Brain and blood transcriptome profiles delineate common genetic pathways across suicidal ideation and suicide.
Shengnan SunQingkun LiuZhaoyu WangYung-Yu HuangM Elizabeth SubletteAndrew J DworkGorazd RosoklijaYongchao GeHanga C GalfalvyJ John MannFatemeh HaghighiPublished in: Molecular psychiatry (2024)
Human genetic studies indicate that suicidal ideation and behavior are both heritable. Most studies have examined associations between aberrant gene expression and suicide behavior, but behavior risk is linked to the severity of suicidal ideation. Through a gene network approach, this study investigates how gene co-expression patterns are associated with suicidal ideation and severity using RNA-seq data in peripheral blood from 46 live participants with elevated suicidal ideation and 46 with no ideation. Associations with the presence of suicidal ideation were found within 18 co-expressed modules (p < 0.05), as well as in 3 co-expressed modules associated with suicidal ideation severity (p < 0.05, not explained by severity of depression). Suicidal ideation presence and severity-related gene modules with enrichment of genes involved in defense against microbial infection, inflammation, and adaptive immune response were identified and investigated using RNA-seq data from postmortem brain that revealed gene expression differences with moderate effect sizes in suicide decedents vs. non-suicides in white matter, but not gray matter. Findings support a role of brain and peripheral blood inflammation in suicide risk, showing that suicidal ideation presence and severity are associated with an inflammatory signature detectable in blood and brain, indicating a biological continuity between ideation and suicidal behavior that may underlie a common heritability.
Keyphrases
- rna seq
- white matter
- single cell
- gene expression
- peripheral blood
- genome wide
- copy number
- depressive symptoms
- resting state
- immune response
- oxidative stress
- dna methylation
- multiple sclerosis
- endothelial cells
- functional connectivity
- poor prognosis
- network analysis
- cerebral ischemia
- genome wide identification
- machine learning
- big data
- transcription factor
- mass spectrometry
- artificial intelligence
- long non coding rna