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

Decreases in metabolic transcripts included lactate dehydrogenase A (LDHA), nicotinamide adenine dinucleotide dehydrogenases (NADH), and ATP synthases. We have also recently demonstrated decreases in glycolytic enzymes (HK1, and phosphofructokinase muscle type, PFKM) and glucose transporters (GLUT1 and GLUT3) in pyramidal neurons, but not astrocytes, in the DLPFC of schizophrenia subjects (n=16) (18) . While examining the expression of individual targets is important, employing a signature based bioinformatics approach may help elucidate the pathophysiology of large biological networks. gene to determine if a knockdown signature has been catalogued in the database. By clustering the seed gene knockdown signatures in iLINCS, we are able to analyze the connectivity of the knockdown signatures (and thus establish a schizophrenia bioenergetic profile), identifying groups of L1000 genes that change concordantly following genetic perturbation. Furthermore, we can perform Enrichr analyses on these genes to gain perspective on bioenergetic pathology, describe potential multisystem pathological mechanisms, and inform pharmacological studies.