Background: The drugs commonly used to treat bipolar disorder have limited efficacy and drug discovery is hampered by the paucity of knowledge of the pathophysiology of this disease. This study aims to explore the role of microRNAs in bipolar disorder and understand the molecular mechanisms of action of commonly used bipolar disorder drugs.
Methods: The transcriptional effects of bipolar disorder drug combination (lithium, valproate, lamotrigine and quetiapine) in cultured human neuronal cells were studied using next generation sequencing. Differential expression of genes (n=20) and microRNAs (n=6) was assessed and the differentially expressed microRNAs were confirmed with TaqMan MicroRNA Assays. The expression of the differentially expressed microRNAs were inhibited to determine bipolar disorder drug effects on their target genes (n=8). Independent samples t-test was used for normally distributed data and Kruskal-Wallis/Mann-Whitney U test was used for data not distributed normally. Significance levels were set at p<0.05.
Results: We found that bipolar disorder drugs tended to increase the expression of miR-128 and miR-378 (p<0.05). Putative target genes of these microRNAs targeted pathways including those identified as "neuron projection development" and "axonogenesis". Many of the target genes are inhibitors of neurite outgrowth and neurogenesis and were downregulated following bipolar disorder drug combination treatment (all p<0.05). The bipolar disorder drug combination tended to decrease the expression of the target genes (NOVA1, GRIN3A, and VIM), however this effect could be reversed by the application of microRNA inhibitors.
Conclusions: We conclude that at a transcriptional level, bipolar disorder drugs affect several genes in concert that would increase neurite outgrowth and neurogenesis and hence neural plasticity, and that this effect is mediated (at least in part) by modulation of the expression of these two key microRNAs.
Keywords: bipolar disorder; depression; mania; microRNAs; neurite outgrowth; next generation sequencing.