Adenosine deaminase acting on RNA (ADAR) editing plays a major role in shaping transcriptome diversity by creating variant isoforms that enable fine-tuning of calcium-mediated excitatory and other signaling needed for brain development, neural plasticity and mood regulation. The spatio-temporal ADAR editing landscapes are tightly regulated by controlling ADAR expression levels to preserve preferential binding and editing. Previous studies have shown that activation of the interferon pathways of the innate immune system ? such as those seen in viral infections - leads to increased expression of ADAR1p150, which ultimately results in changes to ADAR editing patterns. Furthermore, common side effects to innate immune activation by interferon alpha therapies include increased risk of depression and suicide. The changes in spatio-temporal regulation of editing patterns can lead to a wide spectrum of neurological symptoms, including neuropsychiatric disorders (e.g., decreased ADAR editing in the serotonin receptor subunit2C in the prefrontal cortex observed in individuals who commit suicide). Yet, our understanding of ADAR editing landscapes remain cursory. Advances in high throughput RNA-seq enable more accurate variant calling from the sequencing reads, providing a way to map ADAR editing patterns in the transcriptome. However, there are no computational pipelines focused on ADAR editing that are easy to use, are reproducible and can handle large scale analysis. I have recently built a pipeline to handle meta-analysis of RNA-seq data that incorporates variant calling steps, but further work is needed to validate this tool to assure accuracy and reproducibility of results. It can then be used to map the spatio-temporal variation of ADAR editing landscapes. The proposed project will study ADAR editing landscapes in the following ways: (i) new computational pipelines will be benchmarked to use variant calling with RNA-seq datasets using simulated reads, (ii) ADAR editing landscape diversity in the publicly available human samples will be mapped; the computational predictions and hypotheses generated from the pipeline will be validated using (iii) measuring calcium flux in cells with known differential ADAR editing landscapes caused by PolyI:C (viral infection mimic) treatment. The proposed work will yield a validated pipeline capable of mapping ADAR editing landscapes with machine learning algorithms. Defining ADAR editing landscapes is paramount to biomarker discovery and can influence precision medicine applications in diagnosis and treatment of neuropsychiatric disorders. This project will allow for me to gain the knowledge base necessary to become an independent researcher with a unique skill set of both computational and benchwork methods to advance the field of neuroscience.
Inferring ADAR editing landscapes and their link with ion homeostasis and excitatory signaling in the brain is important for understanding, diagnosing or staging neurological and neuropsychiatric disorders, including major depressive disorder and suicide. This proposed project will develop and validate computational tools to use RNA-seq from publicly available datasets for ADAR editing inferences and to delineate patterns of editing changes in cells experiencing viral infections. Overall, this project will give me the training to build my unique skill set of both computational and experimental methods that will enable me as an independent researcher to bridge the gap between bioinformatics and experimental researchers and translate my findings into precision medicine.
