Bipolar disorder is a chronic and often severe psychiatric disorder with a strong genetic basis. However, even with significant progress made by molecular genetic studies, the few genetic variants associated with the disorder explain only a small portion of its heritability, which led to the hypothesis of the involvement of epigenetic alterations in BD?s pathophysiology. While recent studies have attempted to identify epigenetic biomarkers in BD, most results are limited due to the tissue specificity of epigenetic alterations and the biased assessments of candidate genes or genome-wide investigations through microarrays. Since the search for biomarkers have repetitively focused on peripheral measures that do not necessarily reflect brain alterations, significant biologically- and clinically-relevant findings have been significantly hindered. This study will fill this important gap by measuring epigenetic markers in plasma-derived extracellular vesicles (EVs) released by the brain. Specifically, we will focus on microRNAs and other non-coding RNAs that have been recently proposed to mediate important mechanisms in BD and may integrate gene and environment stimuli. Of note, microRNAs- filled EVs are released by the neural tissue as a method of cell-to-cell communication and may be the key players in transferring epigenetic markers to germ cells, ultimately contributing to the inter- and transgenerational transmission of BD. Our working hypothesis is that patients with BD will show alterations in specific miRNAs and other non-coding RNA transcripts in plasma neural-derived EVs compared to controls. To test this, we will initially identify neural-specific EVs microRNAs in BD by analyzing blood samples from 60 healthy controls and 60 BD type I patients (already collected and stored in our Department) (Aim 1). After neural-derived EVs immunoprecipitation and characterization, RNA will be isolated and next generation sequencing libraries will be prepared and sequenced on an Illumina NextSeq instrument with 1x75 bp single-end reads at an approximate depth of 10-15 million reads per sample. Significantly altered miRNAs will be validated by real-time PCR and the putative biological relevance of the differentially expressed transcripts will be assessed by functional pathway analyses. In addition, a machine learning model will be built using the miRNA expression data in order to predict whether an individual sample belongs to the BD or control group, allowing for the establishment of a clinically useful predictive biosignature that can have immediate impact in the field. On a second step we will investigate the correlation between neuronal EVs transcriptome markers with clinical, neuroanatomical, and neurocognitive parameters available for each subject, with the ultimate goal of identifying the clinical relevance of the newly- identified EVs markers in endophenotypes of illness (Aim 2). The identification of such brain-specific markers will likely open new avenues for scientific investigation of BD.
The proposed research is relevant to public health because bipolar disorder is a chronic and highly disabling condition with a strong familial aggregation, yet non-genetic mechanisms (such as microRNAs) underlying the disorder are still fairly unknown. The investigation of plasma neural-derived extracellular vesicles in bipolar disorder patients and controls and the development of a machine learning algorithm for the identification of a microRNA biosignature can lead to the identification of clinically relevant peripherally measured brain-specific biomarkers that can improve our understanding of the disorder and inform the development of personalized treatments.
