Antipsychotic drug use complicates assessment of gene expression changes associated with schizophrenia.
Anton SchulmannStefano MarencoMarquis P VawterNirmala AkulaAgenor LimonAjeet MandalPavan K AuluckYash PatelBarbara K LipskaFrancis J McMahonPublished in: Translational psychiatry (2023)
Recent postmortem transcriptomic studies of schizophrenia (SCZ) have shown hundreds of differentially expressed genes. However, the extent to which these gene expression changes reflect antipsychotic drug (APD) exposure remains uncertain. We compared differential gene expression in the prefrontal cortex of SCZ patients who tested positive for APDs at the time of death with SCZ patients who did not. APD exposure was associated with numerous changes in the brain transcriptome, especially among SCZ patients on atypical APDs. Brain transcriptome data from macaques chronically treated with APDs showed that APDs affect the expression of many functionally relevant genes, some of which show expression changes in the same directions as those observed in SCZ. Co-expression modules enriched for synaptic function showed convergent patterns between SCZ and some of the APD effects, while those associated with inflammation and glucose metabolism exhibited predominantly divergent patterns between SCZ and APD effects. In contrast, major cell-type shifts inferred in SCZ were primarily unaffected by APD use. These results show that APDs may confound SCZ-associated gene expression changes in postmortem brain tissue. Disentangling these effects will help identify causal genes and improve our neurobiological understanding of SCZ.
Keyphrases
- gene expression
- dna methylation
- genome wide
- poor prognosis
- prefrontal cortex
- bipolar disorder
- resting state
- rna seq
- newly diagnosed
- single cell
- end stage renal disease
- emergency department
- magnetic resonance
- binding protein
- chronic kidney disease
- ejection fraction
- long non coding rna
- magnetic resonance imaging
- machine learning
- cerebral ischemia
- prognostic factors
- bioinformatics analysis
- multiple sclerosis
- genome wide identification
- big data
- genome wide analysis
- contrast enhanced
- network analysis
- clinical evaluation