1
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. Science 2024; 384:eadi5199. [PMID: 38781369 DOI: 10.1126/science.adi5199] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 04/05/2024] [Indexed: 05/25/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multiomics datasets into a resource comprising >2.8 million nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550,000 cell type-specific regulatory elements and >1.4 million single-cell expression quantitative trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Department of Ophthalmology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Chicago, IL 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | | | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | | | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597 Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
- Department of Computer Science, Yale University, New Haven, CT 06520, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT 06520, USA
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2
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Emani PS, Liu JJ, Clarke D, Jensen M, Warrell J, Gupta C, Meng R, Lee CY, Xu S, Dursun C, Lou S, Chen Y, Chu Z, Galeev T, Hwang A, Li Y, Ni P, Zhou X, Bakken TE, Bendl J, Bicks L, Chatterjee T, Cheng L, Cheng Y, Dai Y, Duan Z, Flaherty M, Fullard JF, Gancz M, Garrido-Martín D, Gaynor-Gillett S, Grundman J, Hawken N, Henry E, Hoffman GE, Huang A, Jiang Y, Jin T, Jorstad NL, Kawaguchi R, Khullar S, Liu J, Liu J, Liu S, Ma S, Margolis M, Mazariegos S, Moore J, Moran JR, Nguyen E, Phalke N, Pjanic M, Pratt H, Quintero D, Rajagopalan AS, Riesenmy TR, Shedd N, Shi M, Spector M, Terwilliger R, Travaglini KJ, Wamsley B, Wang G, Xia Y, Xiao S, Yang AC, Zheng S, Gandal MJ, Lee D, Lein ES, Roussos P, Sestan N, Weng Z, White KP, Won H, Girgenti MJ, Zhang J, Wang D, Geschwind D, Gerstein M. Single-cell genomics and regulatory networks for 388 human brains. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.18.585576. [PMID: 38562822 PMCID: PMC10983939 DOI: 10.1101/2024.03.18.585576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the brain. Yet, little is understood about how genetic variants influence cell-level gene expression. Addressing this, we uniformly processed single-nuclei, multi-omics datasets into a resource comprising >2.8M nuclei from the prefrontal cortex across 388 individuals. For 28 cell types, we assessed population-level variation in expression and chromatin across gene families and drug targets. We identified >550K cell-type-specific regulatory elements and >1.4M single-cell expression-quantitative-trait loci, which we used to build cell-type regulatory and cell-to-cell communication networks. These networks manifest cellular changes in aging and neuropsychiatric disorders. We further constructed an integrative model accurately imputing single-cell expression and simulating perturbations; the model prioritized ~250 disease-risk genes and drug targets with associated cell types.
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Affiliation(s)
- Prashant S Emani
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jason J Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Declan Clarke
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Matthew Jensen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Chirag Gupta
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Siwei Xu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Cagatay Dursun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yuhang Chen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Zhiyuan Chu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, 92697, USA
| | - Yunyang Li
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
| | - Pengyu Ni
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Xiao Zhou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lucy Bicks
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Tanima Chatterjee
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | | | - Yuyan Cheng
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Department of Opthalmology, Perlman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Yi Dai
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | | | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Michael Gancz
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Diego Garrido-Martín
- Department of Genetics, Microbiology and Statistics, Universitat de Barcelona, Barcelona, 08028, Spain
| | - Sophia Gaynor-Gillett
- Tempus Labs, Inc., Chicago, IL, 60654, USA
- Department of Biology, Cornell College, Mount Vernon, IA, 52314, USA
| | - Jennifer Grundman
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Natalie Hawken
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Ella Henry
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Ao Huang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Ting Jin
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Riki Kawaguchi
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
| | - Saniya Khullar
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Jianyin Liu
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Junhao Liu
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Shuang Liu
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Shaojie Ma
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Michael Margolis
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Samantha Mazariegos
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jill Moore
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | | | - Eric Nguyen
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Nishigandha Phalke
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Milos Pjanic
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Henry Pratt
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Diana Quintero
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | | | - Tiernon R Riesenmy
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
| | - Nicole Shedd
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Manman Shi
- Tempus Labs, Inc., Chicago, IL, 60654, USA
| | | | - Rosemarie Terwilliger
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
| | | | - Brie Wamsley
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Gaoyuan Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Yan Xia
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Shaohua Xiao
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Andrew C Yang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Suchen Zheng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
| | - Michael J Gandal
- Interdepartmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ed S Lein
- Allen Institute for Brain Science, Seattle, WA, 98109, USA
- Department of Neurological Surgery, University of Washington, Seattle, WA, 98195, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, 98195, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Mental Illness Research Education and Clinical Center, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
- Center for Precision Medicine and Translational Therapeutics, James J. Peters VA Medical Center, Bronx, NY, 10468, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University, New Haven, CT, 06510, USA
| | - Zhiping Weng
- Department of Genomics and Computational Biology, UMass Chan Medical School, Worcester, MA, 01605, USA
| | - Kevin P White
- Yong Loo Lin School of Medicine, National University of Singapore, 117597, Singapore
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Matthew J Girgenti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT, 06520, USA
- Clinical Neuroscience Division, National Center for Posttraumatic Stress Disorder, Veterans Affairs Connecticut Healthcare System, West Haven, CT, 06516, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, 92697, USA
| | - Daifeng Wang
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Daniel Geschwind
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
- Center for Autism Research and Treatment, Semel Institute, University of California, Los Angeles, CA, 90095, USA
- Department of Psychiatry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Precision Health, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, 06520, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06520, USA
- Department of Computer Science, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Department of Biomedical Informatics & Data Science, Yale University, New Haven, CT, 06520, USA
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3
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Gomez CR. Role of heat shock proteins in aging and chronic inflammatory diseases. GeroScience 2021; 43:2515-2532. [PMID: 34241808 PMCID: PMC8599533 DOI: 10.1007/s11357-021-00394-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/24/2021] [Indexed: 01/01/2023] Open
Abstract
Advanced age is associated with a decline in response to stress. This contributes to the establishment of chronic inflammation, one of the hallmarks of aging and age-related disease. Heat shock proteins (HSP) are determinants of life span, and their progressive malfunction leads to age-related pathology. To discuss the function of HSP on age-related chronic inflammation and illness. An updated review of literature and discussion of relevant work on the topic of HSP in normal aging and chronic inflammatory pathology was performed. HSP contribute to inflamm-aging. They also play a key role in age-associated pathology linked to chronic inflammation such as autoimmune disorders, neurological disease, cardiovascular disorder, and cancer. HSP may be targeted for control of their effects related to age and chronic inflammation. Research on HSP functions in age-linked chronic inflammatory disorders provides an opportunity to improve health span and delay age-related chronic disorders.
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Affiliation(s)
- Christian R Gomez
- Department of Pathology, University of Mississippi Medical Cent, er, 2500 N. State St, Jackson, MS, 39216, USA.
- Department of Radiation Oncology, University of Mississippi Medical Center, 2500 N. State St, Jackson, MS, 39216, USA.
- Preclinical Research Unit, Center for Clinical and Translational Science, University of Mississippi, 2500 N. State St, Jackson, MS, 39216, USA.
- Cancer Center and Research Institute, University of Mississippi Medical Center, 2500 N. State St, Jackson, MS, 39216, USA.
- Division of Lung Diseases, National Institutes of Health (NIH), National Heart, Lung and Blood Institute (NHLBI), Bethesda, MD, USA.
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Vicente Miranda H, Chegão A, Oliveira MS, Fernandes Gomes B, Enguita FJ, Outeiro TF. Hsp27 reduces glycation-induced toxicity and aggregation of alpha-synuclein. FASEB J 2020; 34:6718-6728. [PMID: 32259355 DOI: 10.1096/fj.201902936r] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 03/02/2020] [Accepted: 03/12/2020] [Indexed: 01/20/2023]
Abstract
α-synuclein (aSyn) is a major player in Parkinson's disease and a group of other disorders collectively known as synucleinopathies, but the precise molecular mechanisms involved are still unclear. aSyn, as virtually all proteins, undergoes a series of posttranslational modifications during its lifetime, which can affect its biology and pathobiology. We recently showed that glycation of aSyn by methylglyoxal (MGO) potentiates its oligomerization and toxicity, induces dopaminergic neuronal cell loss in mice, and affects motor performance in flies. Small heat-shock proteins (sHsps) are molecular chaperones that facilitate the folding of proteins or target misfolded proteins for clearance. Importantly, sHsps were shown to prevent aSyn aggregation and cytotoxicity. Upon treating cells with increasing amounts of methylglyoxal, we found that the levels of Hsp27 decreased in a dose-dependent manner. Therefore, we hypothesized that restoring the levels of Hsp27 in glycating environments could alleviate the pathogenicity of aSyn. Consistently, we found that Hsp27 reduced MGO-induced aSyn aggregation in cells, leading to the formation of nontoxic aSyn species. Remarkably, increasing the levels of Hsp27 suppressed the deleterious effects induced by MGO. Our findings suggest that in glycating environments, the levels of Hsp27 are important for modulating the glycation-associated cellular pathologies in synucleinopathies.
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Affiliation(s)
- Hugo Vicente Miranda
- CEDOC, Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Ana Chegão
- CEDOC, Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Márcia S Oliveira
- CEDOC, Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Bárbara Fernandes Gomes
- CEDOC, Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Francisco J Enguita
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisboa, Portugal
| | - Tiago Fleming Outeiro
- CEDOC, Chronic Diseases Research Center, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal.,Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center Göttingen, Göttingen, Germany.,Max Planck Institute for Experimental Medicine, Göttingen, Germany.,Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, UK
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5
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Ramsey J, Martin EC, Purcell OM, Lee KM, MacLean AG. Self-injurious behaviours in rhesus macaques: Potential glial mechanisms. JOURNAL OF INTELLECTUAL DISABILITY RESEARCH : JIDR 2018; 62:1008-1017. [PMID: 30450801 PMCID: PMC6385863 DOI: 10.1111/jir.12558] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Revised: 06/20/2018] [Accepted: 09/28/2018] [Indexed: 06/09/2023]
Abstract
BACKGROUND Self-injurious behaviour (SIB) can be classified as intentional, direct injuring of body tissue usually without suicidal intent. In its non-suicidal form it is commonly seen as a clinical sign of borderline personality disorder, autism, PTSD, depression, and anxiety affecting a wide range of ages and conditions. In rhesus macaques SIB is most commonly manifested through hair plucking, self-biting, self-hitting, and head banging. SIB in the form of self-biting is observed in approximately 5-15% of individually housed monkeys. Recently, glial cells are becoming recognised as key players in regulating behaviours. METHOD The goal of this study was to determine the role of glial activation, including astrocytes, in macaques that had displayed SIB. To this end, we performed immunohistochemistry and next generation sequence of brain tissues from rhesus macaques with SIB. RESULTS Our studies showed increased vimentin, but not nestin, expression on astrocytes of macaques displaying SIB. Initial RNA Seq analyses indicate activation of pathways involved in tissue remodelling, neuroinflammation and cAMP signalling. CONCLUSIONS Glia are most probably activated in primates with self-injury, and are therefore potential novel targets for therapeutics.
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Affiliation(s)
- Joseph Ramsey
- Tulane Program in Neuroscience, Tulane University, New Orleans, LA 70112
| | - Elizabeth C. Martin
- Center for Stem Cell Research and Regenerative Medicine, School of Medicine, Tulane University, New Orleans, LA 70112
| | - Olivia M. Purcell
- Tulane Program in Neuroscience, Tulane University, New Orleans, LA 70112
| | - Kim M. Lee
- Tulane National Primate Research Center, Covington, LA 70433
- Tulane Program in Biomedical Science, Tulane Medical School, New Orleans, LA 70112
| | - Andrew G. MacLean
- Tulane Program in Neuroscience, Tulane University, New Orleans, LA 70112
- Tulane National Primate Research Center, Covington, LA 70433
- Tulane Program in Biomedical Science, Tulane Medical School, New Orleans, LA 70112
- Department of Microbiology & Immunology, Tulane Medical School, New Orleans, LA 70112
- Tulane Center for Aging, Tulane University New Orleans, LA 70112
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6
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Didier ES, MacLean AG, Mohan M, Didier PJ, Lackner AA, Kuroda MJ. Contributions of Nonhuman Primates to Research on Aging. Vet Pathol 2016; 53:277-90. [PMID: 26869153 DOI: 10.1177/0300985815622974] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Aging is the biological process of declining physiologic function associated with increasing mortality rate during advancing age. Humans and higher nonhuman primates exhibit unusually longer average life spans as compared with mammals of similar body mass. Furthermore, the population of humans worldwide is growing older as a result of improvements in public health, social services, and health care systems. Comparative studies among a wide range of organisms that include nonhuman primates contribute greatly to our understanding about the basic mechanisms of aging. Based on their genetic and physiologic relatedness to humans, nonhuman primates are especially important for better understanding processes of aging unique to primates, as well as for testing intervention strategies to improve healthy aging and to treat diseases and disabilities in older people. Rhesus and cynomolgus macaques are the predominant monkeys used in studies on aging, but research with lower nonhuman primate species is increasing. One of the priority topics of research about aging in nonhuman primates involves neurologic changes associated with cognitive decline and neurodegenerative diseases. Additional areas of research include osteoporosis, reproductive decline, caloric restriction, and their mimetics, as well as immune senescence and chronic inflammation that affect vaccine efficacy and resistance to infections and cancer. The purpose of this review is to highlight the findings from nonhuman primate research that contribute to our understanding about aging and health span in humans.
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Affiliation(s)
- E S Didier
- Division of Microbiology, Tulane National Primate Research Center, Covington, LA, USA
| | - A G MacLean
- Division of Comparative Pathology, Tulane National Primate Research Center, Covington, LA, USA
| | - M Mohan
- Division of Comparative Pathology, Tulane National Primate Research Center, Covington, LA, USA
| | - P J Didier
- Division of Comparative Pathology, Tulane National Primate Research Center, Covington, LA, USA
| | - A A Lackner
- Division of Comparative Pathology, Tulane National Primate Research Center, Covington, LA, USA
| | - M J Kuroda
- Division of Immunology, Tulane National Primate Research Center, Covington, LA, USA
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7
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Posimo JM, Weilnau JN, Gleixner AM, Broeren MT, Weiland NL, Brodsky JL, Wipf P, Leak RK. Heat shock protein defenses in the neocortex and allocortex of the telencephalon. Neurobiol Aging 2015; 36:1924-37. [PMID: 25771395 DOI: 10.1016/j.neurobiolaging.2015.02.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 01/30/2015] [Accepted: 02/11/2015] [Indexed: 11/18/2022]
Abstract
The telencephalic allocortex develops protein inclusions before the neocortex in many age-related proteinopathies. One major defense mechanism against proteinopathic stress is the heat shock protein (Hsp) network. We therefore contrasted Hsp defenses in stressed primary neocortical and allocortical cells. Neocortical neurons were more resistant to the proteasome inhibitor MG132 than neurons from 3 allocortical subregions: entorhinal cortex, piriform cortex, and hippocampus. However, allocortical neurons exhibited higher MG132-induced increases in Hsp70 and heat shock cognate 70 (Hsc70). MG132-treated allocortical neurons also exhibited greater levels of protein ubiquitination. Inhibition of Hsp70/Hsc70 activity synergistically exacerbated MG132 toxicity in allocortical neurons more than neocortical neurons, suggesting that the allocortex is more reliant on these Hsp defenses. In contrast, astrocytes harvested from the neocortex or allocortex did not differ in their response to Hsp70/Hsc70 inhibition. Consistent with the idea that chaperones are maximally engaged in allocortical neurons, an increase in Hsp70/Hsc70 activity was protective only in neocortical neurons. Finally, the levels of select Hsps were altered in the neocortex and allocortex in vivo with aging.
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Affiliation(s)
- Jessica M Posimo
- Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA
| | - Justin N Weilnau
- Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA
| | - Amanda M Gleixner
- Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA
| | - Matthew T Broeren
- Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA
| | - Nicole L Weiland
- Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA
| | - Jeffrey L Brodsky
- Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter Wipf
- Department of Chemistry, University of Pittsburgh, Pittsburgh, PA, USA; Department of Pharmaceutical Sciences, University of Pittsburgh, Pittsburgh, PA, USA
| | - Rehana K Leak
- Division of Pharmaceutical Sciences, Duquesne University, Pittsburgh, PA, USA.
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8
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Leak RK. Heat shock proteins in neurodegenerative disorders and aging. J Cell Commun Signal 2014; 8:293-310. [PMID: 25208934 DOI: 10.1007/s12079-014-0243-9] [Citation(s) in RCA: 113] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 09/01/2014] [Indexed: 12/20/2022] Open
Abstract
Many members of the heat shock protein family act in unison to refold or degrade misfolded proteins. Some heat shock proteins also directly interfere with apoptosis. These homeostatic functions are especially important in proteinopathic neurodegenerative diseases, in which specific proteins misfold, aggregate, and kill cells through proteotoxic stress. Heat shock protein levels may be increased or decreased in these disorders, with the direction of the response depending on the individual heat shock protein, the disease, cell type, and brain region. Aging is also associated with an accrual of proteotoxic stress and modulates expression of several heat shock proteins. We speculate that the increase in some heat shock proteins in neurodegenerative conditions may be partly responsible for the slow progression of these disorders, whereas the increase in some heat shock proteins with aging may help delay senescence. The protective nature of many heat shock proteins in experimental models of neurodegeneration supports these hypotheses. Furthermore, some heat shock proteins appear to be expressed at higher levels in longer-lived species. However, increases in heat shock proteins may be insufficient to override overwhelming proteotoxic stress or reverse the course of these conditions, because the expression of several other heat shock proteins and endogenous defense systems is lowered. In this review we describe a number of stress-induced changes in heat shock proteins as a function of age and neurodegenerative pathology, with an emphasis on the heat shock protein 70 (Hsp70) family and the two most common proteinopathic disorders of the brain, Alzheimer's and Parkinson's disease.
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Affiliation(s)
- Rehana K Leak
- Division of Pharmaceutical Sciences, Duquesne University, 600 Forbes Ave, Pittsburgh, PA, 15282, USA,
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9
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Schumpert C, Handy I, Dudycha JL, Patel RC. Relationship between heat shock protein 70 expression and life span in Daphnia. Mech Ageing Dev 2014; 139:1-10. [PMID: 24814302 DOI: 10.1016/j.mad.2014.04.001] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2013] [Revised: 04/23/2014] [Accepted: 04/29/2014] [Indexed: 10/25/2022]
Abstract
The longevity of an organism is directly related to its ability to effectively cope with cellular stress. Heat shock response (HSR) protects the cells against accumulation of damaged proteins after exposure to elevated temperatures and also in aging cells. To understand the role of Hsp70 in regulating life span of Daphnia, we examined the expression of Hsp70 in two ecotypes that exhibit strikingly different life spans. Daphnia pulicaria, the long lived ecotype, showed a robust Hsp70 induction as compared to the shorter lived Daphnia pulex. Interestingly, the short-lived D. pulex isolates showed no induction of Hsp70 at the mid point in their life span. In contrast to this, the long-lived D. pulicaria continued to induce Hsp70 expression at an equivalent age. We further show that the Hsp70 expression was induced at transcriptional level in response to heat shock. The transcription factor responsible for Hsp70 induction, heat shock factor-1 (HSF-1), although present in aged organisms did not exhibit DNA-binding capability. Thus, the decline of Hsp70 induction in old organisms could be attributed to a decline in HSF-1's DNA-binding activity. These results for the first time, present a molecular analysis of the relationship between HSR and life span in Daphnia.
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Affiliation(s)
- Charles Schumpert
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, United States
| | - Indhira Handy
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, United States
| | - Jeffry L Dudycha
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, United States
| | - Rekha C Patel
- Department of Biological Sciences, University of South Carolina, Columbia, SC 29208, United States.
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10
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Gleixner AM, Pulugulla SH, Pant DB, Posimo JM, Crum TS, Leak RK. Impact of aging on heat shock protein expression in the substantia nigra and striatum of the female rat. Cell Tissue Res 2014; 357:43-54. [PMID: 24723229 DOI: 10.1007/s00441-014-1852-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 02/17/2014] [Indexed: 12/17/2022]
Abstract
Many heat shock proteins are chaperones that help refold or degrade misfolded proteins and battle apoptosis. Because of their capacity to protect against protein misfolding, they may help keep diseases of aging at bay. A few reports have examined heat shock proteins (eg. Hsp25, Hsp60, Hsp70, and heat shock cognate 70 or Hsc70) as a function of age in the striatum and nigra. In the present study, we examined the impact of aging on Hsp25, heme oxygenase 1 (HO1 or Hsp32), Hsp40, Hsp60, Hsc70, Hsc/Hsp70 interacting protein (Hip), 78 kDa glucose-regulated protein (GRP78), Hsp90, and ubiquitinated proteins in the nigra and striatum of the female rat by infrared immunoblotting. Female animals are not typically examined in aging studies, adding further to the novelty of our study. Striatal HO1 and Hsp40 were both higher in middle-aged females than in the oldest group. Hsp60 levels were also highest in middle age in the nigra, but were highest in the oldest animals in the striatum. Striatal levels of Hsc70 and the co-chaperone Hip were lower in the oldest group relative to the youngest animals. In contrast, Hsp25 rose with advancing age in both regions. Hsp25 was also colocalized with tyrosine hydroxylase in nigral neurons. Ubiquitinated proteins exhibited a trend to rise in the oldest animals in both regions, and K48 linkage-specific ubiquitin rose significantly from 4-6 to 16-19 months in the striatum. Our study reveals a complex array of age-related changes in heat shock proteins. Furthermore, the age-related rises in some proteins, such as Hsp25, may reflect endogenous adaptations to cellular stress.
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Affiliation(s)
- A M Gleixner
- Graduate School of Pharmaceutical Sciences, Mylan School of Pharmacy, Duquesne University, 407 Mellon Hall, 600 Forbes Ave, Pittsburgh, PA, 15282, USA
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11
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Abstract
Although severe stress can elicit toxicity, mild stress often elicits adaptations. Here we review the literature on stress-induced adaptations versus stress sensitization in models of neurodegenerative diseases. We also describe our recent findings that chronic proteotoxic stress can elicit adaptations if the dose is low but that high-dose proteotoxic stress sensitizes cells to subsequent challenges. In these experiments, long-term, low-dose proteasome inhibition elicited protection in a superoxide dismutase-dependent manner. In contrast, acute, high-dose proteotoxic stress sensitized cells to subsequent proteotoxic challenges by eliciting catastrophic loss of glutathione. However, even in the latter model of synergistic toxicity, several defensive proteins were upregulated by severe proteotoxicity. This led us to wonder whether high-dose proteotoxic stress can elicit protection against subsequent challenges in astrocytes, a cell type well known for their resilience. In support of this new hypothesis, we found that the astrocytes that survived severe proteotoxicity became harder to kill. The adaptive mechanism was glutathione dependent. If these findings can be generalized to the human brain, similar endogenous adaptations may help explain why neurodegenerative diseases are so delayed in appearance and so slow to progress. In contrast, sensitization to severe stress may explain why defenses eventually collapse in vulnerable neurons.
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Affiliation(s)
- Rehana K Leak
- Division of Pharmaceutical Sciences, Mylan School of Pharmacy, Duquesne University
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12
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Aridon P, Geraci F, Turturici G, D’Amelio M, Savettieri G, Sconzo G. Protective Role of Heat Shock Proteins in Parkinson’s Disease. NEURODEGENER DIS 2011; 8:155-68. [DOI: 10.1159/000321548] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2010] [Accepted: 09/16/2010] [Indexed: 01/04/2023] Open
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13
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Wengenack TM, Reyes DA, Curran GL, Borowski BJ, Lin J, Preboske GM, Holasek SS, Gilles EJ, Chamberlain R, Marjanska M, Jack CR, Garwood M, Poduslo JF. Regional differences in MRI detection of amyloid plaques in AD transgenic mouse brain. Neuroimage 2010; 54:113-22. [PMID: 20728546 DOI: 10.1016/j.neuroimage.2010.08.033] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2010] [Revised: 07/09/2010] [Accepted: 08/16/2010] [Indexed: 11/19/2022] Open
Abstract
Our laboratory and others have reported the ability to detect individual Alzheimer's disease (AD) amyloid plaques in transgenic mouse brain in vivo by magnetic resonance imaging (MRI). Since amyloid plaques contain iron, most MRI studies attempting to detect plaques in AD transgenic mouse brain have employed techniques that exploit the paramagnetic effect of iron and have had mixed results. In the present study, using five-way anatomic spatial coregistration of MR images with three different histological techniques, properties of amyloid plaques in AD transgenic mouse brain were revealed that may explain their variable visibility in gradient- and spin-echo MR images. The results demonstrate differences in the visibility of plaques in the cortex and hippocampus, compared to plaques in the thalamus, by the different MRI sequences. All plaques were equally detectable by T(2)SE, while only thalamic plaques were reliably detectable by T(2)*GE pulse sequences. Histology revealed that cortical/hippocampal plaques have low levels of iron while thalamic plaques have very high levels. However, the paramagnetic effect of iron does not appear to be the sole factor leading to the rapid decay of transverse magnetization (short T(2)) in cortical/hippocampal plaques. Accordingly, MRI methods that rely less on iron magnetic susceptibility effect may be more successful for eventual human AD plaque MR imaging, particularly since human AD plaques more closely resemble the cortical and hippocampal plaques of AD transgenic mice than thalamic plaques.
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Affiliation(s)
- T M Wengenack
- Department of Neurology, Mayo Clinic College of Medicine, Rochester, MN 55905, USA
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14
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Yang J, Tower J. Expression of hsp22 and hsp70 transgenes is partially predictive of drosophila survival under normal and stress conditions. J Gerontol A Biol Sci Med Sci 2009; 64:828-38. [PMID: 19420297 DOI: 10.1093/gerona/glp054] [Citation(s) in RCA: 57] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Drosophila Hsp70 is a highly conserved molecular chaperone with numerous cytoplasmic targets. Hsp22 is an alpha-crystallin-related chaperone (small hsp) that localizes to the mitochondrial matrix. The hsp70 and hsp22 genes are induced in response to acute heat and oxidative stress and are also upregulated during normal aging. Here the hsp22 promoter (-314 to +10) and the hsp70 promoter (-194 to +10) were used to drive expression of the fluorescent reporter proteins green fluorescent protein (GFP) and Discosoma sp. red fluorescent protein (DsRED) in transgenic flies. Multiple transgenic lines were analyzed under normal culture conditions and under oxidative stress and heat stress conditions that significantly shorten life span. Flies were individually housed, and GFP (or DsRED) was quantified at young-age time points using the fluorescence stereomicroscope and image analysis software. Expression of the hsp reporters in young flies was partially predictive of remaining life span: Young flies with high expression tended to die sooner under both control and stress conditions.
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Affiliation(s)
- Junsheng Yang
- Molecular and Computational Biology Program, Department of Biological Sciences, University of Southern California, 1050 Childs Way, RRI 201, Los Angeles, CA 90089-2910, USA
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15
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Márquez M, Serafin A, Fernández-Bellon H, Serrat S, Ferrer-Admetlla A, Bertranpetit J, Ferrer I, Pumarola M. Neuropathologic Findings in an Aged Albino Gorilla. Vet Pathol 2008; 45:531-7. [DOI: 10.1354/vp.45-4-531] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Pallido-nigral spheroids associated with iron deposition have been observed in some aged clinically normal nonhuman primates. In humans, similar findings are observed in neurodegeneration with brain iron accumulation diseases, which, in some cases, show associated mutations in pantothenate kinase 2 gene (PANK2). Here we present an aged gorilla, 40 years old, suffering during the last 2 years of life from progressive tetraparesis, nystagmus, and dyskinesia of the arms, hands, and neck, with accompanying abnormal behavior. The postmortem neuropathologic examination revealed, in addition to aging-associated changes in the brain, numerous corpora amylacea in some brain areas, especially the substantia nigra, and large numbers of axonal spheroids associated with iron accumulation in the internal globus pallidus. Sequencing of the gorilla PANK2 gene failed to detect any mutation. The clinical, neuropathologic, and genetic findings in this gorilla point to an age-related pallido-nigral degeneration that presented PKAN-like neurologic deficits.
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Affiliation(s)
- M. Márquez
- Animal Tissue Bank of Catalunya, Departament de Medicina i Cirurgia Animals, Facultat de Veterinària, Universitat Autonòma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain
| | - A. Serafin
- Animal Tissue Bank of Catalunya, Departament de Medicina i Cirurgia Animals, Facultat de Veterinària, Universitat Autonòma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain
| | | | - S. Serrat
- Parc Zoològic de Barcelona (HF-B, SS), Barcelona, Spain
| | - A. Ferrer-Admetlla
- Unitat de Biologia Evolutiva, Universitat Pompeu Fabra, Barcelona, Spain
| | - J. Bertranpetit
- Unitat de Biologia Evolutiva, Universitat Pompeu Fabra, Barcelona, Spain
| | - I. Ferrer
- Institut Neuropatologia, Servei Anatomia Patològica, IDIBELL-Hospital Universitari de Bellvitge, carrer Feixa Llarga s/n, Hospitalet de Llobregat, Spain
| | - M. Pumarola
- Animal Tissue Bank of Catalunya, Departament de Medicina i Cirurgia Animals, Facultat de Veterinària, Universitat Autonòma de Barcelona, Bellaterra (Cerdanyola del Vallès), Barcelona, Spain
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16
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Bridge KE, Berg N, Adalbert R, Babetto E, Dias T, Spillantini MG, Ribchester RR, Coleman MP. Late onset distal axonal swelling in YFP-H transgenic mice. Neurobiol Aging 2007; 30:309-21. [PMID: 17658198 DOI: 10.1016/j.neurobiolaging.2007.06.002] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2007] [Revised: 06/03/2007] [Accepted: 06/07/2007] [Indexed: 11/19/2022]
Abstract
Axonal swellings, or spheroids, are a feature of central nervous system (CNS) axon degeneration during normal aging and in many disorders. The direct cause and mechanism are unknown. The use of transgenic mouse line YFP-H, which expresses yellow-fluorescent protein (YFP) in a subset of neurons, greatly facilitates longitudinal imaging and live imaging of axonal swellings, but it has not been established whether long-term expression of YFP itself contributes to axonal swelling. Using conventional methods to compare YFP-H mice with their YFP negative littermates, we found an age-related increase in swellings in discrete CNS regions in both genotypes, but the presence of YFP caused significantly more swellings in mice aged 8 months or over. Increased swelling was found in gracile tract, gracile nucleus and dorsal roots but not in lateral columns, olfactory bulb, motor cortex, ventral roots or peripheral nerve. Thus, long-term expression of YFP accelerates age-related axonal swelling in some axons and data reliant on the presence of YFP in these CNS regions in older animals needs to be interpreted carefully. The ability of a foreign protein to exacerbate age-related axon pathology is an important clue to the mechanisms by which such pathology can arise.
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Affiliation(s)
- Katherine E Bridge
- Laboratory of Molecular Signalling, Babraham Institute, Babraham, Cambridge CB22 3AT, UK
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17
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Fukushima K, Mizuno Y, Takatama M, Okamoto K. Increased neuronal expression of alpha B-crystallin in human olivary hypertrophy. Neuropathology 2006; 26:196-200. [PMID: 16771174 DOI: 10.1111/j.1440-1789.2006.00682.x] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We studied morphologic changes in olivary hypertrophy from dentato-olivary tract lesions by immunohistologic methods with antialpha B-crystallin and antiheat shock protein 27 (HSP 27). The majority of central chromatolysis-like enlarged neurons, which are frequently seen in the early stages of olivary hypertrophy on ipsilateral lesions, showed a marked expression of alpha B-crystallin; however, HSP 27 did not show increased expression in those neurons. In the later stages of olivary hypertrophy, increased expressions of alpha B-crystallin varied in the remaining neurons and the expression of HSP 27 increased in hypertrophied astrocytes, although the expression of alpha B-crystallin in hypertrophic astrocytes was not prominent. The accumulation of alpha B-crystallin and HSP 27 may represent responses to pathologic conditions.
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Affiliation(s)
- Kazuko Fukushima
- Department of Neurology, Gunma University Graduate School of Medicine, Maebashi, Gunma, Japan
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18
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Poon HF, Calabrese V, Calvani M, Butterfield DA. Proteomics analyses of specific protein oxidation and protein expression in aged rat brain and its modulation by L-acetylcarnitine: insights into the mechanisms of action of this proposed therapeutic agent for CNS disorders associated with oxidative stress. Antioxid Redox Signal 2006; 8:381-94. [PMID: 16677085 DOI: 10.1089/ars.2006.8.381] [Citation(s) in RCA: 82] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Abstract
Impaired function of the central nervous system (CNS) in aged animals is associated with increased susceptibility to the development of many neurodegenerative diseases. Age-related functional deterioration in brain is consistent with the free radical theory of aging that predicts, among other things, that free radical reactions with and damage to biomolecules, such as proteins and membrane lipid bilayers, leads to loss of neurons and subsequently diminished cognition. These oxidatively modified biomolecules are believed to contribute to the decreased antioxidant content, mitochondrial dysfunction, and impaired plasticity in aged brains. Treatment of rodents with L-acetylcarnitine (LAC; gamma-trimethyl-beta-acetylbutyrobetaine) can improve these functional losses. Although it is well established that administration of LAC can decrease protein oxidation in aged brains, it is not clear which proteins are decreased in their level of oxidation in the brains of aged rats treated with LAC. The current study used a parallel redox proteomics approach to identify the proteins that are oxidized in aged rat cortex and hippocampus of aged rats. Moreover, those proteins that are reduced in oxidation status were identified in aged brains from rats treated in vivo with LAC. The findings are discussed in reference to brain aging and age-related cognitive impairment.
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Affiliation(s)
- H Fai Poon
- Department of Chemistry, University of Kentucky, Lexington, Kentucky 40506-0055, USA.
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19
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Fukuda T, Shimizu J, Furuhata H, Abe T, Shimizu K, Oishi T, Ogihara M, Kubota J, Sasaki A, Sasaki K, Azuma T, Umemura S. Overexpression of heat shock proteins in pallido-nigral axonal spheroids of nonhuman aged primates. Acta Neuropathol 2005; 110:145-50. [PMID: 15971056 DOI: 10.1007/s00401-005-1030-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2005] [Revised: 04/09/2005] [Accepted: 04/09/2005] [Indexed: 10/25/2022]
Abstract
The occurrence of spheroids has been described in the globus pallidus (GP) and substantia nigra pars reticulata (SNr) of aged rhesus monkeys. Opinions vary as to the origin of spheroids. Ultrastructural and immunohistochemical analysis suggested that spheroids originate from degenerating axons or astroglia. In the present study, we have investigated the GP and SNr of aged monkeys (Macaca fascicularis and Macaca mulatta). Although immunoreactive for microtubule-associated protein (MAP) 1A, tau, amyloid precursor protein, synaptophysin and phosphorylated neurofilament, spheroids were not immunoreactive for MAP1B and MAP2. We confirmed the axonal nature of pallido-nigral spheroids in aged rhesus monkeys. Pallido-nigral spheroids have been reported to overexpress stress proteins, such as ubiquitin, alphaB-crystallin, and heat shock protein (Hsp) 27. We further evaluated the expression of Hsps in pallido-nigral spheroids. As well as being intensely immunoreactive for ubiquitin, alphaB-crystallin, Hsp27, and Hsp70, spheroids were immunoreactive for Hsp32 (heme oxygenase-1), Hsp40, Hsp60, and Hsp90. On the basis of these findings, we speculate that Hsp32-immunoreactive spheroids might be expressed as an oxidative stress response. Induction of other Hsps might play a role in protection of axons from the aggregation of neurofilament, MAPs and other proteins, and failure to protect degenerating axons might result in their proteolysis by the ubiquitin-proteasome system.
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Affiliation(s)
- Takahiro Fukuda
- Division of Neuropathology, Department of Neuroscience, Research Center for Medical Sciences, The Jikei University School of Medicine, 3-25-8 Nishi-shimbashi, Minato-ku, 105-8461, Tokyo, Japan.
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20
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Lowe J, Hand N, Mayer RJ. Application of Ubiquitin Immunohistochemistry to the Diagnosis of Disease. Methods Enzymol 2005; 399:86-119. [PMID: 16338351 DOI: 10.1016/s0076-6879(05)99007-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2023]
Abstract
Ubiquitin immunohistochemistry has changed understanding of the pathophysiology of many diseases, particularly chronic neurodegenerative diseases. Protein aggregates (inclusions) containing ubiquitinated proteins occur in neurones and other cell types in the central nervous system in afflicted cells. The inclusions are present in all the neurological illnesses, including Alzheimer's disease, Parkinson's disease, amyotrophic lateral sclerosis, polyglutamine diseases, and rarer forms of neurodegenerative disease. A new cause of cognitive decline in the elderly, "dementia with Lewy bodies," accounting for some 15-30% of cases, was initially discovered and characterized by ubiquitin immunocytochemistry. The optimal methods for carrying out immunohistochemical analyses of paraffin-embedded tissues are described, and examples of all the types of intracellular inclusions detected by ubiquitin immunohistochemistry in the diseases are illustrated. The role of the ubiquitin proteasome system (UPS) in disease progression is being actively researched globally and increasingly, because it is now realized that the UPS controls most pathways in cellular homeostasis. Many of these regulatory mechanisms will be dysfunctional in diseased cells. The goal is to understand fully the role of the UPS in the disorders and then therapeutically intervene in the ubiquitin pathway to treat these incurable diseases.
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Affiliation(s)
- James Lowe
- School of Molecular Medical Sciences, University of Nottingham Medical School, Queens Medical Centre, Nottingham, United Kingdom
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21
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den Engelsman J, Bennink EJ, Doerwald L, Onnekink C, Wunderink L, Andley UP, Kato K, de Jong WW, Boelens WC. Mimicking phosphorylation of the small heat-shock protein alphaB-crystallin recruits the F-box protein FBX4 to nuclear SC35 speckles. ACTA ACUST UNITED AC 2004; 271:4195-203. [PMID: 15511225 DOI: 10.1111/j.1432-1033.2004.04359.x] [Citation(s) in RCA: 57] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Abstract
The mammalian small heat shock protein alphaB-crystallin can be phosphorylated at three different sites, Ser19, Ser45 and Ser59. We compared the intracellular distribution of wild-type, nonphosphorylatable and all possible pseudophosphorylation mutants of alphaB-crystallin by immunoblot and immunocytochemical analyses of stable and transiently transfected cells. We observed that pseudophosphorylation at two (especially S19D/S45D) or all three (S19D/S45D/S59D) sites induced the partial translocation of alphaB-crystallin from the detergent-soluble to the detergent-insoluble fraction. Double immunofluorescence studies showed that the pseudophosphorylation mutants localized in nuclear speckles containing the splicing factor SC35. The alphaB-crystallin mutants in these speckles were resistant to mild detergent treatment, and also to DNase I or RNase A digestion, indicating a stable interaction with one or more speckle proteins, not dependent on intact DNA or RNA. We further found that FBX4, an adaptor protein of the ubiquitin-protein isopeptide ligase SKP1/CUL1/F-box known to interact with pseudophosphorylated alphaB-crystallin, was also recruited to SC35 speckles when cotransfected with the pseudophosphorylation mutants. Because SC35 speckles also react with an antibody against alphaB-crystallin endogenously phosphorylated at Ser45, our findings suggest that alphaB-crystallin has a phosphorylation-dependent role in the ubiquitination of a component of SC35 speckles.
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Affiliation(s)
- John den Engelsman
- Department of Biochemistry 161, Nijmegen Center for Molecular Life Sciences, University of Nijmegen, the Netherlands
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22
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Wang YL, Takeda A, Osaka H, Hara Y, Furuta A, Setsuie R, Sun YJ, Kwon J, Sato Y, Sakurai M, Noda M, Yoshikawa Y, Wada K. Accumulation of β- and γ-synucleins in the ubiquitin carboxyl-terminal hydrolase L1-deficient gad mouse. Brain Res 2004; 1019:1-9. [PMID: 15306232 DOI: 10.1016/j.brainres.2004.05.023] [Citation(s) in RCA: 31] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2004] [Indexed: 01/08/2023]
Abstract
The synuclein family includes three isoforms, termed alpha, beta and gamma. alpha-Synuclein accumulates in various pathological lesions resulting from neurodegenerative disorders including Parkinson's disease (PD), dementia with Lewy bodies (DLB) and multiple system atrophy. However, neither beta- nor gamma-synuclein has been detected in Lewy bodies, and thus it is unclear whether these isoforms contribute to neurological pathology. In the present study, we used immunohistochemistry to demonstrate accelerated accumulation of beta- and gamma-synucleins in axonal spheroids in gracile axonal dystrophy (gad) mice, which do not express ubiquitin carboxyl-terminal hydrolase L1 (UCH-L1). gamma-Synuclein immunoreactivity in the spheroids appeared in the gracile nucleus at 3 weeks of age and was maintained until 32 weeks. beta-Synuclein immunoreactivity appeared in spheroids around 12 weeks of age. In contrast, alpha-synuclein immunoreactivity was barely detectable in spheroids. Immunoreactivity for synaptophysin and ubiquitin were either faint or undetectable in spheroids. Given that UCH-L1 deficiency results in axonal degeneration and spheroid formation, our findings suggest that beta- and gamma-synuclein participate in the pathogenesis of axonal swelling in gad mice.
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Affiliation(s)
- Yu-Lai Wang
- Department of Degenerative Neurological Diseases, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8502, Japan
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23
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Lachance PED, Chaudhuri A. Microarray analysis of developmental plasticity in monkey primary visual cortex. J Neurochem 2004; 88:1455-69. [PMID: 15009647 DOI: 10.1046/j.1471-4159.2003.02274.x] [Citation(s) in RCA: 36] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
We performed microarray gene expression analyses on the visual cortex of Old-World monkeys (Cercopithicus aethiops) in an effort to identify transcripts associated with developmental maturation and activity-driven changes during the visual critical period. Samples derived from normal animals and those subjected to monocular enucleation (ME) were hybridized to human Affymetrix HG-U95Av2 oligonucleotide microarrays (N = 12) and the results were independently validated by real-time quantitative RT-PCR. To identify genes exhibiting significant expression differences among our samples, the microarray hybridization data were processed with two software packages that use different analytical models (Affymetrix MicroArray Suite 5.0, dChip 1.2). We identified 108 transcripts within diverse functional categories that differed in their visual cortical expression at the height of the critical period when compared to adults. The expression levels of four transcripts were also globally modulated following ME during the critical period. These transcripts are particularly sensitive to ME during the critical period but are not significantly modulated in ME adults. Three of the ME-driven genes (NGFI-B, egr3, NARP) are known immediate-early genes (IEG) while the other (DUSP6) is a phosphatase that can regulate IEG expression. The putative biological significance of the ME-driven and developmentally regulated genes is discussed with respect to the critical period for activity-dependent visual cortical neuroplasticity.
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24
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Winklhofer KF, Henn IH, Kay-Jackson PC, Heller U, Tatzelt J. Inactivation of parkin by oxidative stress and C-terminal truncations: a protective role of molecular chaperones. J Biol Chem 2003; 278:47199-208. [PMID: 12972428 DOI: 10.1074/jbc.m306769200] [Citation(s) in RCA: 114] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Loss of parkin function is linked to autosomal recessive juvenile parkinsonism. Here we show that proteotoxic stress and short C-terminal truncations induce misfolding of parkin. As a consequence, wild-type parkin was depleted from a high molecular weight complex and inactivated by aggregation. Similarly, the pathogenic parkin mutant W453Stop, characterized by a C-terminal deletion of 13 amino acids, spontaneously adopted a misfolded conformation. Mutational analysis indicated that C-terminal truncations exceeding 3 amino acids abolished formation of detergent-soluble parkin. In the cytosol scattered aggregates of misfolded parkin contained the molecular chaperone Hsp70. Moreover, increased expression of chaperones prevented aggregation of wild-type parkin and promoted folding of the W453Stop mutant. Analyzing parkin folding in vitro indicated that parkin is aggregation-prone and that its folding is dependent on chaperones. Our study demonstrates that C-terminal truncations impede parkin folding and reveal a new mechanism for inactivation of parkin.
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Affiliation(s)
- Konstanze F Winklhofer
- Department of Cellular Biochemistry, Max-Planck-Institute for Biochemistry, D-82152 Martinsried, Germany.
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25
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Tateyama M, Takeda A, Onodera Y, Matsuzaki M, Hasegawa T, Nunomura A, Hirai K, Perry G, Smith MA, Itoyama Y. Oxidative stress and predominant Abeta42(43) deposition in myopathies with rimmed vacuoles. Acta Neuropathol 2003; 105:581-5. [PMID: 12734664 DOI: 10.1007/s00401-003-0685-2] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2002] [Revised: 01/20/2003] [Accepted: 01/20/2003] [Indexed: 12/01/2022]
Abstract
This study was undertaken to determine the C terminus of amyloid beta protein (Abeta), accumulated in vacuolated muscle fibers, and compare these findings to the level of oxidative stress. Eight patients with myopathies characterized by rimmed vacuoles (RVs) were analyzed. Monoclonal antibodies specific to Abeta40 or Abeta42(43) revealed that the Abeta42(43) immunoreactivity was solely distributed in the vacuolated muscle fibers, and that only a part was also immunopositive for anti-Abeta40. Quantitative analyses in four specimens, in which eight or more vacuolated muscle fibers were observed, revealed that the mean incidence of Abeta42(43)-positive muscle fibers was 79.5+/-6.2% in total vacuolated muscle fibers, whereas that of the Abeta40-positive fibers was 42.9+/-12.6%. The predominance of Abeta42(43) deposition was statistically significant ( P<0.05). Abeta deposition was then compared with the distribution of oxidative nucleic acid damage in muscle fibers using a monoclonal antibody against 8-hydroxy-2'-deoxyguanosine and 8-hydroxyguanosine (8OHdG&G). The cytoplasmic staining for anti-8OHdG&G was found not only in vacuolated muscle fibers, but also in other muscle fibers including morphologically normal ones. Positive staining was completely abolished by RNase pretreatment and, thus, was suggested to reflect an increase of cellular RNA oxidation. The distribution of 8OHdG&G was much broader than the Abeta deposition. These data suggest that Abeta42(43) is predominantly involved in the pathogenesis of muscle fiber degeneration with RVs, and that oxidative damage may precede Abeta deposition in muscle fibers and play a key role in the pathomechanism of myopathies with RVs.
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Affiliation(s)
- M Tateyama
- Department of Neurology, Tohoku University School of Medicine, 1-1 Seiryomachi, Aoba-ku, 980-8574, Sendai, Japan
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26
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Drake SK, Bourdon E, Wehr NB, Levine RL, Backlund PS, Yergey AL, Rouault TA. Numerous proteins in Mammalian cells are prone to iron-dependent oxidation and proteasomal degradation. Dev Neurosci 2003; 24:114-24. [PMID: 12401949 DOI: 10.1159/000065693] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
The mechanisms that underlie iron toxicity in cells and organisms are poorly understood. Previous studies of regulation of the cytosolic iron sensor, iron-regulatory protein 2 (IRP2), indicate that iron-dependent oxidation triggers ubiquitination and proteasomal degradation of IRP2. To determine if oxidization by iron is involved in degradation of other proteins, we have used a carbonyl assay to identify oxidized proteins in lysates from RD4 cells treated with either an iron source or iron chelator. Protein lysates from iron-loaded or iron-depleted cells were resolved on two-dimensional gels and these iron manipulations were also repeated in the presence of proteasomal inhibitors. Eleven abundant proteins were identified as prone to iron-dependent oxidation and subsequent proteasomal degradation. These proteins included two putative iron-binding proteins, hNFU1 and calreticulin; two proteins involved in metabolism of hydrogen peroxide, peroxiredoxin 2 and superoxide dismutase 1; and several proteins identified in inclusions in neurodegenerative diseases, including HSP27, UCHL1, actin and tropomyosin. Our results indicate that cells can recognize and selectively eliminate iron-dependently oxidized proteins, but unlike IRP2, levels of these proteins do not significantly decrease in iron-treated cells. As iron overload is a feature of many human neurological diseases, further characterization of the process of degradation of iron-dependently oxidized proteins may yield insights into mechanisms of human disease.
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Affiliation(s)
- Steven K Drake
- National Institute of Child Health and Human Development, Bethesda, MD, USA
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27
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Söti C, Csermely P. Chaperones and aging: role in neurodegeneration and in other civilizational diseases. Neurochem Int 2002; 41:383-9. [PMID: 12213225 DOI: 10.1016/s0197-0186(02)00043-8] [Citation(s) in RCA: 77] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Chaperones are highly conserved proteins responsible for the preservation and repair of the correct conformation of cellular macromolecules, such as proteins, RNAs, etc. Environmental stress leads to chaperone (heat-shock protein, stress protein) induction reflecting the protective role of chaperones as a key factor for cell survival and in repairing cellular damage after stress. The present review summarizes our current knowledge about the chaperone-deficiency in the aging process, as well as the possible involvement of chaperones in neurodegenerative diseases, such as in Alzheimer's, Parkinson's, Huntington- and prion-related diseases. We also summarize a recent theory implying chaperones as "buffers" of variations in the human genome, which role probably increased during the last 200 years of successful medical practice minimizing natural selection. Chaperone-buffered, silent mutations may be activated during the aging process, which leads to the phenotypic exposure of previously hidden features and might contribute to the onset of polygenic diseases, such as atherosclerosis, cancer, diabetes and several neurodegenerative diseases.
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Affiliation(s)
- Csaba Söti
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 8 Budapest, Hungary
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