This project addresses one of the top priorities from the National Action Alliance for Suicide Prevention Research Prioritization Task Force, namely, the question of How can we better or more optimally detect/predict risk? Our project will address this urgent need by identifying candidate biomarker signatures associated with suicidal ideation/suicide attempt, so that specific blood-based biomarkers can be developed to identify those most at risk. We will use computational analyses of patient transcriptomes to identify relevant RNA editing biomarkers associated with suicide risk. We will use whole-blood transcriptome samples collected from participants who experienced recent suicidal ideation and sex/age-matched healthy controls to elucidate specific differences in RNA editing patterns using computational pipeline developed in Piontkivska lab (Plonski, et al. 2020; Plonski, et al. 2021). These editing profiles will be analyzed to determine whether dynamic RNA editing dysregulation is associated with suicidal ideation risk/onset, and whether changes in RNA editing patterns can serve as biomarkers of suicidal ideation and/or recovery.
The team combines the computational expertise of the PI, Dr. Piontkivska, who developed bioinformatics tools to understand changes in molecular RNA editing profiles in neuropsychiatric disorders, including in suicides, and complementary expertise in mental health and human subjects recruitment of the co-PIs, Dr. Kenne, Dr. Laurene, and Dr. Dalman. Our partnership also includes co-PI Gannon from the Community Health Center Addiction Services (CHC), a major regional health care provider. As a clinical counselor and Chief Clinical Officer at CHC, unique expertise of Ms. Gannon will allow us to recruit participants with a recent history of suicidal ideation and/or suicidal behavior. CHC will also play an essential role in synthesizing and interpreting clinical, mental health and other relevant socioeconomic data for participants with suicidal ideations and those in recovery, thus, allowing us to integrate such non-genomic variable into genomic-based machine learning models for building comprehensive profiles. We will also work closely with Dr. Rossi, M.D./Ph.D., a neurologist at the Summa Health, and Dr. McVoy, M.D., an associate professor of psychiatry at the University Hospitals, a practicing child psychiatrist and a member of the American Academy of Child and Adolescent Psychiatry. Drs. McVoy and Rossi will assist the team with the interpretation of medical records, including co-morbidities and medication histories.
Our own preliminary work and published studies of others have shown feasibility of using RNA (ADAR) editing patterns of a handful of specific genes as biomarkers of neuropsychiatric disorders, including some suicidal ideation. In this project we will extend these findings onto a broader set of genes, including key neural players, to identify a comprehensive molecular signature of editing as suicide risk and/or recovery biomarker. Furthermore, our novel and powerful bioinformatics approach will deepen our understanding of mechanistic molecular changes that are contributing to suicide risk, and can potentially facilitate development of novel treatment approaches informed by comprehensive editing profiles.
