Abstract
OBJECTIVE: To investigate the causal relationship between serotonin levels, adenosine deaminase (ADA) activity and multiple sclerosis (MS) progression using an integrative multi-omics approach. METHODS AND ANALYSIS: A two-sample Mendelian randomisation (MR) analysis was performed using inverse variance weighted (IVW) estimation to assess causality between serotonin, ADA and MS risk. Single-cell transcriptomic data from the Gene Expression Omnibus (GSE194078) were analysed to identify ADA-expressing immune cell subpopulations. Moreover, machine learning algorithms (Support Vector Machine-Recursive Feature Elimination, Least Absolute Shrinkage and Selection Operator and random forest) were applied to identify diagnostic biomarkers, following which a nomogram was constructed and validated. RESULTS: MR analysis revealed that serotonin levels were positively correlated with MS progression (IVW β=0.350, p=3.63E-05), whereas genetically predicted ADA levels were inversely associated with MS risk (IVW β=-0.395, p=2.73E-04). Additionally, serotonin levels exhibited an inverse causal relationship with ADA activity (IVW β=-0.089, p=8.70E-03), with no evidence of reverse causation. Single-cell analysis identified 18 cellular subpopulations and six major immune cell types, with ADA highly expressed in T-NK cells and expressed at lower levels in platelets. Meanwhile, ADA expression was higher in the low immune receptor signalling group. Enrichment analysis indicated that differentially expressed genes were enriched in biological processes such as cytoplasmic translation and RNA splicing, as well as Kyoto Encyclopedia of Genes and Genome pathways such as Ribosome and Neurodegeneration-Multiple Diseases. Three key feature genes (IK, UBA52 and CCDC25) were identified, and the nomogram based on these genes demonstrated high diagnostic accuracy, with an AUC of 1.000 in the training dataset and 0.976 in the validation dataset. CONCLUSIONS: Serotonin promotes MS progression by inhibiting ADA activity, positioning the serotonin-ADA axis as a potential therapeutic target. The identified biomarkers (IK, UBA52 and CCDC25) and the constructed nomogram may enhance diagnostic precision for MS, providing valuable insights for MS management and laying a theoretical reference for future studies.