1
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Huang Y, Wang Q, Peng Y, Du W, Wang Q, Qi J, Hao Z, Wang Y. Spatiotemporal expression patterns of genes coding for plasmalemmal chloride transporters and channels in neurological diseases. Mol Brain 2023; 16:30. [PMID: 36934242 PMCID: PMC10024392 DOI: 10.1186/s13041-023-01018-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 03/07/2023] [Indexed: 03/19/2023] Open
Abstract
Neuronal voltage changes which are dependent on chloride transporters and channels are involved in forming neural functions during early development and maintaining their stability until adulthood. The intracellular chloride concentration maintains a steady state, which is delicately regulated by various genes coding for chloride transporters and channels (GClTC) on the plasmalemma; however, the synergistic effect of these genes in central nervous system disorders remains unclear. In this study, we first defined 10 gene clusters with similar temporal expression patterns, and identified 41 GClTC related to brain developmental process. Then, we found 4 clusters containing 22 GClTC were enriched for the neuronal functions. The GClTC from different clusters presented distinct cell type preferences and anatomical heterogeneity. We also observed strong correlations between clustered genes and diseases, most of which were nervous system disorders. Finally, we found that one of the most well-known GClTC, SLC12A2, had a more profound effect on glial cell-related diseases than on neuron-related diseases, which was in accordance with our observation that SLC12A2 was mainly expressed in oligodendrocytes during brain development. Our findings provide a more comprehensive understanding of the temporal and spatial expression characteristics of GClTC, which can help us understand the complex roles of GClTC in the development of the healthy human brain and the etiology of brain disorders.
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Affiliation(s)
- Yanruo Huang
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Qihang Wang
- State Key Laboratory of Molecular Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Yunsong Peng
- Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Hefei, 230026, China
- Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China
| | - Wenjie Du
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Qi Wang
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Jiangtao Qi
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Zijian Hao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, 200433, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
| | - Yingwei Wang
- Department of Anesthesiology, Huashan Hospital, Fudan University, Shanghai, 200040, China.
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2
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Ren H, Walker BL, Cang Z, Nie Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat Commun 2022; 13:4076. [PMID: 35835774 PMCID: PMC9283532 DOI: 10.1038/s41467-022-31739-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Accepted: 06/30/2022] [Indexed: 11/27/2022] Open
Abstract
One major challenge in analyzing spatial transcriptomic datasets is to simultaneously incorporate the cell transcriptome similarity and their spatial locations. Here, we introduce SpaceFlow, which generates spatially-consistent low-dimensional embeddings by incorporating both expression similarity and spatial information using spatially regularized deep graph networks. Based on the embedding, we introduce a pseudo-Spatiotemporal Map that integrates the pseudotime concept with spatial locations of the cells to unravel spatiotemporal patterns of cells. By comparing with multiple existing methods on several spatial transcriptomic datasets at both spot and single-cell resolutions, SpaceFlow is shown to produce a robust domain segmentation and identify biologically meaningful spatiotemporal patterns. Applications of SpaceFlow reveal evolving lineage in heart developmental data and tumor-immune interactions in human breast cancer data. Our study provides a flexible deep learning framework to incorporate spatiotemporal information in analyzing spatial transcriptomic data.
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Affiliation(s)
- Honglei Ren
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA
| | - Benjamin L Walker
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA
| | - Zixuan Cang
- Department of Mathematics, North Carolina State University, Raleigh, NC, 27695, USA
| | - Qing Nie
- The NSF-Simons Center for Multiscale Cell Fate Research, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Mathematics, University of California Irvine, Irvine, CA, 92627, USA.
- Department of Developmental and Cell Biology, University of California Irvine, Irvine, CA, 92627, USA.
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3
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Xie Q, Liu L, Chen X, Cheng Y, Li J, Zhang X, Xu N, Han Y, Liu H, Wei L, Peng J, Shen A. Identification of Cysteine Protease Inhibitor CST2 as a Potential Biomarker for Colorectal Cancer. J Cancer 2021; 12:5144-5152. [PMID: 34335931 PMCID: PMC8317524 DOI: 10.7150/jca.53983] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 05/26/2021] [Indexed: 12/14/2022] Open
Abstract
Additional biomarkers for the development and progression of colorectal cancer (CRC) remain to be identified. Hence, the current study aimed to identify potential diagnostic markers for CRC. Analyses of cysteine protease inhibitor [cystatins (CSTs)] expression in CRC samples and its correlation with cancer stage or survival in patients with CRC demonstrated that CRC tissues had greater CST1 and CST2 mRNA expression compared to noncancerous adjacent tissues, while higher CST2 mRNA expression in CRC tissues was correlated with advanced stages and disease-free survival in patients with CRC, encouraging further exploration on the role of CST2 in CRC. Through an online database search and tissue microarray (TMA), we confirmed that CRC samples had higher CST2 expression compared to noncancerous adjacent tissue or normal colorectal tissues at both the mRNA and protein levels. TMA also revealed that colorectal adenoma, CRC, and metastatic CRC tissues exhibited a significantly increased CST2 protein expression. Accordingly, survival analysis demonstrated that the increase in CST2 protein expression was correlated with shorter overall survival of patients with CRC. Moreover, our results found a significant upregulation of CST2 in multiple cancer tissues. Taken together, these findings suggest the potential role of CST2 as a diagnostic and prognostic biomarker for CRC.
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Affiliation(s)
- Qiurong Xie
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Liya Liu
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Xiaoping Chen
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Ying Cheng
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Jiapeng Li
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Department of Physical Education, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Xiuli Zhang
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Nanhui Xu
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Yuying Han
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Huixin Liu
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Lihui Wei
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Jun Peng
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
| | - Aling Shen
- Academy of Integrative Medicine, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China.,Fujian Key Laboratory of Integrative Medicine on Geriatrics, Fujian University of Traditional Chinese Medicine, 1 Qiuyang Road, Minhou Shangjie, Fuzhou, Fujian 350122, China
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4
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Molecular characterization of the stress network in individuals at risk for schizophrenia. Neurobiol Stress 2021; 14:100307. [PMID: 33644266 PMCID: PMC7893486 DOI: 10.1016/j.ynstr.2021.100307] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 01/14/2021] [Accepted: 02/03/2021] [Indexed: 01/24/2023] Open
Abstract
The biological mechanisms underlying inter-individual differences in human stress reactivity remain poorly understood. We aimed to identify the molecular underpinning of aberrant neural stress sensitivity in individuals at risk for schizophrenia. Linking mRNA expression data from the Allen Human Brain Atlas to task-based fMRI revealed 201 differentially expressed genes in cortex-specific brain regions differentially activated by stress in individuals with low (healthy siblings of schizophrenia patients) or high (healthy controls) stress sensitivity. These genes are associated with stress-related psychiatric disorders (e.g. schizophrenia and anxiety) and include markers for specific neuronal populations (e.g. ADCYAP1, GABRB1, SSTR1, and TNFRSF12A), neurotransmitter receptors (e.g. GRIN3A, SSTR1, GABRB1, and HTR1E), and signaling factors that interact with the corticosteroid receptor and hypothalamic-pituitary-adrenal axis (e.g. ADCYAP1, IGSF11, and PKIA). Overall, the identified genes potentially underlie altered stress reactivity in individuals at risk for schizophrenia and other psychiatric disorders and play a role in mounting an adaptive stress response in at-risk individuals, making them potentially druggable targets for stress-related diseases.
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5
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Exploring the secrets of brain transcriptional regulation: developing methodologies, recent significant findings, and perspectives. Brain Struct Funct 2021; 226:313-322. [PMID: 33547496 DOI: 10.1007/s00429-021-02230-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 01/22/2021] [Indexed: 10/22/2022]
Abstract
Exploring and revealing the secret of the function of the human brain has been the dream of mankind and science. Delineating brain transcriptional regulation has been extremely challenging, but recent technological advances have facilitated a deeper investigation of molecular processes in the brain. Tracing the molecular regulatory mechanisms of different gene expression profiles in the brain is divergent and has made it possible to connect spatial and temporal variations in gene expression to distributed properties of brain structure and function. Here, we review the molecular diversity of the brain among rodents, non-human primates and humans. We also discuss the molecular mechanism of non-coding DNA/RNA at the transcriptional/post-transcriptional level based on recent technical advances to highlight an improved understanding of the complex transcriptional network in the brain. Spatiotemporal and single-cell transcriptomics have attempted to gain novel insight into the development and evolution of the brain as well as the progression of human diseases. Although it is clear that the field is developing and challenges remain to be resolved, the impressive recent progress provides a solid foundation to better understand the brain and evidence-based recommendations for the diagnosis and treatment of brain diseases.
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6
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Ščupáková K, Balluff B, Tressler C, Adelaja T, Heeren RM, Glunde K, Ertaylan G. Cellular resolution in clinical MALDI mass spectrometry imaging: the latest advancements and current challenges. Clin Chem Lab Med 2020; 58:914-929. [PMID: 31665113 PMCID: PMC9867918 DOI: 10.1515/cclm-2019-0858] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Accepted: 10/07/2019] [Indexed: 02/07/2023]
Abstract
Mass spectrometry (MS) is the workhorse of metabolomics, proteomics and lipidomics. Mass spectrometry imaging (MSI), its extension to spatially resolved analysis of tissues, is a powerful tool for visualizing molecular information within the histological context of tissue. This review summarizes recent developments in MSI and highlights current challenges that remain to achieve molecular imaging at the cellular level of clinical specimens. We focus on matrix-assisted laser desorption/ionization (MALDI)-MSI. We discuss the current status of each of the analysis steps and remaining challenges to reach the desired level of cellular imaging. Currently, analyte delocalization and degradation, matrix crystal size, laser focus restrictions and detector sensitivity are factors that are limiting spatial resolution. New sample preparation devices and laser optic systems are being developed to push the boundaries of these limitations. Furthermore, we review the processing of cellular MSI data and images, and the systematic integration of these data in the light of available algorithms and databases. We discuss roadblocks in the data analysis pipeline and show how technology from other fields can be used to overcome these. Finally, we conclude with curative and community efforts that are needed to enable contextualization of the information obtained.
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Affiliation(s)
- Klára Ščupáková
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Benjamin Balluff
- Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands
| | - Caitlin Tressler
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Tobi Adelaja
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ron M.A. Heeren
- Corresponding author: Ron M.A. Heeren, Maastricht MultiModal Molecular Imaging Institute (M4I), University of Maastricht, Maastricht, The Netherlands,
| | - Kristine Glunde
- Russell H. Morgan Department of Radiology and Radiological Science, Division of Cancer Imaging Research, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; and The Sidney Kimmel Comprehensive Cancer Center, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Gökhan Ertaylan
- Unit Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
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7
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Wu Y, Tamayo P, Zhang K. Visualizing and Interpreting Single-Cell Gene Expression Datasets with Similarity Weighted Nonnegative Embedding. Cell Syst 2018; 7:656-666.e4. [PMID: 30528274 PMCID: PMC6311449 DOI: 10.1016/j.cels.2018.10.015] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 08/15/2018] [Accepted: 10/29/2018] [Indexed: 01/19/2023]
Abstract
High-throughput single-cell gene expression profiling has enabled the definition of new cell types and developmental trajectories. Visualizing these datasets is crucial to biological interpretation, and a popular method is t-stochastic neighbor embedding (t-SNE), which visualizes local patterns well but distorts global structure, such as distances between clusters. We developed similarity weighted nonnegative embedding (SWNE), which enhances interpretation of datasets by embedding the genes and factors that separate cell states on the visualization alongside the cells and maintains fidelity when visualizing local and global structure for both developmental trajectories and discrete cell types. SWNE uses nonnegative matrix factorization to decompose the gene expression matrix into biologically relevant factors; embeds the cells, genes, and factors in a 2D visualization; and uses a similarity matrix to smooth the embeddings. We demonstrate SWNE on single-cell RNA-seq data from hematopoietic progenitors and human brain cells. SWNE is available as an R package at github.com/yanwu2014/swne.
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Affiliation(s)
- Yan Wu
- Department of Bioengineering, University of California, San Diego, San Diego, CA, USA
| | - Pablo Tamayo
- Moores Cancer Center, University of California, San Diego, San Diego, CA, USA; School of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Kun Zhang
- Department of Bioengineering, University of California, San Diego, San Diego, CA, USA.
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8
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Jia X, Han Q, Lu Z. Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps. BMC Bioinformatics 2018; 19:512. [PMID: 30558536 PMCID: PMC6296107 DOI: 10.1186/s12859-018-2495-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2018] [Accepted: 11/16/2018] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND For analyzing these gene expression data sets under different samples, clustering and visualizing samples and genes are important methods. However, it is difficult to integrate clustering and visualizing techniques when the similarities of samples and genes are defined by PCC(Person correlation coefficient) measure. RESULTS Here, for rare samples of gene expression data sets, we use MG-PCC (mini-groups that are defined by PCC) algorithm to divide them into mini-groups, and use t-SNE-SSP maps to display these mini-groups, where the idea of MG-PCC algorithm is that the nearest neighbors should be in the same mini-groups, t-SNE-SSP map is selected from a series of t-SNE(t-statistic Stochastic Neighbor Embedding) maps of standardized samples, and these t-SNE maps have different perplexity parameter. Moreover, for PCC clusters of mass genes, they are displayed by t-SNE-SGI map, where t-SNE-SGI map is selected from a series of t-SNE maps of standardized genes, and these t-SNE maps have different initialization dimensions. Here, t-SNE-SSP and t-SNE-SGI maps are selected by A-value, where A-value is modeled from areas of clustering projections, and t-SNE-SSP and t-SNE-SGI maps are such t-SNE map that has the smallest A-value. CONCLUSIONS From the analysis of cancer gene expression data sets, we demonstrate that MG-PCC algorithm is able to put tumor and normal samples into their respective mini-groups, and t-SNE-SSP(or t-SNE-SGI) maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Furthermore, t-SNE-SS(m)(or t-SNE-SG(n)) maps are able to construct independent tree diagrams of the nearest sample(or gene) neighbors, where each tree diagram is corresponding to a mini-group of samples(or genes).
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Affiliation(s)
- Xingang Jia
- School of Mathematics, Southeast University, Nanjing, 210096, People's Republic of China.
| | - Qiuhong Han
- Department of Mathematics, Nanjing Forestry University, Nanjing, 210037, People's Republic of China
| | - Zuhong Lu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, People's Republic of China
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9
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Mahinrad S, Bulk M, van der Velpen I, Mahfouz A, van Roon-Mom W, Fedarko N, Yasar S, Sabayan B, van Heemst D, van der Weerd L. Natriuretic Peptides in Post-mortem Brain Tissue and Cerebrospinal Fluid of Non-demented Humans and Alzheimer's Disease Patients. Front Neurosci 2018; 12:864. [PMID: 30534047 PMCID: PMC6275179 DOI: 10.3389/fnins.2018.00864] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2018] [Accepted: 11/05/2018] [Indexed: 12/13/2022] Open
Abstract
Animal studies suggest the involvement of natriuretic peptides (NP) in several brain functions that are known to be disturbed during Alzheimer's disease (AD). However, it remains unclear whether such findings extend to humans. In this study, we aimed to: (1) map the gene expression and localization of NP and their receptors (NPR) in human post-mortem brain tissue; (2) compare the relative amounts of NP and NPR between the brain tissue of AD patients and non-demented controls, and (3) compare the relative amounts of NP between the cerebrospinal fluid (CSF) of AD patients and non-demented controls. Using the publicly available Allen Human Brain Atlas dataset, we mapped the gene expression of NP and NPR in healthy humans. Using immunohistochemistry, we visualized the localization of NP and NPR in the frontal cortex of AD patients (n = 10, mean age 85.8 ± 6.2 years) and non-demented controls (mean age = 80.2 ± 9.1 years). Using Western blotting and ELISA, we quantified the relative amounts of NP and NPR in the brain tissue and CSF of these AD patients and non-demented controls. Our results showed that NP and NPR genes were ubiquitously expressed throughout the brain in healthy humans. NP and NPR were present in various cellular structures including in neurons, astrocyte-like structures, and cerebral vessels in both AD patients and non-demented controls. Furthermore, we found higher amounts of NPR type-A in the brain of AD patients (p = 0.045) and lower amounts of NP type-B in the CSF of AD patients (p = 0.029). In conclusion, this study shows the abundance of NP and NPR in the brain of humans suggesting involvement of NP in various brain functions. In addition, our findings suggest alterations of NP levels in the brain of AD patients. The role of NP in the development and progression of AD remains to be elucidated.
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Affiliation(s)
- Simin Mahinrad
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Marjolein Bulk
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
- Percuros BV, Leiden, Netherlands
| | - Isabelle van der Velpen
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Ahmed Mahfouz
- Leiden Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands
- Delft Bioinformatics Lab, Delft University of Technology, Delft, Netherlands
| | - Willeke van Roon-Mom
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
| | - Neal Fedarko
- Clinical Research Core Laboratory, Johns Hopkins Institute for Clinical and Translational Research, Baltimore, MD, United States
| | - Sevil Yasar
- Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Behnam Sabayan
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Diana van Heemst
- Department of Internal Medicine, Section Gerontology and Geriatrics, Leiden University Medical Center, Leiden, Netherlands
| | - Louise van der Weerd
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
- Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands
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10
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Huisman SMH, Mahfouz A, Batmanghelich NK, Lelieveldt BPF, Reinders MJT. A structural equation model for imaging genetics using spatial transcriptomics. Brain Inform 2018; 5:13. [PMID: 30390165 PMCID: PMC6429169 DOI: 10.1186/s40708-018-0091-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 10/21/2018] [Indexed: 11/10/2022] Open
Abstract
Imaging genetics deals with relationships between genetic variation and imaging variables, often in a disease context. The complex relationships between brain volumes and genetic variants have been explored with both dimension reduction methods and model-based approaches. However, these models usually do not make use of the extensive knowledge of the spatio-anatomical patterns of gene activity. We present a method for integrating genetic markers (single nucleotide polymorphisms) and imaging features, which is based on a causal model and, at the same time, uses the power of dimension reduction. We use structural equation models to find latent variables that explain brain volume changes in a disease context, and which are in turn affected by genetic variants. We make use of publicly available spatial transcriptome data from the Allen Human Brain Atlas to specify the model structure, which reduces noise and improves interpretability. The model is tested in a simulation setting and applied on a case study of the Alzheimer’s Disease Neuroimaging Initiative.
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Affiliation(s)
- Sjoerd M H Huisman
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.,Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
| | - Ahmed Mahfouz
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.,Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands
| | | | - Boudewijn P F Lelieveldt
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands.,Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.,Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Marcel J T Reinders
- Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands. .,Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.
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11
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Mühleisen TW, Reinbold CS, Forstner AJ, Abramova LI, Alda M, Babadjanova G, Bauer M, Brennan P, Chuchalin A, Cruceanu C, Czerski PM, Degenhardt F, Fischer SB, Fullerton JM, Gordon SD, Grigoroiu-Serbanescu M, Grof P, Hauser J, Hautzinger M, Herms S, Hoffmann P, Kammerer-Ciernioch J, Khusnutdinova E, Kogevinas M, Krasnov V, Lacour A, Laprise C, Leber M, Lissowska J, Lucae S, Maaser A, Maier W, Martin NG, Mattheisen M, Mayoral F, McKay JD, Medland SE, Mitchell PB, Moebus S, Montgomery GW, Müller-Myhsok B, Oruc L, Pantelejeva G, Pfennig A, Pojskic L, Polonikov A, Reif A, Rivas F, Rouleau GA, Schenk LM, Schofield PR, Schwarz M, Streit F, Strohmaier J, Szeszenia-Dabrowska N, Tiganov AS, Treutlein J, Turecki G, Vedder H, Witt SH, Schulze TG, Rietschel M, Nöthen MM, Cichon S. Gene set enrichment analysis and expression pattern exploration implicate an involvement of neurodevelopmental processes in bipolar disorder. J Affect Disord 2018; 228:20-25. [PMID: 29197740 DOI: 10.1016/j.jad.2017.11.068] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/20/2017] [Accepted: 11/12/2017] [Indexed: 11/22/2022]
Abstract
BACKGROUND Bipolar disorder (BD) is a common and highly heritable disorder of mood. Genome-wide association studies (GWAS) have identified several independent susceptibility loci. In order to extract more biological information from GWAS data, multi-locus approaches represent powerful tools since they utilize knowledge about biological processes to integrate functional sets of genes at strongly to moderately associated loci. METHODS We conducted gene set enrichment analyses (GSEA) using 2.3 million single-nucleotide polymorphisms, 397 Reactome pathways and 24,025 patients with BD and controls. RNA expression of implicated individual genes and gene sets were examined in post-mortem brains across lifespan. RESULTS Two pathways showed a significant enrichment after correction for multiple comparisons in the GSEA: GRB2 events in ERBB2 signaling, for which 6 of 21 genes were BD associated (PFDR = 0.0377), and NCAM signaling for neurite out-growth, for which 11 out of 62 genes were BD associated (PFDR = 0.0451). Most pathway genes showed peaks of RNA co-expression during fetal development and infancy and mapped to neocortical areas and parts of the limbic system. LIMITATIONS Pathway associations were technically reproduced by two methods, although they were not formally replicated in independent samples. Gene expression was explored in controls but not in patients. CONCLUSIONS Pathway analysis in large GWAS data of BD and follow-up of gene expression patterns in healthy brains provide support for an involvement of neurodevelopmental processes in the etiology of this neuropsychiatric disease. Future studies are required to further evaluate the relevance of the implicated genes on pathway functioning and clinical aspects of BD.
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Affiliation(s)
- Thomas W Mühleisen
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
| | - Céline S Reinbold
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
| | - Andreas J Forstner
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Lilia I Abramova
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Martin Alda
- Department of Psychiatry, Dalhousie University, Halifax, Canada; National Institute of Mental Health, Klecany, Czech Republic
| | - Gulja Babadjanova
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Paul Brennan
- Genetic Epidemiology Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Alexander Chuchalin
- Institute of Pulmonology, Russian State Medical University, Moscow, Russian Federation
| | - Cristiana Cruceanu
- Montreal Neurological Institute, McGill University, Montreal, Canada; Department of Human Genetics, McGill University, Montreal, Canada; McGill Group for Suicide Studies & Douglas Research Institute, Montreal, Canada
| | - Piotr M Czerski
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Franziska Degenhardt
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Sascha B Fischer
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland
| | - Janice M Fullerton
- Neuroscience Research Australia, Sydney, Australia; School of Medical Sciences Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Scott D Gordon
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Maria Grigoroiu-Serbanescu
- Biometric Psychiatric Genetics Research Unit, Alexandru Obregia Clinical Psychiatric Hospital, Bucharest, Romania
| | - Paul Grof
- The International Group for the Study of Lithium-Treated Patients (IGSLI), Berlin, Germany; Mood Disorders Center of Ottawa, Ottawa, Ontario, Canada K1G 4G3; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada M5T 1R8
| | - Joanna Hauser
- Laboratory of Psychiatric Genetics, Department of Psychiatry, Poznan University of Medical Sciences, Poznan, Poland
| | - Martin Hautzinger
- Department of Psychology, Clinical Psychology and Psychotherapy, Eberhard Karls University Tübingen, Tübingen, Germany
| | - Stefan Herms
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Per Hoffmann
- Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | | | - Elza Khusnutdinova
- Institute of Biochemistry and Genetics, Ufa Scientific Center of Russian Academy of Sciences, Ufa, Russian Federation; Department of Genetics and Fundamental Medicine of Bashkir State University, Ufa, Russian Federation
| | - Manolis Kogevinas
- Center for Research in Environmental Epidemiology (CREAL), Barcelona, Spain
| | - Valery Krasnov
- Moscow Research Institute of Psychiatry, Moscow, Russian Federation
| | - André Lacour
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Catherine Laprise
- Département des sciences fondamentales, Université du Québec à Chicoutimi, Saguenay, QC, Canada; Centre intégré universitaire de santé et services sociaux du Saguenay-Lac-Saint-Jean, Saguenay, Québec, Canada
| | - Markus Leber
- Department of Psychiatry & Psychotherapy, University of Cologne, Cologne, Germany
| | - Jolanta Lissowska
- Department of Cancer Epidemiology and Prevention, Maria Sklodowska-Curie Memorial Cancer Centre and Institute of Oncology Warsaw, Warsaw, Poland
| | | | - Anna Maaser
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Wolfgang Maier
- Department of Psychiatry, University of Bonn, Bonn, Germany
| | - Nicholas G Martin
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Manuel Mattheisen
- Department of Biomedicine and Centre for integrative Sequencing, iSEQ, Aarhus University, Aarhus, Denmark; The Lundbeck Foundation Initiative for integrative Psychiatric Research, iPSYCH, Aarhus and Copenhagen, Denmark
| | - Fermin Mayoral
- Department of Psychiatry, Hospital Regional Universitario, Biomedical Institute of Malaga, Malaga, Spain
| | - James D McKay
- Genetic Cancer Susceptibility Group, International Agency for Research on Cancer (IARC), Lyon, France
| | - Sarah E Medland
- Queensland Institute of Medical Research (QIMR), Brisbane, Australia
| | - Philip B Mitchell
- School of Psychiatry, University of New South Wales, Randwick, Australia; Black Dog Institute, Prince of Wales Hospital, Randwick, Australia
| | - Susanne Moebus
- Institute of Medical Informatics, Biometry and Epidemiology, University Duisburg-Essen, Essen, Germany
| | | | - Bertram Müller-Myhsok
- Max Planck Institute of Psychiatry, Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany; University of Liverpool, Institute of Translational Medicine, Liverpool, United Kingdom
| | - Lilijana Oruc
- Psychiatric Clinic, Clinical Center University of Sarajevo, Bolnička 25, 71000 Sarajevo, Bosnia and Herzegovina
| | - Galina Pantelejeva
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Andrea Pfennig
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, TU Dresden, Dresden, Germany
| | - Lejla Pojskic
- Institute for Genetic Engineering and Biotechnology, University of Sarajevo, Zmaja od Bosne 8 - Campus, 71000 Sarajevo, Bosnia and Herzegovina
| | - Alexey Polonikov
- Department of Biology, Medical Genetics and Ecology, Kursk State Medical University, Kursk, Russian Federation; Research Institute for Genetic and Molecular Epidemiology, Kursk State Medical University, Kursk, Russian Federation
| | - Andreas Reif
- Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt am Main, Frankfurt am Main, Germany
| | - Fabio Rivas
- Department of Psychiatry, Hospital Regional Universitario, Biomedical Institute of Malaga, Malaga, Spain
| | - Guy A Rouleau
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Lorena M Schenk
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Peter R Schofield
- Neuroscience Research Australia, Sydney, Australia; School of Medical Sciences Faculty of Medicine, University of New South Wales, Sydney, Australia
| | | | - Fabian Streit
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Jana Strohmaier
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | | | - Alexander S Tiganov
- Russian Academy of Medical Sciences, Mental Health Research Center, Moscow, Russian Federation
| | - Jens Treutlein
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Gustavo Turecki
- Department of Human Genetics, McGill University, Montreal, Canada; McGill Group for Suicide Studies & Douglas Research Institute, Montreal, Canada; Department of Psychiatry, McGill University, Montreal, Canada
| | | | - Stephanie H Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Thomas G Schulze
- Institute of Psychiatric Phenomics and Genomics, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany
| | - Sven Cichon
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Biomedicine & Institute of Medical Genetics and Pathology, Human Genomics Research Group and Division of Medical Genetics, Department of Biomedicine, University and University Hospital Basel, Basel, Switzerland; Institute of Human Genetics, Institute of Human Genetics, University of Bonn School of Medicine & University Hospital Bonn, Bonn, Germany; Department of Genomics, Life & Brain Research Center, University of Bonn, Bonn, Germany.
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12
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Abdelmoula WM, Pezzotti N, Hölt T, Dijkstra J, Vilanova A, McDonnell LA, Lelieveldt BPF. Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution. J Proteome Res 2018; 17:1054-1064. [PMID: 29430923 PMCID: PMC5838640 DOI: 10.1021/acs.jproteome.7b00725] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
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Technological
advances in mass spectrometry imaging (MSI) have
contributed to growing interest in 3D MSI. However, the large size
of 3D MSI data sets has made their efficient analysis and visualization
and the identification of informative molecular patterns computationally
challenging. Hierarchical stochastic neighbor embedding (HSNE), a
nonlinear dimensionality reduction technique that aims at finding
hierarchical and multiscale representations of large data sets, is
a recent development that enables the analysis of millions of data
points, with manageable time and memory complexities. We demonstrate
that HSNE can be used to analyze large 3D MSI data sets at full mass
spectral and spatial resolution. To benchmark the technique as well
as demonstrate its broad applicability, we have analyzed a number
of publicly available 3D MSI data sets, recorded from various biological
systems and spanning different mass-spectrometry ionization techniques.
We demonstrate that HSNE is able to rapidly identify regions of interest
within these large high-dimensionality data sets as well as aid the
identification of molecular ions that characterize these regions of
interest; furthermore, through clearly separating measurement artifacts,
the HSNE analysis exhibits a degree of robustness to measurement batch
effects, spatially correlated noise, and mass spectral misalignment.
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Affiliation(s)
- Walid M Abdelmoula
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School , Boston, Massachusetts 02115, United States
| | - Nicola Pezzotti
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Thomas Hölt
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | - Jouke Dijkstra
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands
| | - Anna Vilanova
- Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
| | | | - Boudewijn P F Lelieveldt
- Division of Image Processing, Department of Radiology, Leiden University Medical Center , 2333 ZA Leiden, The Netherlands.,Computer Graphics and Visualization Group, Faculty of EEMCS, Delft University of Technology , 2628 CN Delft, The Netherlands
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13
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Keo A, Aziz NA, Dzyubachyk O, van der Grond J, van Roon-Mom WMC, Lelieveldt BPF, Reinders MJT, Mahfouz A. Co-expression Patterns between ATN1 and ATXN2 Coincide with Brain Regions Affected in Huntington's Disease. Front Mol Neurosci 2017; 10:399. [PMID: 29249939 PMCID: PMC5714896 DOI: 10.3389/fnmol.2017.00399] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 11/15/2017] [Indexed: 02/04/2023] Open
Abstract
Cytosine-adenine-guanine (CAG) repeat expansions in the coding regions of nine polyglutamine (polyQ) genes (HTT, ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATN1, AR, and TBP) are the cause of several neurodegenerative diseases including Huntington’s disease (HD), six different spinocerebellar ataxias (SCAs), dentatorubral-pallidoluysian atrophy, and spinobulbar muscular atrophy. The expanded CAG repeat length in the causative gene is negatively related to the age-at-onset (AAO) of clinical symptoms. In addition to the expanded CAG repeat length in the causative gene, the normal CAG repeats in the other polyQ genes can affect the AAO, suggesting functional interactions between the polyQ genes. However, there is no detailed assessment of the relationships among polyQ genes in pathologically relevant brain regions. We used gene co-expression analysis to study the functional relationships among polyQ genes in different brain regions using the Allen Human Brain Atlas (AHBA), a spatial map of gene expression in the healthy brain. We constructed co-expression networks for seven anatomical brain structures, as well as a region showing a specific pattern of atrophy in HD patients detected by magnetic resonance imaging (MRI) of the brain. In this HD-associated region, we found that ATN1 and ATXN2 were co-expressed and shared co-expression partners which were enriched for DNA repair genes. We observed a similar co-expression pattern in the frontal lobe, parietal lobe, and striatum in which this relation was most pronounced. Given that the co-expression patterns for these anatomical structures were similar to those for the HD-associated region, our results suggest that their disruption is likely involved in HD pathology. Moreover, ATN1 and ATXN2 also shared many co-expressed genes with HTT, the causative gene of HD, across the brain. Although this triangular relationship among these three polyQ genes may also be dysregulated in other polyQ diseases, stronger co-expression patterns between ATN1 and ATXN2 observed in the HD-associated region, especially in the striatum, may be more specific to HD.
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Affiliation(s)
- Arlin Keo
- Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands.,Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
| | - N Ahmad Aziz
- Department of Neurology, Leiden University Medical Center, Leiden, Netherlands
| | - Oleh Dzyubachyk
- Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | | | | | - Boudewijn P F Lelieveldt
- Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands.,Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands.,Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
| | - Marcel J T Reinders
- Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands.,Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
| | - Ahmed Mahfouz
- Computational Biology Center, Leiden University Medical Center, Leiden, Netherlands.,Delft Bioinformatics Lab, Department of Intelligent Systems, Delft University of Technology, Delft, Netherlands
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