51
|
MicroRNA-22-3p ameliorates Alzheimer's disease by targeting SOX9 through the NF-κB signaling pathway in the hippocampus. J Neuroinflammation 2022; 19:180. [PMID: 35821145 PMCID: PMC9277852 DOI: 10.1186/s12974-022-02548-1] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 07/06/2022] [Indexed: 11/30/2022] Open
Abstract
Background Studies have suggested that many down-regulated miRNAs identified in the brain tissue or serum of Alzheimer’s disease (AD) patients were involved in the formation of senile plaques and neurofibrillary tangles. Specifically, our previous study revealed that microRNA-22-3p (miR-22-3p) was significantly down-regulated in AD patients. However, the molecular mechanism underlying the down-regulation of miR-22-3p has not been comprehensively investigated. Methods The ameliorating effect of miR-22-3p on apoptosis of the Aβ-treated HT22 cells was detected by TUNEL staining, flow cytometry, and western blotting. The cognition of mice with stereotaxic injection of agomir or antagomir of miR-22-3p was assessed by Morris water maze test. Pathological changes in the mouse hippocampus were analyzed using hematoxylin and eosin (HE) staining, Nissl staining, and immunohistochemistry. Proteomics analysis was performed to identify the targets of miR-22-3p, which were further validated using dual-luciferase reporter analysis and western blotting analysis. Results The miR-22-3p played an important role in ameliorating apoptosis in the Aβ-treated HT22 cells. Increased levels of miR-22-3p in the mouse hippocampus improved the cognition in mice. Although the miR-22-3p did not cause the decrease of neuronal loss in the hippocampus, it reduced the Aβ deposition. Proteomics analysis revealed Sox9 protein as the target of miR-22-3p, which was verified by the luciferase reporter experiments. Conclusion Our study showed that miR-22-3p could improve apoptosis and reduce Aβ deposition by acting on Sox9 through the NF-κB signaling pathway to improve the cognition in AD mice. We concluded that miR-22-3p ameliorated AD by targeting Sox9 through the NF-κB signaling pathway in the hippocampus. Supplementary Information The online version contains supplementary material available at 10.1186/s12974-022-02548-1.
Collapse
|
52
|
Kim TA, Syty MD, Wu K, Ge S. Adult hippocampal neurogenesis and its impairment in Alzheimer's disease. Zool Res 2022; 43:481-496. [PMID: 35503338 PMCID: PMC9113964 DOI: 10.24272/j.issn.2095-8137.2021.479] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Accepted: 04/25/2022] [Indexed: 11/07/2022] Open
Abstract
Adult neurogenesis is the creation of new neurons which integrate into the existing neural circuit of the adult brain. Recent evidence suggests that adult hippocampal neurogenesis (AHN) persists throughout life in mammals, including humans. These newborn neurons have been implicated to have a crucial role in brain functions such as learning and memory. Importantly, studies have also found that hippocampal neurogenesis is impaired in neurodegenerative and neuropsychiatric diseases. Alzheimer's disease (AD) is one of the most common forms of dementia affecting millions of people. Cognitive dysfunction is a common symptom of AD patients and progressive memory loss has been attributed to the degeneration of the hippocampus. Therefore, there has been growing interest in identifying how hippocampal neurogenesis is affected in AD. However, the link between cognitive decline and changes in hippocampal neurogenesis in AD is poorly understood. In this review, we summarized the recent literature on AHN and its impairments in AD.
Collapse
Affiliation(s)
- Thomas A Kim
- Department of Neurobiology & Behavior, SUNY at Stony Brook, Stony Brook, NY 11794, USA
- Medical Scientist Training Program (MSTP), Renaissance School of Medicine at SUNY, Stony Brook, Stony Brook, NY 11794, USA
| | - Michelle D Syty
- Department of Neurobiology & Behavior, SUNY at Stony Brook, Stony Brook, NY 11794, USA
| | - Kaitlyn Wu
- Department of Neurobiology & Behavior, SUNY at Stony Brook, Stony Brook, NY 11794, USA
| | - Shaoyu Ge
- Department of Neurobiology & Behavior, SUNY at Stony Brook, Stony Brook, NY 11794, USA. E-mail:
| |
Collapse
|
53
|
Jiang Y, Li L, Pang K, Liu J, Chen B, Yuan J, Shen L, Chen X, Lu B, Han H. Synaptic degeneration in the prefrontal cortex of a rat AD model revealed by volume electron microscopy. J Mol Cell Biol 2022; 14:6541851. [PMID: 35238943 PMCID: PMC9254871 DOI: 10.1093/jmcb/mjac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/22/2022] [Accepted: 03/01/2022] [Indexed: 11/24/2022] Open
Affiliation(s)
- Yi Jiang
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Linlin Li
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Keliang Pang
- School of Pharmaceutical Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Jiazheng Liu
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Bohao Chen
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Jingbin Yuan
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China
| | - Lijun Shen
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xi Chen
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Bai Lu
- School of Pharmaceutical Sciences, IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.,Beijing Advanced Innovation Center for Structural Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Hua Han
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| |
Collapse
|