Integrated multiomics profiling reveals MS-specific molecular signatures linked to progression and response to nasal anti-CD3 (Foralumab) therapy 2267524

Abstract

Abstract Introduction Chronic neuroinflammation in multiple sclerosis (MS) can persist independently of relapse activity, revealing mechanisms of progression not addressed by current therapies. Dysregulated T-cell activity plays a central role in this process and is the main target of nasal Foralumab immunotherapy. Here, we aimed to identify CNS-related biomarkers in the CSF of MS patients linked to disease progression and to define the molecular effects of Foralumab in secondary progressive (SP) MS. Methods We integrated single-cell and proteomic data from PBMC (n = 4) and CSF (n = 23) of MS patients on anti-CD20 therapy or untreated. Data-independent acquisition (DAI) proteomics was performed in CSF of relapsing-remitting (RR) and SP patients. Results CSF proteomic profiling revealed distinct molecular signatures between RR and SP patients. RR samples showed higher levels of immune activation markers (PTPRC, RGS10) and proteins associated with neuronal plasticity and axonal injury (NPTX2, NETO1). In contrast, SP samples were enriched for fibroblast-related proteins involved in tissue injury, fibrosis, and extracellular matrix (ECM) remodeling (FAP, COL8A1), along with elevated cytotoxicity markers (CTSW) and components of the complement cascade. Foralumab treatment significantly reduced the expression of proteins linked to cytotoxic activity (LAMP1), interferon signaling (IFNAR1), tissue injury/fibrosis (COBA1), and NF-κB—dependent inflammation (SIGLEC14, LY86). Notably, increased TGFB1 expression was observed in both CSF and PBMCs, suggesting a regulatory and immunomodulatory effect of the therapy. Conclusion We identified accessible biomarkers distinguishing RR and SP patients and proteins linked to disease progression and found that nasal Foralumab ameliorates MS by increasing TGFB1 and suppressing T cell cytotoxicity and inflammation. Funding Source n/a Topic Categories Computational and Systems Immunology (COMP)