Alterations in miR-151a-3p of plasma-derived exosomes and associated multimodal neuroimaging patterns in major depressive disorder

作者：Wenjia Liang, Lanwei Hou, Wenjun Wang, Baoshan Wang, Chenxi Sun, Yuan Zhang, Zhuoran Li, Rong Shi, Wenjuan Zhou, Yuchun Tang, Wei Wang, Lejin Yang, Shuwei Liu · 发表于：Molecular Psychiatry · 年份：2025 · DOI：10.1038/s41380-025-03102-0 · 被引用次数：8 · 研究领域：Tryptophan and brain disorders、Bipolar Disorder and Treatment、Stress Responses and Cortisol

Magnetic resonance imaging (MRI) has been recognized as a valuable tool for achieving 'reification of clinical diagnosis' of major depressive disorder (MDD). However, the reliability and validity of MRI results are often compromised by genetic, environmental, and clinical heterogeneity within test samples. Here, we combined MRI with other clinical findings using multimodal MRI fusion algorithm to construct a data-driven, bottom-up diagnostic approach. The covariation patterns between the multimodal MRI features and differential expression of exosomal microRNA (miRNA) were identified on a subset of 70 MDD patients and 71 healthy controls (HCs) (served as a training set) as classification features, whereas data from the other 45 MDD patients and 43 HCs served as a test set. Furthermore, longitudinal data from 28 MDD patients undergoing antidepressant treatment for six months were utilized to validate the identified biomarkers, and related signaling pathways were initially explored in depression-like mice. Plasma exosome-derived miR-151a-3p levels were found to be significantly lower in MDD patients compared to HCs and correlated with abnormal changes in functional MRI (fMRI) metrics in the anterior cingulate cortex (ACC), visual cortex, and default mode network, etc. Then, these multimodal MRI features associated with miR-151a-3p expression distinguished MDD patients from HCs with high classification accuracy of 92.05% in support vector machine (SVM) model, outperforming the di...