A computational pipeline investigating the distinct molecular signatures of Bipolar Disorder (BP) and Major Depressive Disorder (MDD) within the human Amygdala and subgenual Anterior Cingulate Cortex (sgACC) across multiple resolution levels: Gene, Exon, Junction, and Transcript (Isoform).
Distinguishing between MDD and BP remains a significant clinical challenge due to overlapping depressive symptoms. Identifying distinct genomic boundaries is critically important to:
- Improve Early Differential Diagnosis: Reducing misdiagnosis rates and lag time to correct treatment [1, 2].
- Guide Personalized Treatment: Avoiding the risk of antidepressant-induced mania in BP patients [3, 4].
- Map Disease Biology: Uncovering the unique biological mechanisms underlying both conditions [5].
The amygdala acts as a critical hub for determining the emotional significance of stimuli (salience) and forming emotional memories.
- Role in MDD: Demonstrates hyperactivity and heightened reactivity to negative emotional stimuli, coupled with a failure of top-down regulation from the prefrontal cortex (PFC).
- Role in BP: Functions in a state-dependent manner. It shows profound hyperactivity/increased volume during mania, but variable or reduced activity during depressive phases.
The sgACC (Brodmann Area 25) modulates emotional, visceral, and neuroendocrine responses (such as cortisol regulation).
- Shared Structural Abnormality: Both MDD and BP patients show a consistent reduction in gray matter volume in the sgACC, typically driven by a loss of glial cells rather than neurons.
- Metabolic Hyperactivity: During active depressive episodes in both disorders, the sgACC displays elevated metabolic activity ("hot spot") that acts as a therapeutic target for Deep Brain Stimulation (DBS).
| Feature | Major Depressive Disorder (MDD) | Bipolar Disorder (BP) |
|---|---|---|
| Amygdala Activity | Consistently Hyperactive (especially to negative stimuli). | State-Dependent (Hyperactive in mania, variable/hypoactive in depression). |
| sgACC Structure | Reduced Volume (Trait-like abnormality). | Reduced Volume (Trait-like abnormality, similar to MDD). |
| sgACC Activity | Hyperactive during the depressive state. | Hyperactive during the depressive state. |
| Connectivity | Disrupted connectivity between the amygdala and frontal regulatory areas; increased adolescent sgACC-amygdala connectivity. | Distinct patterns of prefrontal-amygdala and sgACC-amygdala connectivity, particularly during pronounced regulatory failure in mania. |
Standard gene-level analysis often masks crucial distinctions because MDD and BP share risk genes. Investigating finer-grained features—Exons, Junctions, and Transcripts (Isoforms)—allows us to bypass these limitations through the lens of Alternative Splicing.
- Differential Transcript (Isoform) Usage: A single gene can produce multiple protein versions with opposing functions. MDD and BP may express the same gene, but produce entirely different isoform profiles.
- Exon & Junction Level Analysis: RNA processing and splicing alterations are fundamental molecular risks in neuropsychiatric conditions. Mapping splice junctions catches specific regulatory defects that total gene expression metrics completely miss.
- Distinct Biological Pathways: High-resolution sequencing reveals that while BP pathways are dominated by immune system activation and immune-regulatory factors, MDD pathways lean heavily toward stress response and metabolic dysregulation.
The structured analytical workflow implemented in this repository is outlined below.
Click the links below to access the code, documentation, and specific results for each step of the pipeline:
- Differential Expression Analysis: Identification of Differentially Expressed Features (DEFs) across genes, transcripts, exons, and junctions.
- WGCNA Network Analysis: Construction of weighted gene co-expression networks to find highly correlated feature modules.
- Gene-Set Enrichment: Functional enrichment and pathway mapping of identified DEFs.
- Fusion-TWAS: Transcriptome-Wide Association Studies integrating GWAS summary statistics with reference expression weights.
- IsoTWAS: Isoform-specific TWAS evaluations focusing on isoform-specific expression (ISE) and tissue-specific regulation.
- Leafcutter Splicing Analysis: Annotation-free quantification of RNA splicing variation focusing explicitly on intron usage across samples.
- Panagiotaropoulou, G. et al. (2025) ‘Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses’, The British Journal of Psychiatry, 226(2), pp. 79–90. doi:10.1192/bjp.2024.125.
- Yang, R. et al. (2023) ‘Differentiation between bipolar disorder and major depressive disorder in adolescents: from clinical to biological biomarkers’, Frontiers in Human Neuroscience, 17, p.1192544.
- Tonozzi, T. R. et al. (2018) ‘Pharmacogenetic Profile and Major Depressive and/or Bipolar Disorder Treatment: a Retrospective, Cross-Sectional Study’, Pharmacogenomics, 19(15), pp. 1169–1179. doi:10.2217/pgs-2018-0088.
- Wilcox, C. (2020) ‘Can Genetics Help Us Distinguish Bipolar Disorder from Major Depression?’, NEJM Journal Watch, p.NA51036.
- Panagiotaropoulou, G. et al. (2025) ‘Identifying genetic differences between bipolar disorder and major depression through multiple genome-wide association analyses’, The British Journal of Psychiatry, 226(2), pp.79-90.