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Fessler JL, Stiles MA, Agbaga MP, Ahmad M, Sherry DM. The Spinocerebellar Ataxia 34-Causing W246G ELOVL4 Mutation Does Not Alter Cerebellar Neuron Populations in a Rat Model. CEREBELLUM (LONDON, ENGLAND) 2024; 23:2082-2094. [PMID: 38850484 DOI: 10.1007/s12311-024-01708-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/30/2024] [Indexed: 06/10/2024]
Abstract
Spinocerebellar ataxia 34 (SCA34) is an autosomal dominant disease that arises from point mutations in the fatty acid elongase, Elongation of Very Long Chain Fatty Acids 4 (ELOVL4), which is essential for the synthesis of Very Long Chain-Saturated Fatty Acids (VLC-SFA) and Very Long Chain-Polyunsaturated Fatty Acids (VLC-PUFA) (28-34 carbons long). SCA34 is considered a neurodegenerative disease. However, a novel rat model of SCA34 (SCA34-KI rat) with knock-in of the W246G ELOVL4 mutation that causes human SCA34 shows early motor impairment and aberrant synaptic transmission and plasticity without overt neurodegeneration. ELOVL4 is expressed in neurogenic regions of the developing brain, is implicated in cell cycle regulation, and ELOVL4 mutations that cause neuroichthyosis lead to developmental brain malformation, suggesting that aberrant neuron generation due to ELOVL4 mutations might contribute to SCA34. To test whether W246G ELOVL4 altered neuronal generation or survival in the cerebellum, we compared the numbers of Purkinje cells, unipolar brush cells, molecular layer interneurons, granule and displaced granule cells in the cerebellum of wildtype, heterozygous, and homozygous SCA34-KI rats at four months of age, when motor impairment is already present. An unbiased, semi-automated method based on Cellpose 2.0 and ImageJ was used to quantify neuronal populations in cerebellar sections immunolabeled for known neuron-specific markers. Neuronal populations and cortical structure were unaffected by the W246G ELOVL4 mutation by four months of age, a time when synaptic and motor dysfunction are already present, suggesting that SCA34 pathology originates from synaptic dysfunction due to VLC-SFA deficiency, rather than aberrant neuronal production or neurodegeneration.
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Affiliation(s)
- Jennifer L Fessler
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 S.L. Young Blvd, BMSB-100, Oklahoma City, OK, 73104, United States of America.
| | - Megan A Stiles
- Department of Ophthalmology, Dean McGee Eye Institute, Oklahoma City, OK, 73104, United States of America
| | - Martin-Paul Agbaga
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 S.L. Young Blvd, BMSB-100, Oklahoma City, OK, 73104, United States of America
- Department of Ophthalmology, Dean McGee Eye Institute, Oklahoma City, OK, 73104, United States of America
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America
| | - Mohiuddin Ahmad
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 S.L. Young Blvd, BMSB-100, Oklahoma City, OK, 73104, United States of America
| | - David M Sherry
- Department of Cell Biology, University of Oklahoma Health Sciences Center, 940 S.L. Young Blvd, BMSB-100, Oklahoma City, OK, 73104, United States of America.
- Department of Neurosurgery, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America.
- Department of Pharmaceutical Sciences, University of Oklahoma Health Sciences Center, Oklahoma City, OK, 73104, United States of America.
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Hao S, Zhu X, Huang Z, Yang Q, Liu H, Wu Y, Zhan Y, Dong Y, Li C, Wang H, Haasdijk E, Wu Z, Li S, Yan H, Zhu L, Guo S, Wang Z, Ye A, Lin Y, Cui L, Tan X, Liu H, Wang M, Chen J, Zhong Y, Du W, Wang G, Lai T, Cao M, Yang T, Xu Y, Li L, Yu Q, Zhuang Z, Xia Y, Lei Y, An Y, Cheng M, Zhao Y, Han L, Yuan Y, Song X, Song Y, Gu L, Liu C, Lin X, Wang R, Wang Z, Wang Y, Li S, Li H, Song J, Chen M, Zhou W, Yuan N, Sun S, Wang S, Chen Y, Zheng M, Fang J, Zhang R, Zhang S, Chai Q, Liu J, Wei W, He J, Zhou H, Sun Y, Liu Z, Liu C, Yao J, Liang Z, Xu X, Poo M, Li C, De Zeeuw CI, Shen Z, Liu Z, Liu L, Liu S, Sun Y, Liu C. Cross-species single-cell spatial transcriptomic atlases of the cerebellar cortex. Science 2024; 385:eado3927. [PMID: 39325889 DOI: 10.1126/science.ado3927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 08/14/2024] [Indexed: 09/28/2024]
Abstract
The molecular and cellular organization of the primate cerebellum remains poorly characterized. We obtained single-cell spatial transcriptomic atlases of macaque, marmoset, and mouse cerebella and identified primate-specific cell subtypes, including Purkinje cells and molecular-layer interneurons, that show different expression of the glutamate ionotropic receptor Delta type subunit 2 (GRID2) gene. Distinct gene expression profiles were found in anterior, posterior, and vestibular regions in all species, whereas region-selective gene expression was predominantly observed in the granular layer of primates and in the Purkinje layer of mice. Gene expression gradients in the cerebellar cortex matched well with functional connectivity gradients revealed with awake functional magnetic resonance imaging, with more lobule-specific differences between primates and mice than between two primate species. These comprehensive atlases and comparative analyses provide the basis for understanding cerebellar evolution and function.
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Affiliation(s)
| | - Xiaojia Zhu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650201, China
| | - Zhi Huang
- BGI Research, Hangzhou 310030, China
- BGI Research, Shenzhen 518083, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Qianqian Yang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Hean Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yan Wu
- BGI Research, Hangzhou 310030, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yafeng Zhan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Dong
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- Lingang Laboratory, Shanghai 200031, China
| | - Chao Li
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - He Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Elize Haasdijk
- Department of Neuroscience, Erasmus MC, 3015 GE Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, 1105 BA Amsterdam, Netherlands
| | - Zihan Wu
- Tencent AI Lab, Shenzhen 518057, China
| | - Shenglong Li
- BGI Research, Hangzhou 310030, China
- School of Biology and Biological Engineering, South China University of Technology, Guangzhou 510006, China
| | - Haotian Yan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lijing Zhu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Zefang Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Aojun Ye
- University of Chinese Academy of Sciences, Beijing 100049, China
| | | | - Luman Cui
- BGI Research, Shenzhen 518083, China
| | - Xing Tan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Mingli Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- Lingang Laboratory, Shanghai 200031, China
| | - Jing Chen
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Yanqing Zhong
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Wensi Du
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Guangling Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tingting Lai
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Mengdi Cao
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Tao Yang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Yuanfang Xu
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ling Li
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Qian Yu
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Ying Xia
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ying Lei
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yingjie An
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengnan Cheng
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yun Zhao
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Lei Han
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yue Yuan
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xinxiang Song
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yumo Song
- BGI Research, Shenzhen 518083, China
| | - Liqin Gu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Chang Liu
- BGI Research, Shenzhen 518083, China
| | | | - Ruiqi Wang
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | | | - Yang Wang
- BGI Research, Shenzhen 518083, China
| | - Shenyu Li
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Huanhuan Li
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jingjing Song
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mengni Chen
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Wanqiu Zhou
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Nini Yuan
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Suhong Sun
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shiwen Wang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Mingyuan Zheng
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jiao Fang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Ruiyi Zhang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Shuzhen Zhang
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Qinwen Chai
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jiabing Liu
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Wu Wei
- Lingang Laboratory, Shanghai 200031, China
| | - Jie He
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haibo Zhou
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangang Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhen Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chuanyu Liu
- BGI Research, Hangzhou 310030, China
- BGI Research, Shenzhen 518083, China
- Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | | | - Zhifeng Liang
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xun Xu
- BGI Research, Hangzhou 310030, China
- Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Muming Poo
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Chengyu Li
- Lingang Laboratory, Shanghai 200031, China
| | - Chris I De Zeeuw
- Department of Neuroscience, Erasmus MC, 3015 GE Rotterdam, Netherlands
- Netherlands Institute for Neuroscience, Royal Academy of Arts and Sciences, 1105 BA Amsterdam, Netherlands
| | - Zhiming Shen
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201602, China
| | - Zhiyong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Longqi Liu
- BGI Research, Hangzhou 310030, China
- Shanxi Medical University-BGI Collaborative Center for Future Medicine, Shanxi Medical University, Taiyuan 030001, China
| | - Shiping Liu
- BGI Research, Hangzhou 310030, China
- BGI Research, Shenzhen 518083, China
| | - Yidi Sun
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Cirong Liu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Genetic Evolution & Animal Models, Chinese Academy of Sciences, Shanghai, 200031, China
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3
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Lackey EP, Moreira L, Norton A, Hemelt ME, Osorno T, Nguyen TM, Macosko EZ, Lee WCA, Hull CA, Regehr WG. Specialized connectivity of molecular layer interneuron subtypes leads to disinhibition and synchronous inhibition of cerebellar Purkinje cells. Neuron 2024; 112:2333-2348.e6. [PMID: 38692278 PMCID: PMC11360088 DOI: 10.1016/j.neuron.2024.04.010] [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: 09/28/2023] [Revised: 01/12/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Abstract
Molecular layer interneurons (MLIs) account for approximately 80% of the inhibitory interneurons in the cerebellar cortex and are vital to cerebellar processing. MLIs are thought to primarily inhibit Purkinje cells (PCs) and suppress the plasticity of synapses onto PCs. MLIs also inhibit, and are electrically coupled to, other MLIs, but the functional significance of these connections is not known. Here, we find that two recently recognized MLI subtypes, MLI1 and MLI2, have a highly specialized connectivity that allows them to serve distinct functional roles. MLI1s primarily inhibit PCs, are electrically coupled to each other, fire synchronously with other MLI1s on the millisecond timescale in vivo, and synchronously pause PC firing. MLI2s are not electrically coupled, primarily inhibit MLI1s and disinhibit PCs, and are well suited to gating cerebellar-dependent behavior and learning. The synchronous firing of electrically coupled MLI1s and disinhibition provided by MLI2s require a major re-evaluation of cerebellar processing.
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Affiliation(s)
| | - Luis Moreira
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Aliya Norton
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Marie E Hemelt
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Tomas Osorno
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Tri M Nguyen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Evan Z Macosko
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA; Broad Institute of Harvard and MIT, Stanley Center for Psychiatric Research, Cambridge, MA, USA
| | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA; Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Court A Hull
- Department of Neurobiology, Duke University Medical School, Durham, NC, USA
| | - Wade G Regehr
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
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4
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Beau M, Herzfeld DJ, Naveros F, Hemelt ME, D’Agostino F, Oostland M, Sánchez-López A, Chung YY, Michael Maibach, Kyranakis S, Stabb HN, Martínez Lopera MG, Lajko A, Zedler M, Ohmae S, Hall NJ, Clark BA, Cohen D, Lisberger SG, Kostadinov D, Hull C, Häusser M, Medina JF. A deep-learning strategy to identify cell types across species from high-density extracellular recordings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.30.577845. [PMID: 38352514 PMCID: PMC10862837 DOI: 10.1101/2024.01.30.577845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but don't reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.
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Affiliation(s)
- Maxime Beau
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - David J. Herzfeld
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Francisco Naveros
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
- Department of Computer Engineering, Automation and Robotics, Research Centre for Information and Communication Technologies, University of Granada, Granada, Spain
| | - Marie E. Hemelt
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Federico D’Agostino
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Marlies Oostland
- Wolfson Institute for Biomedical Research, University College London, London, UK
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Young Yoon Chung
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Michael Maibach
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Stephen Kyranakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Hannah N. Stabb
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | | | - Agoston Lajko
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Marie Zedler
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Shogo Ohmae
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
| | - Nathan J. Hall
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Beverley A. Clark
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Dana Cohen
- The Leslie and Susan Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | | | - Dimitar Kostadinov
- Wolfson Institute for Biomedical Research, University College London, London, UK
- Centre for Developmental Neurobiology, King’s College London, London, UK
| | - Court Hull
- Department of Neurobiology, Duke University School of Medicine, Durham, NC, USA
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London, UK
| | - Javier F. Medina
- Department of Neuroscience, Baylor College of Medicine, Houston, TX, USA
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Schilling K. Revisiting the development of cerebellar inhibitory interneurons in the light of single-cell genetic analyses. Histochem Cell Biol 2024; 161:5-27. [PMID: 37940705 PMCID: PMC10794478 DOI: 10.1007/s00418-023-02251-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/12/2023] [Indexed: 11/10/2023]
Abstract
The present review aims to provide a short update of our understanding of the inhibitory interneurons of the cerebellum. While these cells constitute but a minority of all cerebellar neurons, their functional significance is increasingly being recognized. For one, inhibitory interneurons of the cerebellar cortex are now known to constitute a clearly more diverse group than their traditional grouping as stellate, basket, and Golgi cells suggests, and this diversity is now substantiated by single-cell genetic data. The past decade or so has also provided important information about interneurons in cerebellar nuclei. Significantly, developmental studies have revealed that the specification and formation of cerebellar inhibitory interneurons fundamentally differ from, say, the cortical interneurons, and define a mode of diversification critically dependent on spatiotemporally patterned external signals. Last, but not least, in the past years, dysfunction of cerebellar inhibitory interneurons could also be linked with clinically defined deficits. I hope that this review, however fragmentary, may stimulate interest and help focus research towards understanding the cerebellum.
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Affiliation(s)
- Karl Schilling
- Anatomisches Institut - Anatomie und Zellbiologie, Rheinische Friedrich-Wilhelms-Universität Bonn, Nussallee 10, 53115, Bonn, Germany.
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Zang Y, De Schutter E. Recent data on the cerebellum require new models and theories. Curr Opin Neurobiol 2023; 82:102765. [PMID: 37591124 DOI: 10.1016/j.conb.2023.102765] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/22/2023] [Accepted: 07/23/2023] [Indexed: 08/19/2023]
Abstract
The cerebellum has been a popular topic for theoretical studies because its structure was thought to be simple. Since David Marr and James Albus related its function to motor skill learning and proposed the Marr-Albus cerebellar learning model, this theory has guided and inspired cerebellar research. In this review, we summarize the theoretical progress that has been made within this framework of error-based supervised learning. We discuss the experimental progress that demonstrates more complicated molecular and cellular mechanisms in the cerebellum as well as new cell types and recurrent connections. We also cover its involvement in diverse non-motor functions and evidence of other forms of learning. Finally, we highlight the need to explain these new experimental findings into an integrated cerebellar model that can unify its diverse computational functions.
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Affiliation(s)
- Yunliang Zang
- Academy of Medical Engineering and Translational Medicine, Medical Faculty, Tianjin University, Tianjin 300072, China; Volen Center and Biology Department, Brandeis University, Waltham, MA 02454, USA.
| | - Erik De Schutter
- Computational Neuroscience Unit, Okinawa Institute of Science and Technology, Japan. https://twitter.com/DeschutterOIST
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7
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Zhang K, Yang Z, Gaffield MA, Gross GG, Arnold DB, Christie JM. Molecular layer disinhibition unlocks climbing-fiber-instructed motor learning in the cerebellum. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.04.552059. [PMID: 38654827 PMCID: PMC11037867 DOI: 10.1101/2023.08.04.552059] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Climbing fibers supervise cerebellar learning by providing signals to Purkinje cells (PCs) that instruct adaptive changes to mistakenly performed movements. Yet, climbing fibers are regularly active, even during well performed movements, suggesting that a mechanism dynamically regulates the ability of climbing fibers to induce corrective plasticity in response to motor errors. We found that molecular layer interneurons (MLIs), whose inhibition of PCs powerfully opposes climbing-fiber-mediated excitation, serve this function. Optogenetically suppressing the activity of floccular MLIs in mice during the vestibulo-ocular reflex (VOR) induces a learned increase in gain despite the absence of performance errors. Suppressing MLIs when the VOR is mistakenly underperformed reveled that their inhibitory output is necessary to orchestrate gain-increase learning by conditionally permitting climbing fibers to instruct plasticity induction during ipsiversive head turns. Ablation of an MLI circuit for PC disinhibition prevents gain-increase learning during VOR performance errors which was rescued by re-imposing PC disinhibition through MLI activity suppression. Our findings point to a decisive role for MLIs in gating climbing-fiber-mediated learning through their context-dependent inhibition of PCs.
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8
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Pose-Méndez S, Schramm P, Valishetti K, Köster RW. Development, circuitry, and function of the zebrafish cerebellum. Cell Mol Life Sci 2023; 80:227. [PMID: 37490159 PMCID: PMC10368569 DOI: 10.1007/s00018-023-04879-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/30/2023] [Accepted: 07/17/2023] [Indexed: 07/26/2023]
Abstract
The cerebellum represents a brain compartment that first appeared in gnathostomes (jawed vertebrates). Besides the addition of cell numbers, its development, cytoarchitecture, circuitry, physiology, and function have been highly conserved throughout avian and mammalian species. While cerebellar research in avian and mammals is extensive, systematic investigations on this brain compartment in zebrafish as a teleostian model organism started only about two decades ago, but has provided considerable insight into cerebellar development, physiology, and function since then. Zebrafish are genetically tractable with nearly transparent small-sized embryos, in which cerebellar development occurs within a few days. Therefore, genetic investigations accompanied with non-invasive high-resolution in vivo time-lapse imaging represents a powerful combination for interrogating the behavior and function of cerebellar cells in their complex native environment.
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Affiliation(s)
- Sol Pose-Méndez
- Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, 38106, Braunschweig, Germany.
| | - Paul Schramm
- Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, 38106, Braunschweig, Germany
| | - Komali Valishetti
- Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, 38106, Braunschweig, Germany
| | - Reinhard W Köster
- Cellular and Molecular Neurobiology, Zoological Institute, Technische Universität Braunschweig, 38106, Braunschweig, Germany.
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9
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Ciapponi C, Li Y, Osorio Becerra DA, Rodarie D, Casellato C, Mapelli L, D’Angelo E. Variations on the theme: focus on cerebellum and emotional processing. Front Syst Neurosci 2023; 17:1185752. [PMID: 37234065 PMCID: PMC10206087 DOI: 10.3389/fnsys.2023.1185752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/18/2023] [Indexed: 05/27/2023] Open
Abstract
The cerebellum operates exploiting a complex modular organization and a unified computational algorithm adapted to different behavioral contexts. Recent observations suggest that the cerebellum is involved not just in motor but also in emotional and cognitive processing. It is therefore critical to identify the specific regional connectivity and microcircuit properties of the emotional cerebellum. Recent studies are highlighting the differential regional localization of genes, molecules, and synaptic mechanisms and microcircuit wiring. However, the impact of these regional differences is not fully understood and will require experimental investigation and computational modeling. This review focuses on the cellular and circuit underpinnings of the cerebellar role in emotion. And since emotion involves an integration of cognitive, somatomotor, and autonomic activity, we elaborate on the tradeoff between segregation and distribution of these three main functions in the cerebellum.
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Affiliation(s)
- Camilla Ciapponi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Yuhe Li
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | - Dimitri Rodarie
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Centro Ricerche Enrico Fermi, Rome, Italy
| | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Lisa Mapelli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
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10
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Nguyen TM, Thomas LA, Rhoades JL, Ricchi I, Yuan XC, Sheridan A, Hildebrand DGC, Funke J, Regehr WG, Lee WCA. Structured cerebellar connectivity supports resilient pattern separation. Nature 2023; 613:543-549. [PMID: 36418404 PMCID: PMC10324966 DOI: 10.1038/s41586-022-05471-w] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 10/20/2022] [Indexed: 11/25/2022]
Abstract
The cerebellum is thought to help detect and correct errors between intended and executed commands1,2 and is critical for social behaviours, cognition and emotion3-6. Computations for motor control must be performed quickly to correct errors in real time and should be sensitive to small differences between patterns for fine error correction while being resilient to noise7. Influential theories of cerebellar information processing have largely assumed random network connectivity, which increases the encoding capacity of the network's first layer8-13. However, maximizing encoding capacity reduces the resilience to noise7. To understand how neuronal circuits address this fundamental trade-off, we mapped the feedforward connectivity in the mouse cerebellar cortex using automated large-scale transmission electron microscopy and convolutional neural network-based image segmentation. We found that both the input and output layers of the circuit exhibit redundant and selective connectivity motifs, which contrast with prevailing models. Numerical simulations suggest that these redundant, non-random connectivity motifs increase the resilience to noise at a negligible cost to the overall encoding capacity. This work reveals how neuronal network structure can support a trade-off between encoding capacity and redundancy, unveiling principles of biological network architecture with implications for the design of artificial neural networks.
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Affiliation(s)
- Tri M Nguyen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Logan A Thomas
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Biophysics Graduate Group, University of California Berkeley, Berkeley, CA, USA
| | - Jeff L Rhoades
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Program in Neuroscience, Division of Medical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Ilaria Ricchi
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Neuro-X Institute, École Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
| | - Xintong Cindy Yuan
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Program in Neuroscience, Division of Medical Sciences, Graduate School of Arts and Sciences, Harvard University, Cambridge, MA, USA
| | - Arlo Sheridan
- HHMI Janelia Research Campus, Ashburn, VA, USA
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - David G C Hildebrand
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - Jan Funke
- HHMI Janelia Research Campus, Ashburn, VA, USA
| | - Wade G Regehr
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Wei-Chung Allen Lee
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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11
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Masoli S, Rizza MF, Tognolina M, Prestori F, D’Angelo E. Computational models of neurotransmission at cerebellar synapses unveil the impact on network computation. Front Comput Neurosci 2022; 16:1006989. [PMID: 36387305 PMCID: PMC9649760 DOI: 10.3389/fncom.2022.1006989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/10/2022] [Indexed: 11/25/2022] Open
Abstract
The neuroscientific field benefits from the conjoint evolution of experimental and computational techniques, allowing for the reconstruction and simulation of complex models of neurons and synapses. Chemical synapses are characterized by presynaptic vesicle cycling, neurotransmitter diffusion, and postsynaptic receptor activation, which eventually lead to postsynaptic currents and subsequent membrane potential changes. These mechanisms have been accurately modeled for different synapses and receptor types (AMPA, NMDA, and GABA) of the cerebellar cortical network, allowing simulation of their impact on computation. Of special relevance is short-term synaptic plasticity, which generates spatiotemporal filtering in local microcircuits and controls burst transmission and information flow through the network. Here, we present how data-driven computational models recapitulate the properties of neurotransmission at cerebellar synapses. The simulation of microcircuit models is starting to reveal how diverse synaptic mechanisms shape the spatiotemporal profiles of circuit activity and computation.
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Affiliation(s)
- Stefano Masoli
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | | | | | - Francesca Prestori
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Brain Connectivity Center, Pavia, Italy
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12
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Joyner AL, Bayin NS. Cerebellum lineage allocation, morphogenesis and repair: impact of interplay amongst cells. Development 2022; 149:dev185587. [PMID: 36172987 PMCID: PMC9641654 DOI: 10.1242/dev.185587] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
The cerebellum has a simple cytoarchitecture consisting of a folded cortex with three cell layers that surrounds a nuclear structure housing the output neurons. The excitatory neurons are generated from a unique progenitor zone, the rhombic lip, whereas the inhibitory neurons and astrocytes are generated from the ventricular zone. The growth phase of the cerebellum is driven by lineage-restricted progenitor populations derived from each zone. Research during the past decade has uncovered the importance of cell-to-cell communication between the lineages through largely unknown signaling mechanisms for regulating the scaling of cell numbers and cell plasticity during mouse development and following injury in the neonatal (P0-P14) cerebellum. This Review focuses on how the interplay between cell types is key to morphogenesis, production of robust neural circuits and replenishment of cells after injury, and ends with a discussion of the implications of the greater complexity of the human cerebellar progenitor zones for development and disease.
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Affiliation(s)
- Alexandra L. Joyner
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Biochemistry Cell and Molecular Biology Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY 10065, USA
| | - N. Sumru Bayin
- Wellcome Trust/Cancer Research UK Gurdon Institute, Cambridge University, Cambridge CB2 1NQ, UK
- Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, CB2 3DY, UK
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13
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Structure, Function, and Genetics of the Cerebellum in Autism. JOURNAL OF PSYCHIATRY AND BRAIN SCIENCE 2022; 7:e220008. [PMID: 36425354 PMCID: PMC9683352 DOI: 10.20900/jpbs.20220008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Autism spectrum disorders are common neurodevelopmental disorders that are defined by core behavioral symptoms but have diverse genetic and environmental risk factors. Despite its etiological heterogeneity, several unifying theories of autism have been proposed, including a central role for cerebellar dysfunction. The cerebellum follows a protracted course of development that culminates in an exquisitely crafted brain structure containing over half of the neurons in the entire brain densely packed into a highly organized structure. Through its complex network of connections with cortical and subcortical brain regions, the cerebellum acts as a sensorimotor regulator and affects changes in executive and limbic processing. In this review, we summarize the structural, functional, and genetic contributions of the cerebellum to autism.
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14
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Halverson HE, Kim J, Khilkevich A, Mauk MD, Augustine GJ. Feedback inhibition underlies new computational functions of cerebellar interneurons. eLife 2022; 11:77603. [PMID: 36480240 PMCID: PMC9771357 DOI: 10.7554/elife.77603] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 12/07/2022] [Indexed: 12/13/2022] Open
Abstract
The function of a feedback inhibitory circuit between cerebellar Purkinje cells and molecular layer interneurons (MLIs) was defined by combining optogenetics, neuronal activity recordings both in cerebellar slices and in vivo, and computational modeling. Purkinje cells inhibit a subset of MLIs in the inner third of the molecular layer. This inhibition is non-reciprocal, short-range (less than 200 μm) and is based on convergence of one to two Purkinje cells onto MLIs. During learning-related eyelid movements in vivo, the activity of a subset of MLIs progressively increases as Purkinje cell activity decreases, with Purkinje cells usually leading the MLIs. Computer simulations indicate that these relationships are best explained by the feedback circuit from Purkinje cells to MLIs and that this feedback circuit plays a central role in making cerebellar learning efficient.
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Affiliation(s)
- Hunter E Halverson
- Center for Learning and Memory, The University of TexasAustinUnited States
| | - Jinsook Kim
- Program in Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological UniversitySingaporeSingapore,Institute of Molecular and Cell BiologySingaporeSingapore
| | - Andrei Khilkevich
- Center for Learning and Memory, The University of TexasAustinUnited States
| | - Michael D Mauk
- Center for Learning and Memory, The University of TexasAustinUnited States,Department of Neuroscience, The University of TexasAustinUnited States
| | - George J Augustine
- Program in Neuroscience & Mental Health, Lee Kong Chian School of Medicine, Nanyang Technological UniversitySingaporeSingapore,Institute of Molecular and Cell BiologySingaporeSingapore
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