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. 2019 May 3:10:976.
doi: 10.3389/fimmu.2019.00976. eCollection 2019.

Fecal Metabolomics and Potential Biomarkers for Systemic Lupus Erythematosus

Qiong Zhang  1 , Xiaofeng Yin  1 , Haifang Wang  1 , Xing Wu  2 , Xin Li  1 , Yao Li  1 , Xiaohe Zhang  1 , Chen Fu  1 , Haixia Li  1 , Yurong Qiu  1   3
Affiliations

Fecal Metabolomics and Potential Biomarkers for Systemic Lupus Erythematosus

Qiong Zhang et al. Front Immunol. .

Abstract

The role of metabolomics in autoimmune diseases has been a rapidly expanding area in researches over the last decade, while its pathophysiologic impact on systemic lupus erythematosus (SLE) remains poorly elucidated. In this study, we analyzed the metabolic profiling of fecal samples from SLE patients and healthy controls based on ultra-high-performance liquid chromatography equipped with mass spectrometry for exploring the potential biomarkers of SLE. The results showed that 23 differential metabolites and 5 perturbed pathways were identified between the two groups, including aminoacyl-tRNA biosynthesis, thiamine metabolism, nitrogen metabolism, tryptophan metabolism, and cyanoamino acid metabolism. In addition, logistic regression and ROC analysis were used to establish a diagnostic model for distinguishing SLE patients from healthy controls. The combined model of fecal PG 27:2 and proline achieved an area under the ROC curve of 0.846, and had a good diagnostic efficacy. In the present study, we analyzed the correlations between fecal metabolic perturbations and SLE pathogenesis. In summary, we firstly illustrate the comprehensive metabolic profiles of feces in SLE patients, suggesting that the fecal metabolites could be used as the potential non-invasive biomarkers for SLE.

Keywords: biomarker; feces; liquid chromatography; mass spectrometry; metabolomics; systemic lupus erythematosus.

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Figures

Figure 1
Figure 1
Typical mass spectra of the SLE group (A) and HC group (B).
Figure 2
Figure 2
Partial least squares discriminant analysis (PLS-DA) of fecal metabolomics data from SLE patients and healthy controls. Fecal metabolites distinguished SLE patients from healthy controls. The green dots represented SLE patients and the red dots represented healthy controls in the two-dimensional PLS-DA score plots.
Figure 3
Figure 3
Metabolic patterns in SLE patients and healthy controls. Fecal metabolite profiles in SLE patients and healthy controls were shown as heatmaps. Each row represented data for a specific metabolite and each column represented an individual. Different colors corresponded to the different intensity level of metabolites. Red and blue colors represented increased and decreased levels of metabolites, respectively.
Figure 4
Figure 4
Partial least squares discriminant analysis (PLS-DA) variable importance in projection (VIP) plot of significantly differential metabolites in SLE patients and healthy controls. The χ-axis represented the VIP scores, and the y-axis represented the compounds. Red and green colors represented increased and decreased levels of metabolites, respectively.
Figure 5
Figure 5
Pathway analysis of altered metabolites isolated from SLE patients compared with healthy controls. Twenty Three metabolic pathways were enriched in fecal samples. Aminoacyl-tRNA biosynthesis, thiamine metabolism, nitrogen metabolism, tryptophan metabolism, and cyanoamino acid metabolism significantly disturbed compared with healthy controls (p < 0.1). The χ-axis represented the pathway impact, and the y-axis represented the –log (p).
Figure 6
Figure 6
ROC analysis of potential biomarkers for differentiating SLE patients from healthy controls. PG 27:2 showed an AUC of 0.787 (95% CI: 0.660–0.884, p = 0.0002) (A); the proline presented an AUC of 0.755 (95% CI: 0.624–0.858, p = 0.0009) (B); the combined model performed an AUC of 0.846 (95% CI: 0.727–0.927, p < 0.0001) (C); the combined model was evaluated by 100-fold cross validation (D) and permutation test (E), achieving an AUC of 0.838 (95% CI: 0.677–0.971, p < 0.0001) and a p < 0.01.
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