Temporal changes of gene expression in health, schizophrenia, bipolar disorder, and major depressive disorder.
Arsen ArakelyanSusanna AvagyanAleksey KurnosovTigran MkrtchyanGohar MkrtchyanRoksana ZakharyanKarine R MayilyanHans BinderPublished in: Schizophrenia (Heidelberg, Germany) (2024)
The molecular events underlying the development, manifestation, and course of schizophrenia, bipolar disorder, and major depressive disorder span from embryonic life to advanced age. However, little is known about the early dynamics of gene expression in these disorders due to their relatively late manifestation. To address this, we conducted a secondary analysis of post-mortem prefrontal cortex datasets using bioinformatics and machine learning techniques to identify differentially expressed gene modules associated with aging and the diseases, determine their time-perturbation points, and assess enrichment with expression quantitative trait loci (eQTL) genes. Our findings revealed early, mid, and late deregulation of expression of functional gene modules involved in neurodevelopment, plasticity, homeostasis, and immune response. This supports the hypothesis that multiple hits throughout life contribute to disease manifestation rather than a single early-life event. Moreover, the time-perturbed functional gene modules were associated with genetic loci affecting gene expression, highlighting the role of genetic factors in gene expression dynamics and the development of disease phenotypes. Our findings emphasize the importance of investigating time-dependent perturbations in gene expression before the age of onset in elucidating the molecular mechanisms of psychiatric disorders.
Keyphrases
- bipolar disorder
- gene expression
- major depressive disorder
- genome wide
- dna methylation
- copy number
- machine learning
- early life
- immune response
- poor prognosis
- healthcare
- prefrontal cortex
- genome wide identification
- public health
- mental health
- binding protein
- deep learning
- high resolution
- inflammatory response
- social media
- health information
- transcription factor