Gene expression overlap between neuropsychiatric disorders
Panzenhagen, A. C.; Alves-Teixeira, A.; Wissmann, M. S.; Girardi, C. S.; Santos, L.; Silveira, A. K.; Gelain, D. P.; Moreira, J. C. F.
Show abstract
Common diseases result from a mix of genetic and environmental factors, often involving inflammation. Complex traits like diabetes and psychiatric disorders are polygenic, influenced by many genetic variants. The omnigenic model suggests all expressed genes can impact disease-related genes. This study examines blood transcriptomic variations in psychiatric and neurological disorders to understand mRNA expression profiles and address field discrepancies. Animal models are explored for similar gene expressions. This study extensively searched GEO DataSets and ArrayExpress databases, identifying gene expression profiles associated with neuropsychiatric disorders. From GEO, 10,359 samples were found, with 30 series (1,897 samples) in the qualitative synthesis, revealing 1,364 differentially expressed genes in Schizophrenia, 134 in Bipolar Disorder, 11 in Autism Spectrum Disorder, and 2,784 in Alzheimers Disorder. Comparisons with GWAS studies unveiled overlaps, with 81 genes for SCZ, two for BD, and 135 for ALZ. Notably, 441 genes were shared between ALZ and SCZ. Enrichment analyses indicated associations with signalling pathways. In animal models, 2,360 series were identified, with 175 in the qualitative synthesis, resulting in a meta-analysis focusing on ALZ with hippocampus tissue, revealing 14 consistently differentially expressed genes. Four overlapped with human data (ALOX5AP, P2RY13, RGS10, SH3GL1). These findings contribute to understanding shared and unique molecular signatures across neuropsychiatric disorders, bridging insights between human and animal models. The study efficiently identifies and tests consistent differentially expressed genes in psychiatric and neurological disorders, focusing on blood transcriptomes. Compared to transcriptome-wide or proteome-wide association studies, this approach analyses transcripts directly from individuals with disorders, offering real-world predictive capability. Shared genes between disorders suggest common molecular pathways, emphasizing the need for interdisciplinary approaches in understanding and treating psychiatric disorders. Limitations include sample characterization and the peripheral marker focus. Further investigations, including functional assays, are crucial for validation and extending these findings.
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