Differential ADAR editing landscapes in major depressive disorder and suicide.

Neuropsychiatric disorders including depression and suicide are becoming an increasing public health concern. The current COVID19 pandemic by contributing to the rising rates of both depression and suicide has only hastened our need for objective and reliable diagnostic biomarkers. These can aide clinicians in not only diagnosing, but also in development of treatment plans for depression and suicide. While differential gene expression analysis has highlighted the serotonin signaling cascadeamong other critical neurotransmitter pathways to underly the pathology of depression and suicide, the biological mechanisms remain elusive. Well studied adenosine deaminase action on RNA (ADAR) editing sites such as serotonin and glutamate receptors have been examined individually one at a time. Here we propose a novel approach to better understand molecular underpinnings of neuropsychiatric disorders. We take advantage of publically available RNA-seq datasets to map ADAR editing landscapes in a more global gene-centric view. We use a unique combination of Guttman scaling and random forest classification modeling to create, describe and compare ADAR editing profiles focusing on both temporal and biological sex differences. We use a subset of experimentally confirmed ADAR editing sites located in known protein coding regions, the excitome, to map ADAR editing profiles in Major Depressive Disorder (MDD) and suicide. Using Guttman scaling, we were able to describe significant changes in editing profiles across brain regions in males and females with respect to cause of death (COD) and MDD diagnosis. The temporal distribution of editing sites may provide insight into biological mechanisms under-pinning clinical symptoms associated with MDD and suicidal behavior. Additionally, we use random forest modeling including these differential profiles among other markers of global editing patterns in order to highlight potential biomarkers providing insight into changes in synaptic plasticity. Together these models identify potential prognostic, diagnostic and therapeutic biomarkers for MDD diagnosis and/or suicide.