Eye Hospital, School of Ophthalmology & Optometry, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 47405, USA.
J Nanobiotechnology. 2022 Jul 27;20(1):349. doi: 10.1186/s12951-022-01540-4.
Non-alcoholic fatty liver disease (NAFLD) is a usual chronic liver disease and lacks non-invasive biomarkers for the clinical diagnosis and prognosis. Extracellular vesicles (EVs), a group of heterogeneous small membrane-bound vesicles, carry proteins and nucleic acids as promising biomarkers for clinical applications, but it has not been well explored on their lipid compositions related to NAFLD studies. Here, we investigate the lipid molecular function of urinary EVs and their potential as biomarkers for non-alcoholic steatohepatitis (NASH) detection.
This work includes 43 patients with non-alcoholic fatty liver (NAFL) and 40 patients with NASH. The EVs of urine were isolated and purified using the EXODUS method. The EV lipidomics was performed by LC-MS/MS. We then systematically compare the EV lipidomic profiles of NAFL and NASH patients and reveal the lipid signatures of NASH with the assistance of machine learning.
By lipidomic profiling of urinary EVs, we identify 422 lipids mainly including sterol lipids, fatty acyl lipids, glycerides, glycerophospholipids, and sphingolipids. Via the machine learning and random forest modeling, we obtain a biomarker panel composed of 4 lipid molecules including FFA (18:0), LPC (22:6/0:0), FFA (18:1), and PI (16:0/18:1), that can distinguish NASH with an AUC of 92.3%. These lipid molecules are closely associated with the occurrence and development of NASH.
The lack of non-invasive means for diagnosing NASH causes increasing morbidity. We investigate the NAFLD biomarkers from the insights of urinary EVs, and systematically compare the EV lipidomic profiles of NAFL and NASH, which holds the promise to expand the current knowledge of disease pathogenesis and evaluate their role as non-invasive biomarkers for NASH diagnosis and progression.
非酒精性脂肪性肝病（NAFLD）是一种常见的慢性肝病，缺乏用于临床诊断和预后的非侵入性生物标志物。细胞外囊泡（EVs）是一组异质性的小膜结合囊泡，携带蛋白质和核酸，作为有前途的临床应用生物标志物，但在与 NAFLD 研究相关的脂质组成方面尚未得到很好的研究。在这里，我们研究了尿 EVs 的脂质分子功能及其作为非酒精性脂肪性肝炎（NASH）检测生物标志物的潜力。
本研究包括 43 例非酒精性脂肪肝（NAFL）患者和 40 例 NASH 患者。采用 EXODUS 法分离和纯化尿 EVs。采用 LC-MS/MS 进行 EV 脂质组学分析。然后，我们通过机器学习系统地比较了 NAFL 和 NASH 患者的 EV 脂质组学图谱，并揭示了 NASH 的脂质特征。
通过对尿 EVs 的脂质组学分析，我们鉴定出 422 种脂质，主要包括甾醇脂质、脂肪酸脂、甘油酯、甘油磷脂和鞘脂。通过机器学习和随机森林建模，我们获得了一个由 4 种脂质分子组成的生物标志物谱，包括 FFA（18:0）、LPC（22:6/0:0）、FFA（18:1）和 PI（16:0/18:1），可区分 NASH，AUC 为 92.3%。这些脂质分子与 NASH 的发生和发展密切相关。
缺乏用于诊断 NASH 的非侵入性手段导致发病率不断上升。我们从尿 EVs 的角度研究了 NAFLD 生物标志物，并系统地比较了 NAFL 和 NASH 的 EV 脂质组学图谱，这有望扩展对疾病发病机制的现有认识，并评估其作为 NASH 诊断和进展的非侵入性生物标志物的作用。
