Project Summary/Abstract Prevention of suicidal behavior, especially among patients with mood disorders, is of uttermost importance among mental health professionals. Patients with bipolar disorder (BD), which affects around 1-2% of the population, are at a particularly high risk for developing suicidal behavior, with 25-50% of patients making at least one suicide attempt in their lifetime. In addition, although there is evidence that BD and suicide share a genetic risk and that stress may play a key role in suicidality, the mechanisms by which some patients are more vulnerable to suicidal behavior than others are vastly unknown. Guided by strong preliminary data, we hypothesize that epigenetic mechanisms, including DNA methylation, may underlie suicide risk among BD patients. Moreover, DNA methylation markers may provide clinically-relevant biomarkers for the identification of patients at highest risk for suicidal behavior. These hypotheses will be tested by completing three specific aims. In Aim 1, we will characterize DNA methylation alterations with the latest-generation Illumina MethylationEPIC BeadChip 850K platform in a discovery sample of neurons isolated from post-mortem dorsolateral prefrontal cortex tissues from patients with BD that committed suicide or died of other causes and controls. Promising markers will be validated by oxidative bisulfite pyrosequencing and further explored by pathway analyses. In Aim 2, we will investigate the coordinated changes between genetic risk for BD and suicide attempt and DNA methylation alterations in post-mortem prefrontal cortex using the same cohort as Aim 1 and a replication cohort from the UTHealth Brain Collection for Research in Psychiatric Disorders. In Aim 3, we will analyze the clinical correlates of methylome markers of suicide in an independent cohort of adult patients with BD that have previously attempted to commit suicide or not, compared to healthy controls, from which a comprehensive dataset with demographic, clinical, and neuroanatomical data is available, as well as genome-wide methylation and genotyping data. Specific targets and pathways predicting suicidal behavior will be further explored by sophisticated statistics and bioinformatics tools while controlling for co-variables known to associate with suicidal risk, followed by the development of machine learning algorithms for the prediction of suicidality at the individual level. Of note, the overarching goal of this K01 is to further the PI’s expertise in the biology and clinical aspects of suicide, bioinformatics, post-mortem analyses, and epigenomics, which will ensure a methodologically strong foundation to launch an independent lab in psychiatric epigenetics. Importantly, a strong group of mentors and collaborators with a remarkable track record in training junior faculty has been selected to provide intellectual and technical input during the award period. In addition, the outstanding resources, facilities, and multidiscip...