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). Decreases in metabolic transcripts included lactate dehydrogenase A (LDHA), nicotinamide adenine dinucleotide dehydrogenases (NADH), and ATP synthases. Interestingly, studies using cell-level techniques have demonstrated complex bioenergetic changes, including decreases in mitochondrial oxidative energy metabolism genes in dentate granule neurons (n=22) and in layer 3 and 5 pyramidal neurons from the DLPFC (n=36 and n=19)( 15– 17). There is also evidence for genetic linkage between enzymes that control glycolysis in schizophrenia including 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 2 (PFKFB2) and hexokinase 3 (HK3), suggesting that genetic risk for this illness includes bioenergetic substrates ( 14).
There is also accumulating evidence of bioenergetic dysfunction in chronic schizophrenia, which has recently been reviewed and highlights a number of abnormalities associated with glucose metabolism, the lactate shuttle, and bioenergetic coupling ( 13). A variety of abnormalities have been reported in the DLPFC of schizophrenia patients, including changes in gene expression, cell density, receptor binding, and cerebral blood flow ( 10– 12). The dorsolateral prefrontal cortex (DLPFC) is a brain region heavily implicated in the pathophysiology of schizophrenia, possibly contributing to defects in working memory ( 9). Cognitive impairment is a hallmark feature of the illness, and antipsychotics have poor efficacy in treating cognitive decline ( 7, 8).
and displays a wide range of symptoms ( 1– 6). Schizophrenia is a devastating illness that affects over 2 million people in the U.S. Taken together, our findings suggest that tailored bioinformatics approaches, coupled with the LINCS library of transcriptional signatures of chemical and genetic perturbagens may be employed to identify novel treatment strategies for schizophrenia and related diseases. We administered a PPAR agonist to the GluN1 knockdown model of schizophrenia and found it improved long-term memory. Finally, with the goal of identifying drugs capable of “reversing” the bioenergetic schizophrenia signature, we performed a connectivity analysis with iLINCS and identified peroxisome proliferator-activated receptor (PPAR) agonists as promising therapeutic targets. Next, we used a novel strategy to build a schizophrenia bioenergetic profile by a tailored application of the Library of Integrated Network-Based Cellular Signatures data portal (iLINCS) and investigated connected cellular pathways, kinases, and transcription factors using Enrichr. To replicate these novel bioenergetic findings, we probed independent datasets for bioenergetic targets and found similar abnormalities. We utilized a cell-level approach to examine glycolytic pathways in the DLPFC of subjects with schizophrenia (n=16) and control (n=16) subjects and found decreased mRNA expression of glycolytic enzymes in pyramidal neurons, but not astrocytes.