Metabolic alterations in vitamin D deficient systemic lupus erythematosus patients

Abstract

Vitamin D deficiency is increasingly common in systemic lupus erythematosus (SLE) patients and is associated with the disease activity and proteinuria. Recently, alterations in metabolism have been recognized as key regulators of SLE pathogenesis. Our objective was to identify differential metabolites in the serum metabolome of SLE with vitamin D deficiency. In this study, serum samples from 31 SLE patients were collected. Levels of 25(OH)D3 were assayed by ELISA. Patients were divided into two groups according to their vitamin D level (20 ng/ml). Untargeted metabolomics were used to study the metabolite profiles in serum by high-performance liquid chromatography-tandem mass spectrometry. Subsequently, we performed metabolomics profiling analysis to identify 52 significantly altered metabolites in vitamin D deficient SLE patients. The area under the curve (AUC) from ROC analyses was calculated to assess the diagnostic potential of each candidate metabolite biomarker. Lipids accounted for 66.67% of the differential metabolites in the serum, highlighted the disruption of lipid metabolism. The 52 differential metabolites were mapped to 27 metabolic pathways, with fat digestion and absorption, as well as lipid metabolism, emerging as the most significant pathways. The AUC of (S)-Oleuropeic acid and 2-Hydroxylinolenic acid during ROC analysis were 0.867 and 0.833, respectively, indicating their promising diagnostic potential. In conclusion, our results revealed vitamin D deficiency alters SLE metabolome, impacting lipid metabolism, and thrown insights into the pathogenesis and diagnosis of SLE.

Keywords: Lipids; Metabolism; Systemic lupus erythematosus; Vitamin D.

Figure 1

Serum metabolites profiling in SLE patients with or without serum vitamin D deficiency. (A) Classified bubble plot. The horizontal coordinate represents the number interval of differential metabolites, and the vertical coordinate represents the logarithmic transformation of FC. Each circle represents a differential metabolite, and the larger the circle, the higher the difference significance. Different colors indicate a primary class, where the differentiated metabolites of the same class are arranged together. (B) PCA score plots and heatmap of metabolomics. Overview of PCA score plots from all VD1 (red), all VD2 (green), and QC (blue) in positive and negative mixed mode. (C) Volcano Plot. Metabolites with FC > = 1.5 or FC < = − 1.5 and FDR-adjusted P < 0.05 were considered as statistically significant metabolites. The vertical dashed line analysis indicated log2(1/1.5) and log2(1.5). The red dots represent up-regulated metabolites, and the blue dots represent down-regulated metabolites. (D) Complex heatmap for significantly differential metabolites. The abundance of each metabolite is normalized by Z score normalization. The color is positively correlated with the intensity of change in metabolites, with red indicating up-regulation and green indicating down-regulation. Metabolite ontology, P value and FC value are shown in the left of the heatmap. (E). Classified pie chart of HMDB super class. The numbers and proportions of differential metabolites are shown. (F) Classified pie chart of HMDB class. The numbers and proportions of differential metabolites are shown.

Figure 2

Correlations between differential metabolites. (A) Correlation coefficient matrix heatmap. Positive correlation is shown in red, while negative correlation is shown in blue. The larger the proportion of coloring intervals, the stronger the correlation. (B) Circos plot. From outside to inside, there are metabolite names, HMDB classification of metabolites, log2(fold change), P-value, and correlation lines.

Figure 3

Metabolic pathway analysis of differential metabolites using MetaboAnalyst 4.0 based on the KEGG. (A) Secondary classific significant pathway bubble plot. (B) Significant pathway bubble plot. The significance of pathways in each class decreases from top to bottom. Size of circle represents the number of differential metabolites annotated into the pathway. Color of circle represents the corrected P value. (M, Metabolism; G, Genetic Information Processing; E, Environmental Information Processing; C, Cellular Processes; O, Organismal Systems; H, Human Diseases; D, Drug Development.) (C) Pathway impact plot calculated by Betweenness centrality method. (D) Pathway impact plot calculated by Out degree centrality method.

Figure 4

Regulatory network analysis of differential metabolites. Red circle represents metabolic pathway. Purple circle represents modular information. Yellow circle represents enzyme. Green circle represents background compound of a metabolic pathway. Blue circle represents chemical reaction. Green square represents differential metabolite.

Figure 5

Diagnostic potential of differential metabolites. (A) Relative presentations of differential metabolites in VD1 SLE patients and VD2 SLE patients. (B) ROC curves of the important altered metabolites.