UPLC-MS/MS-based plasma lipidomics reveal a distinctive signature in systemic lupus erythematosus patients

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.

Keywords: biomarkers; lipidomics; systemic lupus erythematosus.

FIGURE 1

Establishment and validation of OPLS‐DA models (SLE group vs. HC group, SLE group vs. RA group) and screening of differential lipids. (A and B) The OPLS‐DA models showed that the lipid metabolites between SLE group and HC group, SLE group and RA group were separated clearly. (C and D) The models had good prediction ability between SLE group and HC group, SLE group and RA group. (E and F) The difference lipid category between SLE group and HC group, SLE group and RA group were identified by volcanic maps

FIGURE 2

The lipidome KEGG enrichment analysis of SLE patients. (A and C) KEGG pathway analysis of differentially expressed lipids between SLE and HC groups. (B and D) KEGG pathway analysis of differentially expressed lipids between SLE and RA groups. The color of bubbles represents the value of adjusted p‐value, and the size of bubbles represents the number of counts

FIGURE 3

Lipid metabolomic clusters in SLE patients identified by K‐means. (A) Cluster 1 comprised 34 lipid metabolites that showed a significant downward trend in SLE patients. (B) Cluster 2 comprised 157 lipid metabolites that showed a significant upward trend in SLE patients. (C) Triglyceride metabolism lipid metabolites changed in SLE patients. (D) Glycerophospholipid metabolism lipid metabolites were upregulated in SLE patients. (E) Sphingolipid metabolism lipid metabolites were upregulated in SLE patients

FIGURE 4

Evaluation potential of lipid metabolites in the diagnosis of SLE from other groups. (A) ROC curve was used to distinguish the patients in the SLE group and HC group; (B) ROC curve was used to distinguish patients in SLE and RA groups

FIGURE 5

The changes of differential lipids in HC, SLEDAI score ≤ 11 and SLEDAI score > 11 groups, respectively. Relative concentration of (A) MG(16:0), (B) MG(18:0), (C) PC(O‐16:2‐18:3), (D) PE(18:3‐16:0), and (E) PE(16:0‐20:4) in HC, SLEDAI score ≤ 11 and SLEDAI score > 11 groups, respectively. Mann‐Whitney U test was used to test the statistical significance. ***p < 0.001, **p < 0.01, *p < 0.05