Huang Luofei, Shi Jian, Li Han, Lin Quanzhi
Department of Anesthesiology, Liuzhou Municipal Liutie Central Hospital, liuzhou, China.
Department of Internal Medicine, The People's Hospital of Laibin, Laibin, China.
BMJ Open. 2025 Sep 22;15(9):e102876. doi: 10.1136/bmjopen-2025-102876.
To investigate the causal relationship between serotonin levels, adenosine deaminase (ADA) activity and multiple sclerosis (MS) progression using an integrative multi-omics approach.
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.
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.
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.
采用整合多组学方法研究血清素水平、腺苷脱氨酶（ADA）活性与多发性硬化症（MS）进展之间的因果关系。
使用逆方差加权（IVW）估计进行两样本孟德尔随机化（MR）分析，以评估血清素、ADA与MS风险之间的因果关系。对来自基因表达综合数据库（GSE194078）的单细胞转录组数据进行分析，以识别表达ADA的免疫细胞亚群。此外，应用机器学习算法（支持向量机-递归特征消除、最小绝对收缩和选择算子以及随机森林）来识别诊断生物标志物，随后构建并验证列线图。
MR分析显示，血清素水平与MS进展呈正相关（IVWβ=0.350，p=3.63×10⁻⁵），而基因预测的ADA水平与MS风险呈负相关（IVWβ=-0.395，p=2.73×10⁻⁴）。此外，血清素水平与ADA活性呈反向因果关系（IVWβ=-0.089，p=8.70×10⁻³），没有反向因果关系的证据。单细胞分析识别出18个细胞亚群和6种主要免疫细胞类型，ADA在T-NK细胞中高表达，在血小板中低表达。同时，ADA在低免疫受体信号组中表达较高。富集分析表明，差异表达基因富集于细胞质翻译和RNA剪接等生物学过程，以及核糖体和神经退行性变-多种疾病等京都基因与基因组百科全书途径。识别出三个关键特征基因（IK、UBA52和CCDC25），基于这些基因的列线图显示出高诊断准确性，训练数据集的AUC为1.000，验证数据集的AUC为0.976。
血清素通过抑制ADA活性促进MS进展，将血清素-ADA轴定位为潜在治疗靶点。识别出的生物标志物（IK、UBA52和CCDC25）以及构建的列线图可能提高MS的诊断精度，为MS管理提供有价值的见解，并为未来研究提供理论参考。
