Unveiling Childhood Trauma-Protein-Brain Interaction via Depressive Symptom-Guided 4-Way Data Fusion
Poster No:
489
Submission Type:
Abstract Submission
Authors:
Xueke Shan1, Dongmei Zhi1, Peng Wang1, Jing Sui1
Institutions:
1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author:
Xueke Shan
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Co-Author(s):
Dongmei Zhi
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Peng Wang
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Jing Sui
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China
Introduction:
Compelling evidence have revealed that major depressive disorder (MDD) is linked to adverse environments, aberrant brain function and structure, abnormal protein expression, and lower cognitive abilities[1][2][5]. However, these researches has mainly focused separately on brain function or structure rather than their covarying patterns. Integrating multidimensional data to identify an environment-brain-protein-symptom circuit could help develop personalized and actionable therapeutic pathways for depression.
Methods:
This study involved 146 participants (10-18 years), including 64 healthy controls and 84 MDD, all of whom had fMRI, sMRI, dMRI and protein data. We used Hamilton Depression Rating Scale-17 (HDRS-17) score as a prior reference to guide the four-way data fusion by multiset canonical correlation analysis with reference (MCCAR), consisting of fractional amplitude of low frequency fluctuations (fALFF), fractional anisotropy (FA), gray matter volume (GMV), and 1158 unique proteins (Figure 1a)[4]. We aim to identify multimodal protein-brain co-varying components that could differentiate MDD and HC, associated with HDRS (Figure 2a), and can be utilized to predict individual post-treatment symptom scores, adjusted for baseline scores, via a linear regression model, see Figure 1b[3]. Finally, mediation analysis was constructed to evaluate which adverse environmental exposures can be mediated by protein and/or brain structures/functions on depression symptoms (Figure 1c).
Results:
Figure 2a displayed the covarying components that were significantly group-discriminating (Two sample t-test: p = 3.43×10-12 for fALFF, p = 2.08×10-5 for FA, p = 3.49×10-6 for GMV, p < 0.001 for protein, FDR corrected) and also significantly correlated with HDRS (r = 0.62 for fALFF, r = -0.41 for FA, r = -0.46 for GMV; r = -0.62 for protein). Specifically, higher depressive symptoms were related to increased fALFF in prefrontal and central regions, decreased FA in the anterior thalamic radiation and cingulum, increased GMV in anterior cingulate cortex, middle temporal gyrus and decreased GMV in precuneus and thalamu, and specific protein (ACTA2, IGKV3D-20, RAC1, and PXDN). As shown in Figure 2b, biological pathway enrichment analysis was conducted for the identified protein component, revealing enrichment in nervous system development, adaptive immune response and adaptive immune system. Protein-protein interaction analysis identified RAC1 and PXDN as key hub proteins. Most importantly, the identified covarying patterns successfully predicted post-treatment depressive symptoms in MDD patients (r = 0.45, p = 0.004, N = 45) (Figure 2c). Mediation analysis revealed that pathways from proteins to brain function significantly mediated the effects of environmental factors (like childhood trauma, suicide attempts, self-harm) on depressive symptoms, and emotional abuse impact on depressive symptoms was mediated separately by brain structure, brain function, and proteins (Figure 2d).
Conclusions:
This is the first study to use depressive symptoms to link proteomics, multimodal brain neuroimaging, and environmental exposures in adolescent depression. We identified a critical childhood trauma-protein-frontoparietal network-depression symptom circuit that could potentially predict individualized post-treatment depressive symptoms in MDD patients. The study underscores the intricate interplay between molecular and neuroimaging markers, providing novel insights into MDD's mechanisms and potential personalized treatments.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Lifespan Development:
Early life, Adolescence, Aging 2
Modeling and Analysis Methods:
Multivariate Approaches
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Affective Disorders
MRI
Trauma
Other - depression; proteomics;
1|2Indicates the priority used for review
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[2]Kang, J. (2024). Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults. Science Bulletin.
[3]Qi, S. (2022). Derivation and utility of schizophrenia polygenic risk associated multimodal MRI frontotemporal network. Nature Communications, 13(1), 4929.
[4]Qi, S. (2018). MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain, 141(3), 916-926.
[5]Xu, M. (2024). Reconfiguration of Structural and Functional Connectivity Coupling in Patient Subgroups With Adolescent Depression. JAMA Netw Open, 7(3), e241933.