Integrative Analyses of Transcriptomes to Explore Common Molecular Effects of Antipsychotic Drugs.
Trang T T TruongChiara C BortolasciSrisaiyini KidnapillaiBriana SpoldingBruna PanizzuttiZoe S J LiuJee Hyun KimOlivia M DeanMark F RichardsonMichael BerkKen R WalderPublished in: International journal of molecular sciences (2022)
There is little understanding of the underlying molecular mechanism(s) involved in the clinical efficacy of antipsychotics for schizophrenia. This study integrated schizophrenia-associated transcriptional perturbations with antipsychotic-induced gene expression profiles to detect potentially relevant therapeutic targets shared by multiple antipsychotics. Human neuronal-like cells (NT2-N) were treated for 24 h with one of the following antipsychotic drugs: amisulpride, aripiprazole, clozapine, risperidone, or vehicle controls. Drug-induced gene expression patterns were compared to schizophrenia-associated transcriptional data in post-mortem brain tissues. Genes regulated by each of four antipsychotic drugs in the reverse direction to schizophrenia were identified as potential therapeutic-relevant genes. A total of 886 genes were reversely expressed between at least one drug treatment (versus vehicle) and schizophrenia (versus healthy control), in which 218 genes were commonly regulated by all four antipsychotic drugs. The most enriched biological pathways include Wnt signaling and action potential regulation. The protein-protein interaction (PPI) networks found two main clusters having schizophrenia expression quantitative trait loci (eQTL) genes such as PDCD10 , ANK2, and AKT3 , suggesting further investigation on these genes as potential novel treatment targets.
Keyphrases
- genome wide
- bipolar disorder
- drug induced
- gene expression
- genome wide identification
- liver injury
- dna methylation
- bioinformatics analysis
- protein protein
- genome wide analysis
- transcription factor
- signaling pathway
- endothelial cells
- risk assessment
- emergency department
- white matter
- brain injury
- blood brain barrier
- multiple sclerosis
- high resolution
- human health
- combination therapy
- deep learning
- subarachnoid hemorrhage
- binding protein