Lipidomics profiling reveals the role of glycerophospholipid metabolism in psoriasis

Abstract

Psoriasis is a common and chronic inflammatory skin disease that is complicated by gene-environment interactions. Although genomic, transcriptomic, and proteomic analyses have been performed to investigate the pathogenesis of psoriasis, the role of metabolites in psoriasis, particularly of lipids, remains unclear. Lipids not only comprise the bulk of the cellular membrane bilayers but also regulate a variety of biological processes such as cell proliferation, apoptosis, immunity, angiogenesis, and inflammation. In this study, an untargeted lipidomics approach was used to study the lipid profiles in psoriasis and to identify lipid metabolite signatures for psoriasis through ultra-performance liquid chromatography-tandem quadrupole mass spectrometry. Plasma samples from 90 participants (45 healthy and 45 psoriasis patients) were collected and analyzed. Statistical analysis was applied to find different metabolites between the disease and healthy groups. In addition, enzyme-linked immunosorbent assay was performed to validate differentially expressed lipids in psoriatic patient plasma. Finally, we identified differential expression of several lipids including lysophosphatidic acid (LPA), lysophosphatidylcholine (LysoPC), phosphatidylinositol (PI), phosphatidylcholine (PC), and phosphatidic acid (PA); among these metabolites, LPA, LysoPC, and PA were significantly increased, while PC and PI were down-regulated in psoriasis patients. We found that elements of glycerophospholipid metabolism such as LPA, LysoPC, PA, PI, and PC were significantly altered in the plasma of psoriatic patients; this study characterizes the circulating lipids in psoriatic patients and provides novel insight into the role of lipids in psoriasis.

Keywords: Glycerophospholipid; Lipidomics; MS/MS; Metabolomics; Psoriasis.

Figure 1:

PCA score plots. Overview of PCA score plots obtained from all psoriasis (red), all healthy (green), and QC (blue) samples in positive mode (A) and negative mode (B). The PCA score map was derived from UPLC-QTOFMS spectra concerning psoriasis (red) and healthy (cyan) samples in positive mode (C) and negative mode (D).

Figure 2:

PLS-DA score plots from the healthy and psoriasis groups in (A) positive mode (R² = 0.699, Q² = 0.536) and (B) negative mode (R² = 0.676, Q² = 0.462). Validation plots were obtained from 200 permutation tests in (C) positive mode and (D) negative mode.

Figure 3:

Heatmap of the 17 significantly altered lipids (20 features). (+) represents positive mode, and (-) represents negative mode. The color is proportional to the intensity of change in metabolites; red indicates upregulation, and green indicates down-regulation.

Figure 4:

Boxplot of the 17 significantly altered lipids (20 features). Red represents the diseased state, and cyan represents the healthy state. The y-axis is the normalized intensity after log2 transformation. (+) represents positive mode, and (-) represents negative mode. (A) LysoPCs, (B) PAs, (C) PCs, (D) PIs.

Figure 5:

(A) ROC curves of LysoPCs with AUC = 0.743, PCs with AUC = 0.747, PAs with AUC = 0.778, and PIs with AUC = 0.758. (B) The best combination of metabolites selected from the 17 metabolites using the random forest method (AUC = 0.939). CI: confidence interval.

Figure 6:

(A) The pathway impact plot based on 17 differential lipids using MetaboAnalyst 3.0. Redder colors represent lower P-values, and larger circles represent higher impact factors. Low P-values and large pathway impact factors indicate that the pathway is greatly influenced. The pathways were mainly enriched in glycerophospholipid metabolism and glycosylphosphatidylinositol (GPI)-anchor biosynthesis. The concentrations of LPA and PA detected in ELISA are shown as (B) and (C). The asterisk (**) indicates a significant difference (p < 0.01) Student's t test.