UPLC‐MS/MS‐based plasma lipidomics reveal a distinctive signature in systemic lupus erythematosus patients. Issue 2 (29th April 2021)

• Record Type: Journal Article
• Authors: Chen, Jiaxi; Liu, Chong; Ye, Shenyi; Lu, Ruyue; Zhu, Hongguo; Xu, Jiaqin
• Is Part Of: MedComm. Volume 2:Issue 2(2021)
• Page Start: 269
• Page End: 278
• Publication Date: 2021-04-29
• Subjects: biomarkers -- lipidomics -- systemic lupus erythematosus; 610
• DOI: 10.1002/mco2.67
• Languages: English
• ISSNs: 2688-2663

Abstract

Abstract: Global lipidomics is of considerable utility for exploring altered lipid profiles and unique diagnostic biomarkers in diseases. We aim to apply ultra‐performance liquid chromatography‐tandem mass spectrometry to characterize the lipidomics profile in systemic lupus erythematosus (SLE) patients and explore the underlying pathogenic pathways using the lipidomics approach. Plasma samples from 18 SLE patients, 20 rheumatoid arthritis (RA) patients, and 20 healthy controls (HC) were collected. A total of 467 lipids molecular features were annotated from each sample. Orthogonal partial least square‐discriminant analysis, K‐mean clustering analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated disrupted lipid metabolism in SLE patients, especially in phospholipid, glycerol, and sphingolipid metabolism. The area under curve (AUC) of lipid metabolism biomarkers was better than SLE inflammation markers that ordinarily used in the clinic. Proposed model of monoglyceride (MG) (16:0), MG (18:0), phosphatidylethanolamine (PE) (18:3–16:0), PE (16:0–20:4), and phosphatidylcholine (PC) (O‐16:2–18:3) yielded AUC 1.000 (95% CI, 1.000–1.000), specificity 100% and sensitivity 100% in the diagnosis of SLE from HC. A panel of three lipids molecular PC (18:3‐18:1), PE (20:3–18:0), PE (16:0–20:4) permitted to accurately diagnosis of SLE from RA, with AUC 0.921 (95% CI, 0.828–1.000), 70% specificity, and 100% sensitivity. The plasma lipidomics signatures could serve as an efficient and accurate tool for early diagnosis and provide unprecedented insight into the pathogenesis of SLE. Abstract : Lipidomics profiling workflow. (a) Plasma lipidomics extraction was accomplished by fractionating samples after addition internal standard. (b) UPLC‐MS/MS‐based platforms were used to perform optimal profiling of plasma lipidomics. Normalization was carried out with quality control (QC) calibration and internal standard correction. (c) Univariate and multivariate data analyses were performed. (d) Selection of the best candidate biomarkers for the early diagnosis of SLE.