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Proteomics based precision medicine for diagnosis of Systemic Lupus Erythematosus

Systemic Lupus Erythematosus (SLE) is an autoimmune disease which presents clinical manifestations in different organs and presenting autoantibodies targeting its own body. The pathogenesis of SLE is not yet fully understood. However, there is no proper biomarker to diagnose SLE and to measure disease activity. Proteomics based precision medicine approaches aims to improve diagnostics and prognostics of SLE. Components of the complement system have been associated with improved diagnosis of SLE patients.

We present a proteomics driven study to address and identify potential proteomic markers for patient subtyping. Plasma samples from the four different SLE patient groups (low vs. high SLEDAI; low vs. high C3gd) were selected based on clinical scores from the SLE patients (n=40). Plasma samples were analyzed by quantitative proteome analysis tandem mass spectrometry and low-abundance regulatory proteins using multiplex analysis. Autoantigen profiles were determined by 1536plex Immunome array. Our findings identify patients with the high SLEDAI scores were diagnosed with SLE at an earlier age of, while the patients with the low SLEDAI score were diagnosed at a later age. Proteomics analysis enabled quantitative assessment of patient group specific changes enabling subtyping of the four patient groups by principle component analysis. When compared between C3dghigh – C3dglow groups, 7 proteins were found to be upregulated and 43 proteins were downregulated. There were 16 upregulated proteins and 10 downregulated proteins in SLEDAIhigh – SLEDAIlow protein comparison.

From our experiments, we found the differences in key parameters of the blood test such as age of diagnosis, anti-dsDNA concentration, lymphocyte count of C1q concentration. The list of upregulated and downregulated proteins was obtained in the proteome analysis of these patient groups. These proteins can be valuable to understand subtypes of SLE patients as well as serve as potential diagnostic biomarkers for SLE.

Keywords: SLE; proteomics