W106 - Integrated Soluble Proteomics: Predicting Renal Involvement in Systemic Lupus Erythematosus

Cristina Arriens – Oklahoma Medical Research Foundation; Carla Guthridge – Oklahoma Medical Research Foundation; Judith James – Oklahoma Medical Research Foundation; Ben Jones – Oklahoma Medical Research Foundation; Joan Merrill – Oklahoma Medical Research Foundation

Abstract Text: Systemic lupus erythematosus (SLE), a clinically diverse autoimmune disease, challenges in predicting its varied manifestations. While cytokine profiles have been used, their predictive accuracy is still limited. This study aimed to evaluate soluble proteomics and autoreactomics in predicting SLE renal involvement. We included 88 plasmas from 16 controls and 72 SLE patients categorized into: Group2 - without renal disease nor anti-nuclear antibodies, Group3 - with anti-dsDNA but no renal disease, Group4 - both anti-dsDNA and renal disease, and Group5 - anti-RNA autoantibodies with some renal disease. Here, we utilized a high-throughput and sensitive human proteomic assay (Olink ExplorerNGS) consisting of 1472 analytes. Univariate analysis showed that there are 186 dysregulated proteins in Group2, 398 in Group3, 444 in Group4, and 375 in Group5 compared to controls. Pathway analysis revealed upregulation in toll-like receptor, interleukin-10, and cytokine/chemokine signaling in Group3, Group4, and Group5. Notably, interferon-gamma production/signaling was prominent only in Group4 with renal disease. Unsupervised UMAP identified four clusters, with cluster4 comprising patients with renal disease. Cluster4 showed significantly higher levels of HAVCR2, BST2, IL10, CD300, and IFNG than other clusters. Random Forest model based on HAVCR2, BST2, TNFRSF1B, CXCL9, CD300, and IFNGR was able to predict patients with renal involvement with ~91.0% accuracy. Of interest, HAVCR2 and BST2 are both expressed in the kidney, suggesting the tissue-specific damaging signatures with cytokines/chemokines may provide a superior RF model. The study underscores the importance of a comprehensive approach, combining molecular signatures and inflammatory mediators, for enhanced prediction and monitoring of severe manifestations.