovered 38 DEPs in the urine EV dataset and 28 DEPs in the plasma EV dataset. For the gene enrichment study, the DEPs from both datasets mainly come from extracellular, exosome, and extracellular regions. Meanwhile, the molecular function and biological processes of the DEPs in both datasets showed a great relationship with complement activity. Further, the pathway analysis showed these were enriched in complement and coagulation cascades, and Staphylococcus aureus infection. For metabolomics analysis in urineEV dataset, differentially expressed metabolites are enriched in tyrosine metabolism. As for the plasmaEV dataset, differentially expressed metabolites are enriched in nicotinate and nicotinamide metabolism. Conclusion: These findings underscore the potential of EVs as biomarkers and therapeutic targets in S-AKI, offering new insights into the pathophysiology and potential treatment strategies for this critical condition.