¹The University of Alabama at Birmingham, Psychiatry and Behavioral Neurobiology, Birmingham, United States, ²Shanghai University of Traditional Chinese Medicine, Institute of Chinese Materai Medica, Shanghai, China, ³The University of Alabama at Birmingham, Department of Psychiatry and Behavioral Neurobiology, Birmingham, United States, ⁴McGill University, Montreal Neurological Institute and Hospital, Montreal, Canada

Background: Major depressive disorder (MDD) is a common but serious mental illness and one of the mental illnesses associated with suicide. Both MDD and suicide are complex diseases affected by genetics, environment and their interactions. From the omics level, exploring the etiology of complex diseases plays an important role in discovering new therapeutic targets and identifying potential biomarkers for high-risk individuals. The rapid development of high-throughput sequencing technology, especially the emergence of single-cell sequencing, spatial transcriptome sequencing and proteomics technology in recent years, has been applied to the study of the biological mechanisms of depression and suicide. Using new omics technologies, researchers have generated a large amount of data at different omics levels from postmortem brain tissue, patient blood and animal models. These omics data have shown changes of the molecules at the genome-wide level and also reflect the biological state of the disease. Artificial intelligence technology is another hot topic in the current research. More and more studies integrate advanced bioinformatics analysis and artificial intelligence methods to study the development of MDD and the mechanism of suicide.

Aims & Objectives: To discover potential molecular targets and effective biomarkers to prevent suicide

Method: We focused on several hot issues in the field of depression and suicide, including depression and psychological resilience, gender differences, early life stress, comorbidity of depression and suicidal behavior, and conducted a combination of dry and wet research studies to understand the development of mood diseases.

Results: We will present the following research results: 1) Establishing a new integrated method including transcriptome data analysis and machine learning; 2) Identifying phenotype-specific or overlapping molecules, modules, and biological pathways related to depression, resilience, early life stress, sex differences, etc.; 3) Studying the gene expression patterns of epigenetic regulation in human depression brain tissue

Discussion & Conclusions: By establishing novel integrated analysis, we reported some novel molecular targets and biomarkers . On the basis of those study, we will further explore the spatial expression patterns and the rules of epigenetic regulation based on chromosomes of depression and suicide brain tissue.