New Findings in Pharmacogenomics of Neuropsychiatric Disorders

Dear Colleagues,

Mental and psychiatric disorders, as defined by the World Health Organization (WHO), rank among the most significant global disease burdens. They are continuing to increase, for various reasons, including the inadequate response of patients to the drug treatments currently available on the market. Different patients may exhibit varying responses to the same drug due to genomic variations in their DNA. Pharmacogenomics (PGx) plays a pivotal role in the identification of the genetic markers responsible for these individual differences. Many drugs used in the treatment of psychiatric disorders display an average correlation between clinical responses and plasma/serum concentrations. Recent meta-analyses of randomized controlled trials demonstrated that major-depressive patients who received PGx-guided treatment achieved 71% greater efficacy and symptom remission compared to those receiving standard prescriptions. Other studies have explored potential links between the genotype-predicted activity scores of key pharmacogenes related to neuropsychiatric conditions and the improvement of symptoms and side effects, as reported by pediatric and adolescent patients undergoing treatment with relevant medications. This Special Issue aims to bring together novel research findings that contribute to the field's understanding of the pharmacogenomics of neuropsychiatric disorders. By fostering discussions on genetic markers, treatment efficacy, and individualized approaches, this Special Issue aims to accelerate the translation of pharmacogenomic knowledge into clinical practice, ultimately improving the precision and effectiveness of drug treatments for individuals with neuropsychiatric disorders. This Special Issue also endeavors to spotlight the transformative role of cutting-edge genomic technologies in uncovering pharmacovariants relevant to neuropsychiatric disorders. These technologies, which include, but are not limited to, high-throughput sequencing, CRISPR-based technologies, and advanced bioinformatics tools, could pave the way to a more comprehensive exploration of the genetic landscape that influences drug responses.

Dr. Alireza Tafazoli
Dr. Mandana Hasanzad
Guest Editors

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• pharmacogenomics
• genome informatics
• advanced DNA-sequencing technologies
• bioinformatics of pharmacogenomics
• diagnostic approaches in the pharmacogenomics of neuropsychiatric disorders

Title: Plasma Circular RNA STAG1 as a putative biomarker for the diagnosis in inflammatory major depressive disorder
Authors: Xiaoyu Yu1, #, Tingting Yang1, #, Shulei Gao1, Jiaming Shen2, Heng Li1, Chenxue Xu1, Ao Sun1, Yachen Shi3, Xiaoying Wang1, *, Yan Lu4,*, Rongrong Huang5,*
Affiliation: 1Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China; 2Department of Pharmacy, Huai’an Maternal and Child Health Hospital, Huai’an 223022, China; 3Department of Neurology, Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People’s Hospital, Wuxi Medical Center, Nanjing Medical University, Wuxi 214023, China; 4Department of Anesthesiology, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204, China; 5Department of Pharmacy, Affiliated Hospital of Nantong University, Nantong 226001, China
Abstract: Background: It is possible that chronic low-grade inflammation contributes to depression’s pathophysiology. Identifying patients with inflammatory depression can be highly beneficial. Circular RNA STAG1 (circSTAG1) has been reported to be negatively correlated with the severity of depression in major depression (MD) patients. Therefore, the purpose of this study was to determine if circSTAG1 could serve as a potential biomarker or predictor of inflamed major depression (IMD) and guide antidepressive treatment. Method: This study included 122 patients, including 35 patients with IMD (defined by highly sensitive C-reactive protein >3 mg/L) and 87 patients with uninflamed major depression (UIMD). Quantitative real-time polymerase chain reaction (qPCR) was used to quantify circSTAG1 copy number in plasma. Interleukin (IL)-1, IL-6, and tumor necrosis factor-alpha (TNF-α) were assayed by enzyme-linked immunosorbent assay (ELISA). To investigate potential sources of circSTAG1 in plasma, a platelet spike-in experiment and a correlation analysis were performed. Results: Compared with UIMD patients, a notably reduced level of circSTAG1 was observed in IMD patients. CircSTAG1 had a positive predictive value with an area under the curve (AUC) at 0.745 and sensitivity, specificity at 62.10%, 77.10% respectively in predicting IMD. Additionally, circSTAG1 showed significantly correlation with the pro-inflammatory cytokines IL-6, IL-1β, and TNF-α in the plasma of IMD patients. Furthermore, the inclusion of circSTAG1 in the risk model resulted in a notable improvement in the AUC, increasing from 0.870 in model A to 0.894 in model B. Subsequent to a 14-day course of antidepressant therapy, a significant elevation in circSTAG1 levels was observed in individuals with IMD patients who exhibited a positive response to the treatment. Additionally, a greater reduction in HAMD-24 scores following treatment was correlated with larger increases in circSTAG1 levels in IMD patients. Specifically, a marked decrease in circSTAG1 levels was observed in neutrophils of IMD patients compared to those with UIMD. Conclusion: CircSTAG1 could be identified as a potential biomarker or predictor of IMD and aid in guiding anti-depressive treatment. Furthermore, our findings lend support to the hypothesis that the inflamed subphenotype of depression appears to be a meaningful construct in the present study.