Transcriptomic signatures associated with mania-to-depression and depression-to-mania transitions in bipolar disorder: a case report using induced microglia-like (iMG) cells
Shogo InamineiD, Sota KyuragiiD, Masahiro OhgidaniiD, Tetsuaki KimuraiD, Ituro InoueiD, Tomohiro NakaoiD, Takahiro A. KatoiD
Abstract
IntroductionBipolar disorder (BD) is characterized by recurring episodes of mania and depression. Despite extensive research, the pathophysiology underlying these mood swings remains elusive. Emerging evidence indicates a potential role for neuroinflammation and microglial activation in the pathophysiology of BD. MethodsWe employed a reverse-translational approach to generate directly induced microglia-like (iMG) cells from peripheral blood monocytes of a single patient with BD, repeatedly sampled across depressive, manic, and subsequent depressive phases. RNA sequencing was performed on iMG cells at each time point to identify differentially expressed genes related to mood state transitions. ResultsA thorough analysis of longitudinal gene expression data has led to the identification of three functional gene categories: "state-dependent genes", "depression-to-mania transition genes (named: firing genes)", and "mania-to-depression transition genes (named: extinguishing genes)". A total of 168 firing, 59 extinguishing, and 77 state-dependent genes were identified. Notably, functional annotation revealed that, compared to the extinction gene set, the firing gene set was enriched in immune and inflammatory response pathways, particularly early-response cytokines such as IL1B and TNF. ConclusionsBased on these findings, we propose that inflammatory immunomodulation by microglia contributes to mood switching in BD, especially in the process of depression-to-mania transition. The classification of genes by their relationship to state transitions offers a novel framework for understanding the molecular mechanisms underlying this complex disorder and may identify potential therapeutic targets to stabilize mood. Further validation with larger cohorts is warranted.
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