1
|
Dong T, Yu C, Mao Q, Han F, Yang Z, Yang Z, Pires N, Wei X, Jing W, Lin Q, Hu F, Hu X, Zhao L, Jiang Z. Advances in biosensors for major depressive disorder diagnostic biomarkers. Biosens Bioelectron 2024; 258:116291. [PMID: 38735080 DOI: 10.1016/j.bios.2024.116291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/25/2024] [Accepted: 04/09/2024] [Indexed: 05/14/2024]
Abstract
Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.
Collapse
Affiliation(s)
- Tao Dong
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China.
| | - Chenghui Yu
- Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China.
| | - Qi Mao
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Feng Han
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhenwei Yang
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhaochu Yang
- Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China
| | - Nuno Pires
- Chongqing Key Laboratory of Micro-Nano Transduction and Intelligent Systems, Collaborative Innovation Center on Micro-Nano Transduction and Intelligent Eco-Internet of Things, Chongqing Key Laboratory of Colleges and Universities on Micro-Nano Systems Technology and Smart Transducing, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Nan'an District, Chongqing, 400067, China
| | - Xueyong Wei
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Weixuan Jing
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Qijing Lin
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Fei Hu
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Xiao Hu
- Engineering Research Center of Ministry of Education for Smart Justice, School of Criminal Investigation, Southwest University of Political Science and Law, Chongqing, 401120, China.
| | - Libo Zhao
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| | - Zhuangde Jiang
- X Multidisciplinary Research Institute, School of Instrument Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
| |
Collapse
|
2
|
Luan X, Xing H, Guo F, Liu W, Jiao Y, Liu Z, Wang X, Gao S. The role of ncRNAs in depression. Heliyon 2024; 10:e27307. [PMID: 38496863 PMCID: PMC10944209 DOI: 10.1016/j.heliyon.2024.e27307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/19/2024] Open
Abstract
Depressive disorders have a significant impact on public health, and depression have an unsatisfactory recurrence rate and are challenging to treat. Non-coding RNAs (ncRNAs) are RNAs that do not code protein, which have been shown to be crucial for transcriptional regulation. NcRNAs are important to the onset, progress and treatment of depression because they regulate various physiological functions. This makes them distinctively useful as biomarkers for diagnosing and tracking responses to therapy among individuals with depression. It is important to seek out and summarize the research findings on the impact of ncRNAs on depression since significant advancements have been made in this area recently. Hence, we methodically outlined the findings of published researches on ncRNAs and depression, focusing on microRNAs. Above all, this review aims to improve our understanding of ncRNAs and provide new insights of the diagnosis and treatment of depression.
Collapse
Affiliation(s)
- Xinchi Luan
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
- Department of Clinical Medicine, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Han Xing
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
- Department of Clinical Medicine, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Feifei Guo
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
| | - Weiyi Liu
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
- Department of Clinical Medicine, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Yang Jiao
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
- Department of Clinical Medicine, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Zhenyu Liu
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
- Department of Clinical Medicine, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Xuezhe Wang
- Department of Physiology and Pathophysiology, School of Basic Medicine, Qingdao University, Qingdao, Shandong, China
- Department of Clinical Medicine, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| | - Shengli Gao
- Biomedical Center, Qingdao Medical College, Qingdao University, Qingdao, Shandong, China
| |
Collapse
|
3
|
Wang K, Yang Y, Wang Y, Jiang Z, Fang S. CircPTK2 may be associated with depressive-like behaviors by influencing miR-182-5p. Behav Brain Res 2024; 462:114870. [PMID: 38266777 DOI: 10.1016/j.bbr.2024.114870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 01/26/2024]
Abstract
BACKGROUND Major depressive disorder (MDD) is a severe psychiatric disorder with uncertain causes. Recent studies have indicated correlations between circular RNAs (circRNAs) and psychiatric disorders. However, the potential role of circRNAs in MDD remains largely unknown. METHODS We investigated the expression and diagnostic significance of circRNA protein tyrosine kinase 2 (circPTK2) by recruiting 50 MDD patients and 40 healthy subjects. Additionally, chronic unpredictable mild stress (CUMS) mouse model was established in animal experiments. QRT-PCR was adopted for circPTK2 and miR-182-5p levels. To investigate the role of circPTK2 in MDD, we utilized microinjection of circPTK2 adeno-associated virus into the mouse hippocampus. Depressive-like behaviors of mice were assessed through forced swim test and open field test. Additionally, the interaction between circPTK2 and miR-182-5p was validated using a dual luciferase reporter assay. RESULTS Decreased expression of circPTK2 was found in peripheral blood mononuclear cells of MDD patients and in hippocampus of CUMS mice, which was useful for distinguishing MDD patients from healthy subjects. Notably, overexpression of circPTK2 was associated with depressive-like behaviors induced by CUMS. Further mechanism research demonstrated that circPTK2 functioned as the sponge for miR-182-5p, which may contribute to the beneficial effect of circPTK2. CONCLUSION Collectively, our findings suggest the participation of circPTK2 and its underlying mechanism in MDD, which might provide a potential target for MDD therapy.
Collapse
Affiliation(s)
- Kunyu Wang
- Department of Neurology, Neuroscience Research Center, The First Hospital of Jilin University, Changchun, China
| | - Yu Yang
- Department of Neurology, Neuroscience Research Center, The First Hospital of Jilin University, Changchun, China
| | - Yiwen Wang
- Department of Neurology, Neuroscience Research Center, The First Hospital of Jilin University, Changchun, China
| | - Zhuoya Jiang
- Department of Neurology, Neuroscience Research Center, The First Hospital of Jilin University, Changchun, China
| | - Shaokuan Fang
- Department of Neurology, Neuroscience Research Center, The First Hospital of Jilin University, Changchun, China.
| |
Collapse
|
4
|
Jin M, Zhang S, Huang B, Li L, Liang H, Ni A, Han L, Liang P, Liu J, Shi H, Lv P. Dulaglutide treatment reverses depression-like behavior and hippocampal metabolomic homeostasis in mice exposed to chronic mild stress. Brain Behav 2024; 14:e3448. [PMID: 38444330 PMCID: PMC10915471 DOI: 10.1002/brb3.3448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/10/2024] [Accepted: 02/04/2024] [Indexed: 03/07/2024] Open
Abstract
INTRODUCTION Treatment strategies for depression based on interventions for glucose and lipid metabolism disorders are receiving increasing attention. Investigating the mechanism of their antidepressant effect and exploring new diagnostic and therapeutic biomarkers have attracted increasing attention. Dulaglutide, a long-acting GLP-1 receptor agonist, has been reported to alleviate cognitive deficits and neuronal damage. However, the antidepressant effect of dulaglutide and, especially, the underlying mechanism are still poorly understood. In this study, we aimed to explore the underlying biomarkers of depression and potential modulatory targets of dulaglutide in chronic mild stress (CMS) mice. METHODS Sixty mice were randomly divided into a control group (CON group), a CMS+Vehicle group (CMS+Veh group), a CMS+0.3 mg/kg dulaglutide group (Low Dula group), and a CMS+0.6 mg/kg dulaglutide group (High Dula group). Numerous behavioral tests, mainly the open field test, forced swimming test, and tail suspension test, were applied to evaluate the potential effect of dulaglutide treatment on anxiety- and depression-like behaviors in mice exposed to chronic stress. Furthermore, a liquid chromatography-tandem mass spectrometry-based metabolomics approach was utilized to investigate the associated mechanisms of dulaglutide treatment. RESULTS Three weeks of dulaglutide treatment significantly reversed depressive-like but not anxiety-like behaviors in mice exposed to chronic stress for 4 weeks. The results from the metabolomics analysis showed that a total of 20 differentially expressed metabolites were identified between the CON and CMS+Veh groups, and 46 metabolites were selected between the CMS+Veh and High Dula groups in the hippocampus of the mice. Comprehensive analysis indicated that lipid metabolism, amino acid metabolism, energy metabolism, and tryptophan metabolism were disrupted in model mice that experienced depression and underwent dulaglutide therapy. CONCLUSION The antidepressant effects of dulaglutide in a CMS depression model were confirmed. We identified 64 different metabolites and four major pathways associated with metabolic pathophysiological processes. These primary data provide a new perspective for understanding the antidepressant-like effects of dulaglutide and may facilitate the use of dulaglutide as a potential therapeutic strategy for depression.
Collapse
Affiliation(s)
- Man Jin
- Department of NeurologyHebei Medical UniversityShijiazhuangChina
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Shipan Zhang
- Department of NeurologyHebei Medical UniversityShijiazhuangChina
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Boya Huang
- Neuroscience Research Center, Institute of Medical and Health ScienceHebei Medical UniversityShijiazhuangChina
| | - Litao Li
- Department of NeurologyHebei Medical UniversityShijiazhuangChina
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Hao Liang
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
- Cardiology DepartmentHebei General HospitalShijiazhuangChina
| | - Aihua Ni
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Lina Han
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Peng Liang
- Department of NeurologyHebei Medical UniversityShijiazhuangChina
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Jing Liu
- Department of NeurologyHebei Medical UniversityShijiazhuangChina
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| | - Haishui Shi
- Nursing SchoolHebei Medical UniversityShijiazhuangChina
| | - Peiyuan Lv
- Department of NeurologyHebei Medical UniversityShijiazhuangChina
- Department of NeurologyHebei General HospitalShijiazhuangChina
- Hebei Provincial Key Laboratory of Cerebral Networks and Cognitive DisordersShijiazhuangChina
| |
Collapse
|