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Published on: September 8, 2021
Alterations in miR-151a-3p of plasma-derived exosomes and associated multimodal neuroimaging patterns in major
Wenjia Liang1,2, Lanwei Hou1, Wenjun Wang1
1Department of Anatomy and Neurobiology, Shandong Key Laboratory of Mental Disorders, Institute for Sectional Anatomy and Digital Human, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Institute of Brain and Brain-Inspired Science, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
Abstract:
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 diagnostic rate when only multimodal MRI features with intergroup differences were entered (70.45%). Furthermore, 10 out of 28 MDD patients exhibited a clinically significant response to the treatment (a reduction of over 50% in Hamilton Rating Scale for Depression (HAMD) score). The significant upregulation of plasma exosomal miR-151a-3p levels and changes of fMRI indicators were also observed in these 10 patients after treatment of six months. Animal experiments have shown that reducing the expression of miR-151-3p in ACC induces depression-like behaviors in mice, while elevating hsa-miR-151a-3p expression in ACC alleviates the depression-like behaviors of mice exposed to chronic unpredictable mild stress. Our study proposed an innovative diagnostic model of MDD by combining the plasma exosome-derived miR-151a-3p expression with its associated multimodal MRI patterns, potentially serving as a novel diagnostic tool.
Insights
This study introduces a novel diagnostic model for major depressive disorder (MDD) by integrating plasma exosomal microRNA (miRNA) levels with multimodal magnetic resonance imaging (MRI) patterns, achieving high diagnostic accuracy.
Area of Science:
- Neuroscience
- Genetics
- Biomarkers
Background:
- Major depressive disorder (MDD) diagnosis faces challenges due to heterogeneity, limiting the reliability of traditional magnetic resonance imaging (MRI).
- Developing objective biomarkers is crucial for accurate MDD diagnosis and treatment monitoring.
Purpose of the Study:
- To develop and validate a data-driven diagnostic model for MDD by combining multimodal MRI features with exosomal microRNA (miRNA) expression.
- To identify specific biomarkers and MRI patterns associated with MDD and treatment response.
Main Methods:
- A multimodal MRI fusion algorithm was employed, integrating functional MRI (fMRI) metrics with plasma exosome-derived miR-151a-3p levels.
- Machine learning models, including support vector machine (SVM), were used for classification accuracy assessment.
- Longitudinal data from MDD patients undergoing treatment and animal models were utilized for validation.
Main Results:
- Plasma exosomal miR-151a-3p levels were significantly lower in MDD patients and correlated with abnormal fMRI metrics in key brain regions.
- The combined biomarker and MRI model achieved a high diagnostic accuracy of 92.05% for MDD.
- Treatment response in MDD patients was associated with significant upregulation of miR-151a-3p and normalization of fMRI indicators.
Conclusions:
- The integration of plasma exosome-derived miR-151a-3p and associated multimodal MRI patterns offers a promising, innovative diagnostic tool for MDD.
- This approach potentially overcomes the limitations of heterogeneity in MDD diagnosis and treatment monitoring.
- Further validation could establish this multimodal model as a novel diagnostic standard for major depressive disorder.
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