Identification of a Novel Serum Proteomic Signature for Primary Sjögren's Syndrome

Abstract

Context: Primary Sjögren's syndrome (pSS) is a complex heterogeneous autoimmune disease (AID) which can mimic rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE). Our exploratory study investigated serum biomarkers that may discriminate pSS from RA and SLE.

Methods: Serum concentrations of 63 biomarkers involved in immune cell trafficking, inflammatory response, cellular movement, and cell-to-cell signaling were measured in AID patients, included prospectively into the study at the Montpellier University Hospital. A multivariate analysis by multiple logistic regression was performed, and discriminative power assessed using logistic regression adjusted on significant demographic factors.

Results: Among the 95 patients enrolled, 42 suffered from pSS, 28 from RA, and 25 from SLE. Statistical analysis showed that concentrations of BDNF (OR = 0.493 with 95% CI [0.273-0.891]; p = 0.0193) and I-TAC/CXCL11 (OR = 1.344 with 95% CI [1.027-1.76]; p = 0.0314) can significantly discriminate pSS from RA. Similarly, greater concentrations of sCD163 (OR = 0.803 with 95% CI [0.649-0.994]; p = 0.0436), Fractalkine/CX3CL1 (OR = 0.534 with 95% CI [0.287-0. 991]; p = 0.0466), MCP-1/CCL2 (OR = 0.839 with 95% CI [0.732-0.962]; p = 0.0121), and TNFa (OR = 0.479 with 95% CI [0.247-0.928]; p = 0.0292) were associated with SLE diagnosis compared to pSS. In addition, the combination of low concentrations of BDNF and Fractalkine/CX3CL1 was highly specific for pSS (specificity 96.2%; positive predictive value 80%) compared to RA and SLE, as well as the combination of high concentrations of I-TAC/CXCL11 and low concentrations of sCD163 (specificity 98.1%; positive predictive value 75%).

Conclusion: Our study highlights biomarkers potentially involved in pSS, RA, and SLE pathophysiology that could be useful for developing a pSS-specific diagnostic tool.

Keywords: biomarkers; primary Sjögren syndrome; proteomics; rheumatoid arthritis; systemic lupus erythematosus.

Figure 1

Heatmap representing the classification of diseases and functions by ingenuity pathway analysis (IPA). The visualization is a hierarchical heat map of functional categories generated by IPA software in which the major boxes represent a family (or category) of related functions. Within each box, each individual rectangle is a sub-function linked to the biological function of a group of proteins. The size of a rectangle is correlated with increasing overlap significance among the proteins members of the related function and the query proteins. The color scheme shown is based on z-scores, with activation in orange, inhibition in blue and undetermined functions in gray. Darker shades indicate higher absolute z-score heat map. For instance, most ‘Inflammatory response’, ‘Cellular movement’, ‘Immune cell trafficking’, ‘Cell-to-cell signaling and interaction’, ‘Hematological system development and function’ proteins were over-represented.

Figure 2

Functional and upstream regulatory networks. The most significant networks included proteins related to: (A) Cell-to-cell signaling and interaction, and cellular movement, (B) immune cell trafficking and inflammatory response, (C) organismal functions, hematological disease, and metabolic disease, (D, E) top-scoring network of regulator effects activated in the list of 63 biomarkers. Protein interaction networks were constructed using the ingenuity pathway analysis (IPA) software. Nodes shaded in pink represent proteins present in the list of 63 biomarkers; red nodes are proteins that are CXCL11 (I-TAC), CX3CL1 (Fractalkine), Brain Derived Neurotrophic Factor (BDNF), and sCD163; and proteins that are not found in the list of 63 biomarkers are in white. Dotted lines indicate indirect molecular interactions between proteins and continuous lines between nodes indicate direct functional interactions between connected proteins. Orange lines lead to activation.