Clozapine and Metabolic Syndrome: Evidence from Integrative Bioinformatics and Molecular Simulation

Abstract Clozapine is an effective treatment for treatment-resistant schizophrenia, but its clinical use is limited by the high risk of developing metabolic syndrome (MetS). This study aimed to identify potential molecular targets and biological mechanisms underlying clozapine-induced MetS. We integrated clozapine-related targets, MetS‑related genes, and differentially expressed genes from the GSE98895 dataset to identify candidate targets. We used functional enrichment analysis and machine learning algorithms, including least absolute shrinkage and selection operator, random forest, support vector machine recursive feature elimination, and extreme gradient boosting, to identify hub genes. We then performed gene set enrichment analysis, gene set variation analysis, and immune cell composition analysis to characterize the biological and immunological features associated with these genes. Subsequently, we used molecular docking, molecular dynamics simulations, and molecular mechanics/Poisson–Boltzmann surface area analysis to evaluate potential interactions between clozapine and the identified proteins. Thirteen overlapping targets were identified and were primarily enriched in pathways related to T helper (Th) 1 and Th2 cell differentiation, Th17 cell differentiation, and necroptosis. Janus kinase 1 ( JAK1 ) and poly (ADP-ribose) polymerase 1 ( PARP1 ) were consistently selected by all machine learning algorithms. JAK1 expression was decreased, whereas PARP1 expression was increased, in individuals with MetS, and the combined model achieved an area under the receiver operating characteristic curve of 0.895. Pathway analyses implicated alterations in immune signaling, mitochondrial energy metabolism, carbohydrate metabolism, circadian regulation, and cellular homeostasis. The MetS group also showed altered proportions of CD8 + T cells, resting natural killer cells, and resting memory CD4 + T cells. Structural analyses suggested that clozapine formed energetically favorable interactions and conformationally stable complexes with JAK1 and PARP1. Collectively, these findings identify JAK1 and PARP1 as candidate genes potentially involved in clozapine-induced MetS and provide testable hypotheses regarding the associated metabolic and immune mechanisms.

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Publication Details

Journal
Research Square
Published
2026-11-02
DOI
https://doi.org/10.21203/rs.3.rs-11327477/v1
Primary Topic
Computational Drug Discovery Methods
Type
preprint
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Clozapine and Metabolic Syndrome: Evidence from Integrative Bioinformatics and Molecular Simulation

Ranli Li, Chen Xi-ming, Chuanjun Zhuo, Hongjun Tian et al.
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Research Square
Computational Drug Discovery Methods
preprint

Clozapine and Metabolic Syndrome: Evidence from Integrative Bioinformatics and Molecular Simulation

Ranli Li, Chen Xi-ming, Chuanjun Zhuo, Hongjun Tian, Xiaoyan Ma
preprint en

Abstract

Abstract Clozapine is an effective treatment for treatment-resistant schizophrenia, but its clinical use is limited by the high risk of developing metabolic syndrome (MetS). This study aimed to identify potential molecular targets and biological mechanisms underlying clozapine-induced MetS. We integrated clozapine-related targets, MetS‑related genes, and differentially expressed genes from the GSE98895 dataset to identify candidate targets. We used functional enrichment analysis and machine learning algorithms, including least absolute shrinkage and selection operator, random forest, support vector machine recursive feature elimination, and extreme gradient boosting, to identify hub genes. We then performed gene set enrichment analysis, gene set variation analysis, and immune cell composition analysis to characterize the biological and immunological features associated with these genes. Subsequently, we used molecular docking, molecular dynamics simulations, and molecular mechanics/Poisson–Boltzmann surface area analysis to evaluate potential interactions between clozapine and the identified proteins. Thirteen overlapping targets were identified and were primarily enriched in pathways related to T helper (Th) 1 and Th2 cell differentiation, Th17 cell differentiation, and necroptosis. Janus kinase 1 ( JAK1 ) and poly (ADP-ribose) polymerase 1 ( PARP1 ) were consistently selected by all machine learning algorithms. JAK1 expression was decreased, whereas PARP1 expression was increased, in individuals with MetS, and the combined model achieved an area under the receiver operating characteristic curve of 0.895. Pathway analyses implicated alterations in immune signaling, mitochondrial energy metabolism, carbohydrate metabolism, circadian regulation, and cellular homeostasis. The MetS group also showed altered proportions of CD8 + T cells, resting natural killer cells, and resting memory CD4 + T cells. Structural analyses suggested that clozapine formed energetically favorable interactions and conformationally stable complexes with JAK1 and PARP1. Collectively, these findings identify JAK1 and PARP1 as candidate genes potentially involved in clozapine-induced MetS and provide testable hypotheses regarding the associated metabolic and immune mechanisms.

Research Square
Nankai University (CN), Tianjin Anding Hospital (CN), Tianjin Fourth Hospital (CN), Tianjin Medical University (CN)
Good health and well-being
Computational Drug Discovery Methods
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