Identification of RNA-based biomarkers associated with manic episodes and lithium response in bipolar disorder
Bipolar disorder (BD) is one of the major mood disorders. A person afflicted with this neuropsychiatric disease undergoes episodes of depression and mania. BD ranks as the highest amongst all the mood disorders for having the most negative affect on a person's life. It is a lifelong illness, which requires constant monitoring and medication. From psychotic behaviours to suicide ideation, this disease is a burden on the afflicted and their families. Although, BD has been shown to have a high heritability factor, none of the studies done so far have been able to identify any biomarkers that could be causative for this disease. It is due to the fact that this disease is multifactorial, in which environment is a major contributing factor. Therefore, we believe that a more 'pattern-seeking' approach would lead to novel findings. We performed gene-set enrichment analysis (GSEA) with BD datasets that consisted of 3 different biological phenotypes; euthymic BD, manic BD and lithium treated BD samples. Since a long time, lithium has been used as 'the leading' drug to treat BD because it functions as a mood stabilizer. Therefore, we also included BD cohorts that had undergone lithium treatment in our analysis. As a result of GSEA, we were able to discover 2 novel patterns. In pattern 1, specific genes were found to up-regulated in euthymic BD and lithium treated cohorts, while the same genes were down-regulated in manic BD cohorts. In pattern 2, the opposite trend was observed, that is; another list of specific genes was down-regulated in euthymic BD and lithium treated cohorts, while they were found to be up-regulated in manic BD. The novelty of utilizing GSEA in our analysis was in the fact that we created our own custom gene-sets. The gene-sets were formed after performing differential gene expression (DEG) analysis on all the 3 types of BD datasets. The advantage of using custom gene-sets was that these were genes representing differential expression in different BD phenotypes, therefore, they were biologically relevant to BD. In the publically available curation of gene-sets on various databases, very few gene-sets represent BD. Hence, the custom gene-sets are more relevant and specific to the disease. Then we proceeded to extract core-enriched genes and performed further analysis, such as, plotting fold-change graphs and performing non-parametric tests. In light of these results, we propose putative biomarkers associated with manic episodes and lithium response in bipolar disorder. We put forth the hypothesis that these patterns can diagnose BD accurately, indicate if a patient is responding to lithium or not, and predict an oncoming manic episode. Therefore, we propose the genes adhering to the discovery patterns as putative biomarkers. Simultaneously, we performed biological analyses and literature review with the list of putative biomarkers. We found out inflammation as a potential underlying cause of pathogenicity in BD. We hypothesize that the process of inflammation is disrupted in BD patients, especially between the different mood states and that in order to treat this disease, inflammation as a pathway should also be targeted. We highlight TNFa as one of the main cytokines that 3 of our biomarkers; ADAMTS9, IL-1 B and STCJ are associated through various pathways, and this disruption of the inflammatory pathway may occur due to alteration in TNFa and biomarkers' levels. We hope that after further, subsequent research, the inclusion of our biomarkers in the clinics will help with the issue of BD misdiagnosis, help save precious treatment time, help with the choice of medication, and help indicate a patient's mood state. Overall, it will help the clinicians to tailor the treatment to every patient's specific profile, making the it easier to design a more personalized treatment strategy.