Development of multiple proteomic marker panel and predictive model for the onset and progress of major psychiatric disorders
Poster C121, Wednesday, October%2010, 11:30%20am%20-%201:00%20pm, Essex%20Ballroom
Yunna Lee1, Hyeyoung Kim2,3, Junhee Lee1,2, Kangeun Lee1, Hyunsuk Shin4, Hyeyoon Kim5, Minah Kim1,2, Yong Min Ahn1,2, Jun Soo Kwon1,2, Dohyun Han4, Kyooseob Ha1,2; 1Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Republic of Korea, 2Department of Psychiatry and Behavioral Science, Seoul National University College of Medicine, Seoul, Republic of Korea, 3Department of Psychiatry, Inha University Hospital, Incheon, Republic of Korea, 4Proteomics core facility, Biomedical Research Institute, Seoul National University Hospital, Seoul, Republic of Korea, 5Proteomics core facility, Biomedical Research Institute, Seoul National University Hospital and Department of Pathology, College of Medicine ,Seoul National University, Seoul, Republic of Korea
Objective: The authors tried to develop a predictive model using multiple proteomic marker panel and ultra-high speed multiple proteomic marker analysis platform to predict the onset and progression of schizophrenia, bipolar disorder, and major depressive disorder among high-risk group of major psychiatric disorders. Methods: The study subjects were recruited from 8 psychiatric centers in South Korea from June 2017. Protein marker candidates were selected by data mining and extracted from blood samples of the subjects and control group by quantitative proteomics applying In-depth proteome profiling using Q-exactive Orbitrap mass spectrometer and Multiple Reaction Monitoring (MRM) technique using Triple Quadrupole Mass spectrometer. Proteins with different expression level between groups were selected using pair-wise analysis, and protein marker candidates that could distinguish each group were selected by ANOVA and pearson correlation-based hierarchical clustering. Results: 342 protein marker candidates were selected by data mining. 120 proteins with different expression level differentiating groups were extracted from blood samples of the subjects and control group. Overall, 428 protein marker candidates were constructed. Among these, 156 proteins were identified as proteins detectable by mass spectrometry through the blood proteome library. Simultaneous multiplex MRM platform for verification of proteomic markers and 150 proteomic marker panels for prospective cohort samples were constructed. Conclusions: Analysis of the proteomic marker candidates showed that each group was clustered with a considerably high correlation. Based on this results, clinical evaluation and proteomics of high-risk group will be integrated to develop a prediction model of the onset and progression of major psychiatric disorders.
Topic Area: Translational Research