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

Our in silico lookup analyses of metabolic targets in schizophrenia also present challenges. The SMRI database analysis function performs a meta-analysis across 12 independent studies, combining molecularly and functionally distinct brain regions. It is possible that for some genes, regions such as the cerebellum drive results, while more subtle differences in cortical regions are not reported. Additionally, both the SMRI and the MSSM databases were generated for whole brain regions, and are not cell-subtype specific. It is not surprising that . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/338392 doi: bioRxiv preprint many of our cell-subtype specific findings were not appreciated at the region-level in our lookup confirmation studies. Gene expression may be increased in certain cell types and decreased in others, with no net changes (10) . The SMRI database particularly raises concern due to the number of individual studies used for comparison, many of which have varying subject demographics. This notion is supported by the observation that many of our targets are differentially regulated at the cell-level in the frontal cortical neuron database (75) . Limitations aside, it will be interesting to probe these datasets (and more publicly accessible databases) for targets in pathways that were implicated in our Enrichr analyses (Table 3 ).