ADAR‐editing Landscapes Predict Biomarkers for Neuropsychiatric Disorders
Authors: Plonski Noel-Marie , Meindl Richard , Damron Derek , Piontkivska Helen 
Published: April 2020

Background

Currently suicide is the 10th leading cause of death in the United States. Despite increased awareness and availability of new drug therapies, the pathology of suicidal ideation remains elusive. Suicidal ideation is often a comorbidity associated with other neuropsychiatric and neurological disorders, including but not limited to, major depressive disorder, schizophrenia, and viral infections with neurological sequelae. In addition, interferon alpha therapy used in the treatment of chronic viral infections has a significantly increased risk of suicide and aggressive behaviors. One potential biological mechanism underlying suicidal ideation may be attributed to spatio‐temporal changes to brain transcriptome diversity. Transcriptome diversity is critical for brain development and contributes to “fine‐tuning” of signaling and other processes involved in neural plasticity. A major player in regulating transcriptome diversity is adenosine deaminase acting on RNA (ADAR), an enzyme that deaminates selected adenosine (A) residues to inosine (I), which later are interpreted by transcriptional machinery as a guanine (G). ADAR expression is in part controlled by innate immune activation, which in turn can lead to dysregulation of normal editing patterns resulting in neurological symptoms including mood instability and suicidal ideation.

Methods

We use publicly available RNA sequencing datasets of prefrontal cortex samples from brains of patients with major depressive disorder who have committed suicide to infer changes in ADAR editing landscapes. Recently developed computational pipeline AIDD (Automated Isoform Diversity Detector) is used to map and contrast ADAR expression and editing patterns across samples using random forest, hierarchal clustering, Guttman scale patterns, and principal component analysis.

Results

ADAR editing sites can be found in dozens of genes, including ion channels, such as the glutamate receptor GRIA2, and in other genes with known functions in cell division and growth pathways involved in neurodevelopment and neurotransmitter processes critical for neural plasticity, memory formation, and ion homeostasis. Notably, we have confirmed differential editing patterns in the serotonin receptor subunit 2C (5HTR2C), a known player in depression, where 5 editing sites can be edited in different combinations to create variant isoforms with different binding affinities and signaling abilities. These changes to the brain excitome, including in 5HTR2C and 147 other genes with known and/or predicted ADAR editing sites, can offer insight into the molecular mechanism of action in neuropsychiatric disorders, including elucidating the potential role of infection‐triggered ADAR editing changes.

Conclusions

Machine learning techniques, including random forest, and Guttman scale patterns can be used to study ADAR editing landscapes, ultimately predicting biomarkers that can be used in a clinical setting for diagnosis, prognosis, and as therapeutic drug targets to treat major depressive disorder and suicidal ideation.

Support or Funding Information

Funded by Kent State University Brain Health Research Institute to H. P. and D. D.