Transcriptome Changes in Relation to Manic Episode - Lee, Ya-Chin; Chao, Yu-Lin; Chang, Chiao-Erh; Hsieh, Ming-Hsien; Liu, Kuan-Ting; Chen, Hsi-Chung; Lu, Mong-Liang; Chen, Wen-Yin; Chen, Chun-Hsin; Tsai, Mong-Hsun; Lu, Tzu-Pin; Huang, Ming-Chyi; et al. (Frontiers in Psychiatry, 2019)
Bipolar disorder (BD) is highly heritable with recurrent manic and depressive episodes. The present study focused on manic episode in BD patients, and to investigate state-specific transcriptome alterations between acute episode and remission, including mRNAs, long non-coding RNAs (lncRNAs), and miRNAs using microarray and RNA sequencing platforms. BD patients were enrolled with clinical and biological samples collected at both acute and remission status following-up for at least two-months. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples, and used the Affymetrix Human Transcriptome 2.0 array to capture transcriptome data at the two time-points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a meta-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intra-individual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that non-coding genes were of majority among the top DEGs. The expression fold change of coding genes among DEGs showed moderate to high correlations (~0.5) across platforms. We found that more non-coding RNAs than coding genes showed signals in both discovery samples and meta-analysis. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in manic state. For coding genes, we reported that the taste function-related genes maybe mania state-specific markers, including TAS2R5 and TAS2R3. We also found that MS4A14 was upregulated in mania state in the discovery samples (P=1.83*10-2), meta-analysis (P=1.67*10-3), and RNA-Seq samples (P=3.36*10-2). We performed weight gene co-expression network analysis to identify gene modules for manic episode. Genes in the manic-related modules were different from the susceptible loci of BD obtained from genomewide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine-cytokine receprtor interaction. Results of the present study showed potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness.