Identification of key modules in metabolic syndrome induced by second-generation antipsychotics based on co-expression network analysis.

Autor:	Sun Y; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China., Zhu C; Affiliated Psychological Hospital of Anhui Medical University, Hefei, China.; Anhui Clinical Center for Mental and Psychological Diseases, Hefei Fourth People's Hospital, Hefei, Anhui, China.; Anhui Mental Health Center, Hefei, Anhui, China., Huang L; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Luo C; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China., Ju P; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.; Shanghai Institute of Traditional Chinese Medicine for Mental Health, Shanghai, China., Chen J; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.; Shanghai Key Laboratory of Psychotic Disorders, Shanghai, China.; Shanghai Institute of Traditional Chinese Medicine for Mental Health, Shanghai, China.; Yueyang Hospital of Integrated Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Jazyk:	angličtina
Zdroj:	Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2024 Jan 05; Vol. 23, pp. 723-731. Date of Electronic Publication: 2024 Jan 05 (Print Publication: 2024).
DOI:	10.1016/j.csbj.2024.01.003
Abstrakt:	Background: Second-generation antipsychotics (SGAs) frequently cause metabolic syndrome (MetS), which raises the risk of heart disease, type 2 diabetes, morbid obesity, atherosclerosis, and hypertension. MetS also impairs cognitive function in patients with schizophrenia. However, the fundamental reasons of MetS caused by SGAs are not yet fully understood. Thus, we aimed to identify potential therapeutic targets for MetS induced by SGAs. Methods: The serum biochemical parameters and the RNA-sequencing of peripheral blood mononuclear cells were measured in three groups (healthy controls and patients with schizophrenia with and without MetS taking SGAs). The study of the weighted gene co-expression network was utilized to pinpoint modules that were significantly connected to clinical markers. Results: Statistical analysis showed significant differences in triglyceride and high-density lipoprotein among the three groups. The TNF signaling pathway, TGF-β signaling pathway, fatty acid metabolism, NF-kappa B signaling pathway, MAPK signaling pathway, and Toll-like receptor signaling pathway were the pathways that were primarily enriched in the two unique co-expression network modules that were found. Finally, five specific genes (TNF, CXCL8, IL1B, TIMP1, and ESR1) associated with metabolism and immunity pathways were identified. Conclusions: This study indicated that SGAs differentially induced MetS of patients with schizophrenia through metabolic and inflammation-related pathways. Therefore, the potential side effects of drugs on inflammatory processes need to be considered when using SGAs for the treatment of schizophrenia. Competing Interests: None of the authors of this manuscript have any conflicts of interests to report. (© 2024 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.)
Databáze:	MEDLINE
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