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Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.
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http://dx.doi.org/10.1016/j.pnpbp.2022.110688 | DOI Listing |
J Appl Toxicol
September 2025
Department of Drug and Cosmetics Technology, Faculty of Pharmaceutical Sciences in Sosnowiec, Medical University of Silesia in Katowice, Sosnowiec, Poland.
Phenothiazine derivatives have been used for decades as antipsychotic drugs in multiple mental health and physical conditions treatment (schizophrenia, mania in bipolar disorder, and psychosis). Epidemiological studies have shown that people with schizophrenia are less likely to suffer from cancer, which indicates the ability of antipsychotics to inhibit the development of cancer cells. It is our third review about the impact of phenothiazine derivatives on cell death.
View Article and Find Full Text PDFJ Affect Disord
September 2025
Department of Psychiatry and Behavioural Neurosciences, McMaster University, Hamilton, ON, Canada; Peter Boris Centre for Addictions Research, St. Joseph's Healthcare Hamilton, Hamilton, ON, Canada; Seniors Mental Health Program, Department of Psychiatry and Neurosciences, McMaster University, Hamil
Electroencephalography (EEG) is a comparatively inexpensive and non-invasive recording technique of neural activity, making it a valuable tool for biomarker discovery in transcranial magnetic stimulation (TMS). This systematic review aimed to examine mechanistic and predictive biomarkers, identified through TMS-EEG or resting-state EEG, of treatment response to TMS in psychiatric and neurocognitive disorders. Nineteen articles were obtained via Embase, APA PsycInfo, MEDLINE, and manual search; conditions included, unipolar depression (k = 13), Alzheimer's disease (k = 3), bipolar depression (k = 2), and schizophrenia (k = 2).
View Article and Find Full Text PDFBrain Behav
September 2025
Pontificia Universidad Javeriana, Facultad De Ciencias, Departamento de Biología, Biología de Plantas y Sistemas Productivos, Bogotá, Colombia.
Introduction: The study explores shared genetic architecture among major psychiatric disorders-major depressive disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder-emphasizing their overlapping molecular pathways. Using public datasets, we identified shared genes and examined their functional implications through protein-protein interaction (PPI) networks and gene set enrichment analysis (GSEA).
Methods: Genes associated with each disorder were identified through the NCBI Gene database.
Neuropsychopharmacol Rep
September 2025
Department of Neuropsychiatry, School of Medicine, Fukushima Medical University, Fukushima, Japan.
Controlling for confounding factors in postmortem brain studies of psychiatric disorders is crucial, particularly in gene expression analyses. Potential confounding factors include sex, age at death, medication history, agonal state, postmortem interval (PMI), tissue storage duration, tissue pH, and RNA integrity number (RIN). pH and RIN are considered particularly important in gene expression analysis because they accurately reflect mRNA quality.
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