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Xu J, Luo Y, Zhang J, Zhong L, Liu H, Weng A, Yang Z, Zhang Y, Ou Z, Yan Z, Cheng Q, Fan X, Zhang X, Zhang W, Hu Q, Liang D, Peng K, Liu G. Progressive thalamic nuclear atrophy in blepharospasm and blepharospasm-oromandibular dystonia. Brain Commun 2024; 6:fcae117. [PMID: 38638150 PMCID: PMC11025674 DOI: 10.1093/braincomms/fcae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/21/2024] [Accepted: 04/05/2024] [Indexed: 04/20/2024] Open
Abstract
The thalamus is considered a key region in the neuromechanisms of blepharospasm. However, previous studies considered it as a single, homogeneous structure, disregarding potentially useful information about distinct thalamic nuclei. Herein, we aimed to examine (i) whether grey matter volume differs across thalamic subregions/nuclei in patients with blepharospasm and blepharospasm-oromandibular dystonia; (ii) causal relationships among abnormal thalamic nuclei; and (iii) whether these abnormal features can be used as neuroimaging biomarkers to distinguish patients with blepharospasm from blepharospasm-oromandibular dystonia and those with dystonia from healthy controls. Structural MRI data were collected from 56 patients with blepharospasm, 20 with blepharospasm-oromandibular dystonia and 58 healthy controls. Differences in thalamic nuclei volumes between groups and their relationships to clinical information were analysed in patients with dystonia. Granger causality analysis was employed to explore the causal effects among abnormal thalamic nuclei. Support vector machines were used to test whether these abnormal features could distinguish patients with different forms of dystonia and those with dystonia from healthy controls. Compared with healthy controls, patients with blepharospasm exhibited reduced grey matter volume in the lateral geniculate and pulvinar inferior nuclei, whereas those with blepharospasm-oromandibular dystonia showed decreased grey matter volume in the ventral anterior and ventral lateral anterior nuclei. Atrophy in the pulvinar inferior nucleus in blepharospasm patients and in the ventral lateral anterior nucleus in blepharospasm-oromandibular dystonia patients was negatively correlated with clinical severity and disease duration, respectively. The proposed machine learning scheme yielded a high accuracy in distinguishing blepharospasm patients from healthy controls (accuracy: 0.89), blepharospasm-oromandibular dystonia patients from healthy controls (accuracy: 0.82) and blepharospasm from blepharospasm-oromandibular dystonia patients (accuracy: 0.94). Most importantly, Granger causality analysis revealed that a progressive driving pathway from pulvinar inferior nuclear atrophy extends to lateral geniculate nuclear atrophy and then to ventral lateral anterior nuclear atrophy with increasing clinical severity in patients with blepharospasm. These findings suggest that the pulvinar inferior nucleus in the thalamus is the focal origin of blepharospasm, extending to pulvinar inferior nuclear atrophy and subsequently extending to the ventral lateral anterior nucleus causing involuntary lower facial and masticatory movements known as blepharospasm-oromandibular dystonia. Moreover, our results also provide potential targets for neuromodulation especially deep brain stimulation in patients with blepharospasm and blepharospasm-oromandibular dystonia.
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Affiliation(s)
- Jinping Xu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Yuhan Luo
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Jiana Zhang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Linchang Zhong
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Huiming Liu
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Ai Weng
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Zhengkun Yang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Yue Zhang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Zilin Ou
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Zhicong Yan
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Qinxiu Cheng
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xinxin Fan
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Xiaodong Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Weixi Zhang
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
| | - Qingmao Hu
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Dong Liang
- Institute of Biomedical and Health Engineering, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Kangqiang Peng
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China
| | - Gang Liu
- Department of Neurology, The First Affiliated Hospital, Sun Yat-Sen University, Guangdong Provincial Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases, National Key Clinical Department and Key Discipline of Neurology, Guangzhou 510080, China
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Wu Q, Xu L, Wan J, Yu Z, Lei Y. Intolerance of uncertainty affects the behavioral and neural mechanisms of higher generalization. Cereb Cortex 2024; 34:bhae153. [PMID: 38615238 DOI: 10.1093/cercor/bhae153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/15/2024] Open
Abstract
Intolerance of uncertainty (IU) is associated with several anxiety disorders. In this study, we employed rewards and losses as unconditioned positive and negative stimuli, respectively, to explore the effects of an individual's IU level on positive and negative generalizations using magnetic resonance imaging technology. Following instrumental learning, 48 participants (24 high IU; 24 low IU) were invited to complete positive and negative generalization tasks; their behavioral responses and neural activities were recorded by functional magnetic resonance imaging. The behavior results demonstrated that participants with high IUs exhibited higher generalizations to both positive and negative cues as compared with participants having low IUs. Neuroimaging results demonstrated that they exhibited higher activation levels in the right anterior insula and the default mode network (i.e. precuneus and posterior cingulate gyrus), as well as related reward circuits (i.e. caudate and right putamen). Therefore, higher generalization scores and the related abnormal brain activation may be key markers of IU as a vulnerability factor for anxiety disorders.
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Affiliation(s)
- Qi Wu
- Institute for Brain and Psychological Sciences, Sichuan Normal University, China
| | - Lei Xu
- Institute for Brain and Psychological Sciences, Sichuan Normal University, China
| | - Jiaming Wan
- Institute for Brain and Psychological Sciences, Sichuan Normal University, China
| | - Zhang Yu
- Institute for Brain and Psychological Sciences, Sichuan Normal University, China
| | - Yi Lei
- Institute for Brain and Psychological Sciences, Sichuan Normal University, China
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Du X, Yan Y, Yu J, Zhu T, Huang CC, Zhang L, Shan X, Li R, Dai Y, Lv H, Zhang XY, Feng J, Li WG, Luo Q, Li F. SH2B1 Tunes Hippocampal ERK Signaling to Influence Fluid Intelligence in Humans and Mice. Research (Wash D C) 2023; 6:0269. [PMID: 38434247 PMCID: PMC10907025 DOI: 10.34133/research.0269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 10/19/2023] [Indexed: 03/05/2024]
Abstract
Fluid intelligence is a cognitive domain that encompasses general reasoning, pattern recognition, and problem-solving abilities independent of task-specific experience. Understanding its genetic and neural underpinnings is critical yet challenging for predicting human development, lifelong health, and well-being. One approach to address this challenge is to map the network of correlations between intelligence and other constructs. In the current study, we performed a genome-wide association study using fluid intelligence quotient scores from the UK Biobank to explore the genetic architecture of the associations between obesity risk and fluid intelligence. Our results revealed novel common genetic loci (SH2B1, TUFM, ATP2A1, and FOXO3) underlying the association between fluid intelligence and body metabolism. Surprisingly, we demonstrated that SH2B1 variation influenced fluid intelligence independently of its effects on metabolism but partially mediated its association with bilateral hippocampal volume. Consistently, selective genetic ablation of Sh2b1 in the mouse hippocampus, particularly in inhibitory neurons, but not in excitatory neurons, significantly impaired working memory, short-term novel object recognition memory, and behavioral flexibility, but not spatial learning and memory, mirroring the human intellectual performance. Single-cell genetic profiling of Sh2B1-regulated molecular pathways revealed that Sh2b1 deletion resulted in aberrantly enhanced extracellular signal-regulated kinase (ERK) signaling, whereas pharmacological inhibition of ERK signaling reversed the associated behavioral impairment. Our cross-species study thus provides unprecedented insight into the role of SH2B1 in fluid intelligence and has implications for understanding the genetic and neural underpinnings of lifelong mental health and well-being.
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Affiliation(s)
- Xiujuan Du
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Yuhua Yan
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education),
School of Life Sciences, East China Normal University, Shanghai 200062, China
- Department of Rehabilitation Medicine, Huashan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science,
Fudan University, Shanghai 200032, China
| | - Juehua Yu
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Center for Experimental Studies and Research,
The First Affiliated Hospital of Kunming Medical University, Kunming 650032, China
| | - Tailin Zhu
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education),
School of Life Sciences, East China Normal University, Shanghai 200062, China
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
| | - Chu-Chung Huang
- Institute of Cognitive Neuroscience, School of Psychology and Cognitive Science,
East China Normal University, Shanghai 200062, China
| | - Lingli Zhang
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
| | - Xingyue Shan
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Key Laboratory of Brain Functional Genomics (Ministry of Education),
School of Life Sciences, East China Normal University, Shanghai 200062, China
| | - Ren Li
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Yuan Dai
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
| | - Hui Lv
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
| | - Xiao-Yong Zhang
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Jianfeng Feng
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Wei-Guang Li
- Department of Rehabilitation Medicine, Huashan Hospital, Institute for Translational Brain Research, State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science,
Fudan University, Shanghai 200032, China
| | - Qiang Luo
- National Clinical Research Center for Aging and Medicine at Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, Ministry of Education-Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence,
Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology and Ministry of Education Frontiers Center for Brain Science, Institutes of Brain Science and Human Phenom Institute,
Fudan University, Shanghai 200032, China
| | - Fei Li
- Developmental and Behavioral Pediatric Department, Brain and Behavioral Research Unit of Shanghai Institute for Pediatric Research and Ministry of Education-Shanghai Key Laboratory for Children’s Environmental Health,
Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China
- Developmental and Behavioral Pediatric Department,
Shanghai Xinhua Children’s Hospital, Shanghai 200092, China
- Shanghai Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China
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Li ZC, Yan J, Zhang S, Liang C, Lv X, Zou Y, Zhang H, Liang D, Zhang Z, Chen Y. Glioma survival prediction from whole-brain MRI without tumor segmentation using deep attention network: a multicenter study. Eur Radiol 2022; 32:5719-5729. [PMID: 35278123 DOI: 10.1007/s00330-022-08640-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 01/10/2022] [Accepted: 02/02/2022] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To develop and validate a deep learning model for predicting overall survival from whole-brain MRI without tumor segmentation in patients with diffuse gliomas. METHODS In this multicenter retrospective study, two deep learning models were built for survival prediction from MRI, including a DeepRisk model built from whole-brain MRI, and an original ResNet model built from expert-segmented tumor images. Both models were developed using a training dataset (n = 935) and an internal tuning dataset (n = 156) and tested on two external test datasets (n = 194 and 150) and a TCIA dataset (n = 121). C-index, integrated Brier score (IBS), prediction error curves, and calibration curves were used to assess the model performance. RESULTS In total, 1556 patients were enrolled (age, 49.0 ± 13.1 years; 830 male). The DeepRisk score was an independent predictor and can stratify patients in each test dataset into three risk subgroups. The IBS and C-index for DeepRisk were 0.14 and 0.83 in external test dataset 1, 0.15 and 0.80 in external dataset 2, and 0.16 and 0.77 in TCIA dataset, respectively, which were comparable with those for original ResNet. The AUCs at 6, 12, 24, 26, and 48 months for DeepRisk ranged between 0.77 and 0.94. Combining DeepRisk score with clinicomolecular factors resulted in a nomogram with a better calibration and classification accuracy (net reclassification improvement 0.69, p < 0.001) than the clinical nomogram. CONCLUSIONS DeepRisk that obviated the need of tumor segmentation can predict glioma survival from whole-brain MRI and offers incremental prognostic value. KEY POINTS • DeepRisk can predict overall survival directly from whole-brain MRI without tumor segmentation. • DeepRisk achieves comparable accuracy in survival prediction with deep learning model built using expert-segmented tumor images. • DeepRisk has independent and incremental prognostic value over existing clinical parameters and IDH mutation status.
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Affiliation(s)
- Zhi-Cheng Li
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- National Innovation Center for Advanced Medical Devices, Shenzhen, China
| | - Jing Yan
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Shenghai Zhang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Chaofeng Liang
- Department of Neurosurgery, The 3rd Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiaofei Lv
- Department of Medical Imaging, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
| | - Yan Zou
- Department of Radiology, The 3rd Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Huailing Zhang
- School of Information Engineering, Guangdong Medical University, Dongguan, China
| | - Dong Liang
- Institute of Biomedical and Health Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- National Innovation Center for Advanced Medical Devices, Shenzhen, China
| | - Zhenyu Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Zhengzhou University, 1 Jian she Dong Road, Zhengzhou, 450052, Henan, China.
| | - Yinsheng Chen
- Department of Neurosurgery/Neuro-oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng East Road, Guangzhou, 510060, China.
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Lu R, Dong Y, Li JD. Necdin regulates BMAL1 stability and circadian clock through SGT1-HSP90 chaperone machinery. Nucleic Acids Res 2020; 48:7944-7957. [PMID: 32667666 PMCID: PMC7430654 DOI: 10.1093/nar/gkaa601] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 07/02/2020] [Accepted: 07/07/2020] [Indexed: 12/13/2022] Open
Abstract
Circadian clocks are endogenous oscillators that control ∼24-hour physiology and behaviors in virtually all organisms. The circadian oscillator comprises interconnected transcriptional and translational feedback loops, but also requires finely coordinated protein homeostasis including protein degradation and maturation. However, the mechanisms underlying the mammalian clock protein maturation is largely unknown. In this study, we demonstrate that necdin, one of the Prader-Willi syndrome (PWS)-causative genes, is highly expressed in the suprachiasmatic nuclei (SCN), the pacemaker of circadian clocks in mammals. Mice deficient in necdin show abnormal behaviors during an 8-hour advance jet-lag paradigm and disrupted clock gene expression in the liver. By using yeast two hybrid screening, we identified BMAL1, the core component of the circadian clock, and co-chaperone SGT1 as two necdin-interactive proteins. BMAL1 and SGT1 associated with the N-terminal and C-terminal fragments of necdin, respectively. Mechanistically, necdin enables SGT1-HSP90 chaperone machinery to stabilize BMAL1. Depletion of necdin or SGT1/HSP90 leads to degradation of BMAL1 through the ubiquitin-proteasome system, resulting in alterations in both clock gene expression and circadian rhythms. Taken together, our data identify the PWS-associated protein necdin as a novel regulator of the circadian clock, and further emphasize the critical roles of chaperone machinery in circadian clock regulation.
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Affiliation(s)
- Renbin Lu
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha 410078, Hunan, P. R. China
- Hunan Key Laboratory of Animal Models for Human Diseases, Changsha 410078, Hunan, P. R. China
| | - Yufan Dong
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha 410078, Hunan, P. R. China
| | - Jia-Da Li
- Center for Medical Genetics, School of Life Sciences, Central South University, Changsha 410078, Hunan, P. R. China
- Hunan Key Laboratory of Animal Models for Human Diseases, Changsha 410078, Hunan, P. R. China
- Hunan Key Laboratory of Medical Genetics, Changsha 410078, Hunan, P. R. China
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