Serum Proteomics Distinguish Subtypes of NMO Spectrum Disorder and MOG Antibody-Associated Disease and Highlight Effects of B-Cell Depletion.

BACKGROUND AND OBJECTIVES: AQP4 antibody-positive NMOSD (AQP4-NMOSD), MOG antibody-associated disease (MOGAD), and seronegative NMOSD (SN-NMOSD) are neuroautoimmune conditions that have overlapping clinical manifestations. Yet, important differences exist in these diseases, particularly in B-cell depletion (BCD) efficacy. Yet, the biology driving these differences remains unclear. Our study aims to clarify biological pathways distinguishing these diseases beyond autoantibodies and investigate variable BCD effects through proteomic comparisons. METHODS: In a retrospective study, 1,463 serum proteins were measured in 53 AQP4-NMOSD, 25 MOGAD, 18 SN-NMOSD, and 49 healthy individuals. To identify disease subtype-associated signatures, we examined serum proteins in patients without anti-CD20 B-cell depletion (NoBCD). We then assessed the effect of BCD treatment within each subtype by comparing proteins between BCD-treated and NoBCD-treated patients. RESULTS: In NoBCD-treated patients, serum profiles distinguished the 3 diseases. AQP4-NMOSD showed elevated type I interferon-induced chemokines (CXCL9 and CXCL10) and TFH chemokine (CXCL13). MOGAD exhibited increased cytotoxic T-cell proteases (granzyme B and granzyme H), while SN-NMOSD displayed elevated Wnt inhibitory factor 1, a marker for nerve injury. Across all subtypes, BCD-treated patients showed reduction of B-cell-associated proteins. In AQP4-NMOSD, BCD led to a decrease in several inflammatory pathways, including IL-17 signaling, cytokine storm, and macrophage activation. By contrast, BCD elevated these pathways in patients with MOGAD. BCD had no effect on these pathways in SN-NMOSD. DISCUSSION: Proteomic profiles show unique biological pathways that distinguish AQP4-NMOSD, MOGAD, or SN-NMOSD. Furthermore, BCD uniquely affects inflammatory pathways in each disease type, providing an explanation for the disparate therapeutic response in AQP4-NMOSD and MOGAD.

Biomarker panel increases accuracy for identification of an MS relapse beyond sNfL.

BACKGROUND: For relapsing-remitting multiple sclerosis (RRMS), there is a need for biomarker development beyond clinical manifestations and MRI. Soluble neurofilament light chain (sNfL) has emerged as a biomarker for inflammatory activity in RRMS. However, there are limitations to the accuracy of sNfL in identifying relapses. Here, we sought to identify a panel of biomarkers that would increase the precision of distinguishing patients in relapse compared to sNfL alone. METHODS: We used a multiplex approach to measure levels of 724 blood proteins in two distinct RRMS cohorts. Multiple t-tests with covariate correction determined biomarkers that were differentially regulated in relapse and remission. Logistic regression models determined the accuracy of biomarkers to distinguish relapses from remission. RESULTS: The discovery cohort identified 37 proteins differentially abundant in active RRMS relapse compared to remission. The verification cohort confirmed four proteins, including sNfL, were altered in active RRMS relapse compared to remission. Logistic regression showed that the 4-protein panel identified active relapse with higher accuracy (AUC = 0.87) than sNfL alone (AUC = 0.69). CONCLUSION: Our studies confirmed that sNfL is elevated during relapses in RRMS patients. Furthermore, we identified three other blood proteins, uPA, hK8 and DSG3 that were altered during relapse. Together, these four biomarkers could be used to monitor disease activity in RRMS patients.