Metabolomic Profiling Reveals Serum Tryptophan as a Potential Therapeutic Target for Systemic Lupus Erythematosus.

Objective: This study aimed to identify potential diagnostic biomarkers for systemic lupus erythematosus (SLE) using metabolomics approaches and machine learning algorithms, and to evaluate therapeutic targets for SLE treatment. Methods: Serum samples from 44 SLE patients with lupus nephritis, 40 rheumatoid arthritis patients, 39 primary Sjögren's syndrome patients, and matched healthy controls were analyzed using ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Eight machine learning algorithms were employed to establish diagnostic models. Partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were used to identify differential metabolites. The therapeutic potential of identified metabolites was validated in MRL-Fas Results: A total of 129 metabolites were detected, with machine learning models achieving area under the curve (AUC) values >0.8. The principal component regression model performed best with AUC values of 0.99 and 0.96 for training and test datasets, respectively. Two key metabolites, tryptophan and beta-alanine, showed significantly decreased levels in SLE patients compared to healthy controls (both p<0.05), while exhibiting opposite patterns in other autoimmune diseases. In the mouse model, tryptophan supplementation improved renal histology, reduced proteinuria, increased naïve T cells and central memory T cells, and decreased effector T cell frequencies in both peripheral blood and spleen. Conclusion: This study demonstrates the successful application of machine learning algorithms to metabolomics data for SLE classification and identifies tryptophan and beta-alanine as potential SLE-specific biomarkers. Tryptophan supplementation shows therapeutic promise in lupus mouse models through immunomodulatory effects on T cell subsets and renal protection.