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Identifying 5 Common Psychiatric Disorders Associated Chemicals Through Integrative Analysis of Genome-Wide Association Study and Chemical-Gene Interaction Datasets.

ABSTRACT: Psychiatric disorders are a group of complex psychological syndromes whose etiology remains unknown. Previous study suggested that various chemicals contributed to the development of psychiatric diseases through affecting gene expression. This study aims to systematically explore the potential relationships between 5 major psychiatric disorders and more than 11 000 chemicals. The genome-wide association studies (GWAS) datasets of attention deficiency/hyperactive disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), major depression disorder (MDD), and schizophrenia (SCZ) were driven from the Psychiatric GWAS Consortium and iPSYCH website. The chemicals related gene sets were obtained from the comparative toxicogenomics database (CTD). First, transcriptome-wide association studies (TWAS) were performed by FUSION to calculate the expression association testing statistics utilizing GWAS summary statistics of the 5 common psychiatric disorders. Chemical-related gene set enrichment analysis (GSEA) was then conducted to explore the relationships between chemicals and each of the psychiatric diseases. We observed several significant correlations between chemicals and each of the psychiatric disorders. We also detected common chemicals between every 4 of the 5 major psychiatric disorders, such as androgen antagonists for ADHD (P value = .0098), ASD (P value = .0330), BD (P value = .0238), and SCZ (P value = .0062), and imipramine for ADHD (P value = .0054), ASD (P value = .0386), MDD (P value = .0438), and SCZ (P value = .0008). Our study results provide new clues for revealing the roles of environmental chemicals in the development of psychiatric disorders.

SUBMITTER: Cheng S

PROVIDER: S-EPMC7505178 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Astrocytes have many important functions in the brain, but their roles in psychiatric disorders and their responses to psychotropic medications are still being elucidated. Here, we used gene enrichment analysis to assess the relationships between different astrocyte subtypes, psychiatric diseases, and psychotropic medications (antipsychotics, antidepressants and mood stabilizers). We also carried out qPCR analyses and "look-up" studies to assess the chronic effects of these drugs on astrocyte marker gene expression. Our bioinformatic analysis identified gene enrichment of different astrocyte subtypes in psychiatric disorders. The highest level of enrichment was found in schizophrenia, supporting a role for astrocytes in this disorder. We also found differential enrichment of astrocyte subtypes associated with specific biological processes, highlighting the complex responses of astrocytes under pathological conditions. Enrichment of protein phosphorylation in astrocytes and disease was confirmed by biochemical analysis. Analysis of LINCS chemical perturbagen gene signatures also found that kinase inhibitors were highly discordant with astrocyte-SCZ associated gene signatures. However, we found that common gene enrichment of different psychotropic medications and astrocyte subtypes was limited. These results were confirmed by "look-up" studies and qPCR analysis, which also reported little effect of psychotropic medications on common astrocyte marker gene expression, suggesting that astrocytes are not a primary target of these medications. Conversely, antipsychotic medication does affect astrocyte gene marker expression in postmortem schizophrenia brain tissue, supporting specific astrocyte responses in different pathological conditions. Overall, this study provides a unique view of astrocyte subtypes and the effect of medications on astrocytes in disease, which will contribute to our understanding of their role in psychiatric disorders and offers insights into targeting astrocytes therapeutically.

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Identifying 5 Common Psychiatric Disorders Associated Chemicals Through Integrative Analysis of Genome-Wide Association Study and Chemical-Gene Interaction Datasets.

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