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Lv K, Hu Y, Cao X, Xie Y, Fu J, Chen H, Xiong J, Zhu L, Geng D, Zhang J. Altered whole-brain functional network in patients with frontal low-grade gliomas: a resting-state functional MRI study. Neuroradiology 2024; 66:775-784. [PMID: 38294728 DOI: 10.1007/s00234-024-03300-7] [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: 11/07/2023] [Accepted: 01/27/2024] [Indexed: 02/01/2024]
Abstract
PURPOSE Gliomas are the most common primary brain tumor. Currently, topological alterations of whole-brain functional network caused by gliomas are not fully understood. The work here clarified the topological reorganization of the functional network in patients with unilateral frontal low-grade gliomas (LGGs). METHODS A total of 45 patients with left frontal LGGs, 19 with right frontal LGGs, and 25 healthy controls (HCs) were enrolled. All the resting-state functional MRI (rs-fMRI) images of the subjects were preprocessed to construct the functional network matrix, which was used for graph theoretical analysis. A two-sample t-test was conducted to clarify the differences in global and nodal network metrics between patients and HCs. A network-based statistic approach was used to identify the altered specific pairs of regions in which functional connectivity in patients with LGGs. RESULTS The local efficiency, clustering coefficient, characteristic path length, and normalized characteristic path length of patients with unilateral frontal LGGs were significantly lower than HCs, while there were no significant differences of global efficiency and small-worldness between patients and HCs. Compared with the HCs, betweenness centrality, degree centrality, and nodal efficiency of several brain nodes were changed significantly in patients. Around the tumor and its adjacent areas, the inter- and intra-hemispheric connections were significantly decreased in patients with left frontal LGGs. CONCLUSION The patients with unilateral frontal LGGs have altered global and nodal network metrics and decreased inter- and intra-hemispheric connectivity. These topological alterations may be involved in functional impairment and compensation of patients.
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Affiliation(s)
- Kun Lv
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yue Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Department of Radiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China
| | - Xin Cao
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yongsheng Xie
- Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Junyan Fu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Ji Xiong
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Zhu
- Department of Radiology, Shanghai Chest Hospital, Shanghai Jiao Tong University, 241 West Huaihai Road, Shanghai, China.
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, China.
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
- Academy for Engineering and Technology, Fudan University, Shanghai, China.
| | - Jun Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China.
- Shanghai Engineering Research Center of Intelligent Imaging for Critical Brain Diseases, Shanghai, China.
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
- Academy for Engineering and Technology, Fudan University, Shanghai, China.
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Li J, Zhang L, Xing H, Geng Y, Lv S, Luo X, He W, Fu Z, Li G, Hu B, Jiang S, Yang Z, Zhu N, Zhang Q, Zhao J, Tao Y, Shen C, Li R, Tang F, Zheng S, Bao Y, He Q, Geng D, Wang Z. The Absence of Intra-Tumoral Tertiary Lymphoid Structures is Associated with a Worse Prognosis and mTOR Signaling Activation in Hepatocellular Carcinoma with Liver Transplantation: A Multicenter Retrospective Study. Adv Sci (Weinh) 2024:e2309348. [PMID: 38498682 DOI: 10.1002/advs.202309348] [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] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 02/21/2024] [Indexed: 03/20/2024]
Abstract
Tertiary lymphoid structure (TLS) can predict the prognosis and sensitivity of tumors to immune checkpoint inhibitors (ICIs) therapy, whether it can be noninvasively predicted by radiomics in hepatocellular carcinoma with liver transplantation (HCC-LT) has not been explored. In this study, it is found that intra-tumoral TLS abundance is significantly correlated with recurrence-free survival (RFS) and overall survival (OS). Tumor tissues with TLS are characterized by inflammatory signatures and high infiltration of antitumor immune cells, while those without TLS exhibit uncontrolled cell cycle progression and activated mTOR signaling by bulk and single-cell RNA-seq analyses. The regulators involved in mTOR signaling (RHEB and LAMTOR4) and S-phase (RFC2, PSMC2, and ORC5) are highly expressed in HCC with low TLS. In addition, the largest cohort of HCC patients is studied with available radiomics data, and a classifier is built to detect the presence of TLS in a non-invasive manner. The classifier demonstrates remarkable performance in predicting intra-tumoral TLS abundance in both training and test sets, achieving areas under receiver operating characteristic curve (AUCs) of 92.9% and 90.2% respectively. In summary, the absence of intra-tumoral TLS abundance is associated with mTOR signaling activation and uncontrolled cell cycle progression in tumor cells, indicating unfavorable prognosis in HCC-LT.
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Affiliation(s)
- Jianhua Li
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Li Zhang
- Shanghai Institute of Immunology, Department of Immunology and Microbiology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, P. R. China
| | - Hao Xing
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Yan Geng
- Hepatobiliary Surgery, Department of General Surgery, Huashan Hospital & Cancer Metastasis Institute, Fudan University, Shanghai, 200040, P. R. China
| | - Shaocheng Lv
- Department of Hepatobiliary Surgery, Beijing Chaoyang Hospital affiliated to Capital Medical University, Beijing, 100020, P. R. China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, 200032, P. R. China
| | - Weiqiao He
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Zhi Fu
- General Surgery Center, Beijing Youan Hospital, Capital Medical University, Beijing, 100020, P. R. China
| | - Guangming Li
- General Surgery Center, Beijing Youan Hospital, Capital Medical University, Beijing, 100020, P. R. China
| | - Bin Hu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, 200040, P. R. China
| | - Shengran Jiang
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Zhe Yang
- Department of Hepatobiliary and Pancreatic Surgery, Shulan (Hangzhou) Hospital, Zhejiang Shuren University School of Medicine, Hangzhou, 310022, P. R. China
| | - Ningqi Zhu
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Quanbao Zhang
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Jing Zhao
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Yifeng Tao
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Conghuan Shen
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
| | - Ruidong Li
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
- Department of Critical Care Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
| | - Feng Tang
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
| | - Shusen Zheng
- Department of Hepatobiliary and Pancreatic Surgery, Shulan (Hangzhou) Hospital, Zhejiang Shuren University School of Medicine, Hangzhou, 310022, P. R. China
| | - Yun Bao
- Department of Pathology, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
| | - Qiang He
- Department of Hepatobiliary Surgery, Beijing Chaoyang Hospital affiliated to Capital Medical University, Beijing, 100020, P. R. China
| | - Daoying Geng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, 200040, P. R. China
| | - Zhengxin Wang
- Liver Transplantation Center, Department of General Surgery, Huashan Hospital, Fudan University, Shanghai, 200040, P. R. China
- Institute of Organ Transplantation, Fudan University, Shanghai, 200040, P. R. China
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Wang Z, Ding Y, Yuan W, Chen H, Chen W, Chen C. Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas. Bioengineering (Basel) 2024; 11:276. [PMID: 38534550 DOI: 10.3390/bioengineering11030276] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 03/01/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
EEG, which can provide brain alteration information via recording the electrical activity of neurons in the cerebral cortex, has been widely used in neurophysiology. However, conventional wet electrodes in EEG monitoring typically suffer from inherent limitations, including the requirement of skin pretreatment, the risk of superficial skin infections, and signal performance deterioration that may occur over time due to the air drying of the conductive gel. Although the emergence of dry electrodes has overcome these shortcomings, their electrode-skin contact impedance is significantly high and unstable, especially in hair-covered areas. To address the above problems, an active claw-shaped dry electrode is designed, moving from electrode morphological design, slurry preparation, and coating to active electrode circuit design. The active claw-shaped dry electrode, which consists of a claw-shaped electrode and active electrode circuit, is dedicated to offering a flexible solution for elevating electrode fittings on the scalp in hair-covered areas, reducing electrode-skin contact impedance and thus improving the quality of the acquired EEG signal. The performance of the proposed electrodes was verified by impedance, active electrode circuit, eyes open-closed, steady-state visually evoked potential (SSVEP), and anti-interference tests, based on EEG signal acquisition. Experimental results show that the proposed claw-shaped electrodes (without active circuit) can offer a better fit between the scalp and electrodes, with a low electrode-skin contact impedance (18.62 KΩ@1 Hz in the hairless region and 122.15 KΩ@1 Hz in the hair-covered region). In addition, with the active circuit, the signal-to-noise ratio (SNR) of the acquiring EEG signal was improved and power frequency interference was restrained, therefore, the proposed electrodes can yield an EEG signal quality comparable to wet electrodes.
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Affiliation(s)
- Zaihao Wang
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Yuhao Ding
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Wei Yuan
- Center for Intelligent Medical Equipment and Devices, Institute for Innovative Medical Devices, School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230026, China
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China
| | - Hongyu Chen
- Center for Intelligent Medical Electronics, School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Wei Chen
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW 2006, Australia
| | - Chen Chen
- Human Phenome Institute, Fudan University, Shanghai 201203, China
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Yang YT, Gan Z, Zhang J, Zhao X, Yang Y, Han S, Wu W, Zhao XM. STAB2: an updated spatio-temporal cell atlas of the human and mouse brain. Nucleic Acids Res 2024; 52:D1033-D1041. [PMID: 37904591 PMCID: PMC10767951 DOI: 10.1093/nar/gkad955] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 09/30/2023] [Accepted: 10/13/2023] [Indexed: 11/01/2023] Open
Abstract
The brain is constituted of heterogeneous types of neuronal and non-neuronal cells, which are organized into distinct anatomical regions, and show precise regulation of gene expression during development, aging and function. In the current database release, STAB2 provides a systematic cellular map of the human and mouse brain by integrating recently published large-scale single-cell and single-nucleus RNA-sequencing datasets from diverse regions and across lifespan. We applied a hierarchical strategy of unsupervised clustering on the integrated single-cell transcriptomic datasets to precisely annotate the cell types and subtypes in the human and mouse brain. Currently, STAB2 includes 71 and 61 different cell subtypes defined in the human and mouse brain, respectively. It covers 63 subregions and 15 developmental stages of human brain, and 38 subregions and 30 developmental stages of mouse brain, generating a comprehensive atlas for exploring spatiotemporal transcriptomic dynamics in the mammalian brain. We also augmented web interfaces for querying and visualizing the gene expression in specific cell types. STAB2 is freely available at https://mai.fudan.edu.cn/stab2.
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Affiliation(s)
- Yucheng T Yang
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang 313000, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, Zhejiang 313000, China
- Institute of Science and Technology for Brain‐Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Ziquan Gan
- Institute of Science and Technology for Brain‐Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Jinglong Zhang
- Institute of Science and Technology for Brain‐Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Xingzhong Zhao
- Institute of Science and Technology for Brain‐Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Yifan Yang
- Institute of Science and Technology for Brain‐Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
| | - Shuwen Han
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang 313000, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, Zhejiang 313000, China
| | - Wei Wu
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang 313000, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, Zhejiang 313000, China
| | - Xing-Ming Zhao
- Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang 313000, China
- Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer of Huzhou, Huzhou, Zhejiang 313000, China
- Institute of Science and Technology for Brain‐Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai 200032, China
- International Human Phenome Institutes (Shanghai), Shanghai 200433, China
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Luo X, Li H, Xia W, Quan C, ZhangBao J, Tan H, Wang N, Bao Y, Geng D, Li Y, Yang L. Joint radiomics and spatial distribution model for MRI-based discrimination of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder. Eur Radiol 2023:10.1007/s00330-023-10529-y. [PMID: 38127076 DOI: 10.1007/s00330-023-10529-y] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Revised: 10/26/2023] [Accepted: 11/23/2023] [Indexed: 12/23/2023]
Abstract
OBJECTIVE To develop a discrimination pipeline concerning both radiomics and spatial distribution features of brain lesions for discrimination of multiple sclerosis (MS), aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder (NMOSD), and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder (MOGAD). METHODS Hyperintensity T2 lesions were delineated in 212 brain MRI scans of MS (n = 63), NMOSD (n = 87), and MOGAD (n = 45) patients. To avoid the effect of fixed training/test dataset sampling when developing machine learning models, patients were allocated into 4 sub-groups for cross-validation. For each scan, 351 radiomics and 27 spatial distribution features were extracted. Three models, i.e., multi-lesion radiomics, spatial distribution, and joint models, were constructed using random forest and logistic regression algorithms for differentiating: MS from the others (MS models) and MOGAD from NMOSD (MOG-NMO models), respectively. Then, the joint models were combined with demographic characteristics (i.e., age and sex) to create MS and MOG-NMO discriminators, respectively, based on which a three-disease discrimination pipeline was generated and compared with radiologists. RESULTS For classification of both MS-others and MOG-NMO, the joint models performed better than radiomics or spatial distribution model solely. The MS discriminator achieved AUC = 0.909 ± 0.027 and bias-corrected C-index = 0.909 ± 0.027, and the MOG-NMO discriminator achieved AUC = 0.880 ± 0.064 and bias-corrected C-index = 0.883 ± 0.068. The three-disease discrimination pipeline differentiated MS, NMOSD, and MOGAD patients with 75.0% accuracy, prominently outperforming the three radiologists (47.6%, 56.6%, and 66.0%). CONCLUSIONS The proposed pipeline integrating multi-lesion radiomics and spatial distribution features could effectively differentiate MS, NMOSD, and MOGAD. CLINICAL RELEVANCE STATEMENT The discrimination pipeline merging both radiomics and spatial distribution features of brain lesions may facilitate the differential diagnoses of multiple sclerosis, neuromyelitis optica spectrum disorder, and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder. KEY POINTS • Our study introduces an approach by combining radiomics and spatial distribution models. • The joint model exhibited superior performance in distinguishing multiple sclerosis from aquaporin-4-IgG-seropositive neuromyelitis optica spectrum disorder and myelin-oligodendrocyte-glycoprotein-IgG-associated disorder as well as discriminating the latter two diseases. • The three-disease discrimination pipeline showcased remarkable accuracy, surpassing the performance of experienced radiologists, highlighting its potential as a valuable diagnostic tool.
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Affiliation(s)
- Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Haiqing Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
| | - Wei Xia
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Chao Quan
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jingzi ZhangBao
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hongmei Tan
- Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
- National Center for Neurological Disorders, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Na Wang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
| | - Yifang Bao
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China.
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Liqin Yang
- Department of Radiology, Huashan Hospital, Fudan University, 12 Wulumuqi Middle Road, Shanghai, 200040, China.
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
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Yang A, Yang YT, Zhao XM. An augmented Mendelian randomization approach provides causality of brain imaging features on complex traits in a single biobank-scale dataset. PLoS Genet 2023; 19:e1011112. [PMID: 38150468 PMCID: PMC10775988 DOI: 10.1371/journal.pgen.1011112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 01/09/2024] [Accepted: 12/12/2023] [Indexed: 12/29/2023] Open
Abstract
Mendelian randomization (MR) is an effective approach for revealing causal risk factors that underpin complex traits and diseases. While MR has been more widely applied under two-sample settings, it is more promising to be used in one single large cohort given the rise of biobank-scale datasets that simultaneously contain genotype data, brain imaging data, and matched complex traits from the same individual. However, most existing multivariable MR methods have been developed for two-sample setting or a small number of exposures. In this study, we introduce a one-sample multivariable MR method based on partial least squares and Lasso regression (MR-PL). MR-PL is capable of considering the correlation among exposures (e.g., brain imaging features) when the number of exposures is extremely upscaled, while also correcting for winner's curse bias. We performed extensive and systematic simulations, and demonstrated the robustness and reliability of our method. Comprehensive simulations confirmed that MR-PL can generate more precise causal estimates with lower false positive rates than alternative approaches. Finally, we applied MR-PL to the datasets from UK Biobank to reveal the causal effects of 36 white matter tracts on 180 complex traits, and showed putative white matter tracts that are implicated in smoking, blood vascular function-related traits, and eating behaviors.
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Affiliation(s)
- Anyi Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Yucheng T. Yang
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
| | - Xing-Ming Zhao
- Department of Neurology, Zhongshan Hospital and Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, People’s Republic of China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, People’s Republic of China
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, People’s Republic of China
- International Human Phenome Institutes (Shanghai), Shanghai, People’s Republic of China
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Zhao X, Song L, Yang A, Zhang Z, Zhang J, Yang YT, Zhao XM. Prioritizing genes associated with brain disorders by leveraging enhancer-promoter interactions in diverse neural cells and tissues. Genome Med 2023; 15:56. [PMID: 37488639 PMCID: PMC10364416 DOI: 10.1186/s13073-023-01210-6] [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] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/10/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them. METHODS By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues. RESULTS We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied. CONCLUSIONS Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets.
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Affiliation(s)
- Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Anyi Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Zichao Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Jinglong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
- Internatioal Human Phenome Institutes (Shanghai), Shanghai, 200433, China.
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