Connectivity analyses of bioenergetic changes in schizophrenia: Identification of novel treatments

Courtney R. Sullivan; Catharine A. Mielnik; Sinead M. O’Donovan; Adam J. Funk; Eduard Bentea; Erica A.K. DePasquale; Zhexing Wen; Vahram Haroutunian; Pavel Katsel; Amy J. Ramsey; Jarek Meller; Robert E. McCullumsmith

Big biomedical data resources, such as LINCS, open new avenues for innovative analyses of primary data generated in conjunction with focused, hypothesis driven research being undertaken in individual research labs. Interdisciplinary studies that combine such resources and research expertise in neuroscience, bioinformatics, and data science can greatly enhance our ability to generate novel hypotheses and offer key insights into the mechanisms of human disease. The goal of this study was 4 parts: first, to further characterize changes in glycolytic enzyme expression at the cellular level in schizophrenia and to explore these findings in available in silico databases; second, to analyze the connectivity of bioenergetic pathology in schizophrenia by generating a schizophrenia bioenergetic profile in iLINCS and performing pathway analyses on panels of clustered genes that are highly differentially expressed across knockdown signatures; third, to generate a list of promising drug interventions with the goal of future preclinical testing; and fourth, to examine endophenotypes of schizophrenia following treatment with an FDA approved drug identified by our bioinformatic analyses in an animal model.