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Wang Z, Yang X, Li H, Wang S, Liu Z, Wang Y, Zhang X, Chen Y, Xu Q, Xu J, Wang Z, Wang J. Bidirectional two-sample Mendelian randomization analyses support causal relationships between structural and diffusion imaging-derived phenotypes and the risk of major neurodegenerative diseases. Transl Psychiatry 2024; 14:215. [PMID: 38806463 DOI: 10.1038/s41398-024-02939-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 05/10/2024] [Accepted: 05/16/2024] [Indexed: 05/30/2024] Open
Abstract
Previous observational investigations suggest that structural and diffusion imaging-derived phenotypes (IDPs) are associated with major neurodegenerative diseases; however, whether these associations are causal remains largely uncertain. Herein we conducted bidirectional two-sample Mendelian randomization analyses to infer the causal relationships between structural and diffusion IDPs and major neurodegenerative diseases using common genetic variants-single nucleotide polymorphism (SNPs) as instrumental variables. Summary statistics of genome-wide association study (GWAS) for structural and diffusion IDPs were obtained from 33,224 individuals in the UK Biobank cohort. Summary statistics of GWAS for seven major neurodegenerative diseases were obtained from the largest GWAS for each disease to date. The forward MR analyses identified significant or suggestively statistical causal effects of genetically predicted three structural IDPs on Alzheimer's disease (AD), frontotemporal dementia (FTD), and multiple sclerosis. For example, the reduction in the surface area of the left superior temporal gyrus was associated with a higher risk of AD. The reverse MR analyses identified significantly or suggestively statistical causal effects of genetically predicted AD, Lewy body dementia (LBD), and FTD on nine structural and diffusion IDPs. For example, LBD was associated with increased mean diffusivity in the right superior longitudinal fasciculus and AD was associated with decreased gray matter volume in the right ventral striatum. Our findings might contribute to shedding light on the prediction and therapeutic intervention for the major neurodegenerative diseases at the neuroimaging level.
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Affiliation(s)
- Zirui Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xuan Yang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
- Department of Radiology, Jining No.1 People's Hospital, Jining, Shandong, 272000, China
| | - Haonan Li
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Siqi Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Zhixuan Liu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yaoyi Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Xingyu Zhang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Yayuan Chen
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Qiang Xu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China
| | - Jiayuan Xu
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Zengguang Wang
- Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, 300052, China.
| | - Junping Wang
- Department of Radiology, Tianjin Key Lab of Functional Imaging & Tianjin Institute of Radiology, Tianjin Medical University General Hospital, Tianjin, 300052, China.
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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. 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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3
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Nelson PT, Fardo DW, Wu X, Aung KZ, Cykowski MD, Katsumata Y. Limbic-predominant age-related TDP-43 encephalopathy (LATE-NC): Co-pathologies and genetic risk factors provide clues about pathogenesis. J Neuropathol Exp Neurol 2024; 83:396-415. [PMID: 38613823 PMCID: PMC11110076 DOI: 10.1093/jnen/nlae032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/15/2024] Open
Abstract
Limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is detectable at autopsy in more than one-third of people beyond age 85 years and is robustly associated with dementia independent of other pathologies. Although LATE-NC has a large impact on public health, there remain uncertainties about the underlying biologic mechanisms. Here, we review the literature from human studies that may shed light on pathogenetic mechanisms. It is increasingly clear that certain combinations of pathologic changes tend to coexist in aging brains. Although "pure" LATE-NC is not rare, LATE-NC often coexists in the same brains with Alzheimer disease neuropathologic change, brain arteriolosclerosis, hippocampal sclerosis of aging, and/or age-related tau astrogliopathy (ARTAG). The patterns of pathologic comorbidities provide circumstantial evidence of mechanistic interactions ("synergies") between the pathologies, and also suggest common upstream influences. As to primary mediators of vulnerability to neuropathologic changes, genetics may play key roles. Genes associated with LATE-NC include TMEM106B, GRN, APOE, SORL1, ABCC9, and others. Although the anatomic distribution of TDP-43 pathology defines the condition, important cofactors for LATE-NC may include Tau pathology, endolysosomal pathways, and blood-brain barrier dysfunction. A review of the human phenomenology offers insights into disease-driving mechanisms, and may provide clues for diagnostic and therapeutic targets.
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Affiliation(s)
- Peter T Nelson
- Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky, USA
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
| | - David W Fardo
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky, USA
| | - Xian Wu
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky, USA
| | - Khine Zin Aung
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky, USA
| | - Matthew D Cykowski
- Department of Pathology and Genomic Medicine, Houston Methodist Hospital, Houston, Texas, USA
| | - Yuriko Katsumata
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
- Department of Biostatistics, University of Kentucky, Lexington, Kentucky, USA
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4
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Hodgson L, Li Y, Iturria-Medina Y, Stratton JA, Wolf G, Krishnaswamy S, Bennett DA, Bzdok D. Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer's disease progression. Commun Biol 2024; 7:591. [PMID: 38760483 PMCID: PMC11101463 DOI: 10.1038/s42003-024-06273-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 05/01/2024] [Indexed: 05/19/2024] Open
Abstract
Late onset Alzheimer's disease (AD) is a progressive neurodegenerative disease, with brain changes beginning years before symptoms surface. AD is characterized by neuronal loss, the classic feature of the disease that underlies brain atrophy. However, GWAS reports and recent single-nucleus RNA sequencing (snRNA-seq) efforts have highlighted that glial cells, particularly microglia, claim a central role in AD pathophysiology. Here, we tailor pattern-learning algorithms to explore distinct gene programs by integrating the entire transcriptome, yielding distributed AD-predictive modules within the brain's major cell-types. We show that these learned modules are biologically meaningful through the identification of new and relevant enriched signaling cascades. The predictive nature of our modules, especially in microglia, allows us to infer each subject's progression along a disease pseudo-trajectory, confirmed by post-mortem pathological brain tissue markers. Additionally, we quantify the interplay between pairs of cell-type modules in the AD brain, and localized known AD risk genes to enriched module gene programs. Our collective findings advocate for a transition from cell-type-specificity to gene modules specificity to unlock the potential of unique gene programs, recasting the roles of recently reported genome-wide AD risk loci.
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Affiliation(s)
- Liam Hodgson
- School of Computer Science, McGill University, Montréal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
| | - Yue Li
- School of Computer Science, McGill University, Montréal, QC, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre (BIC), MNI, Faculty of Medicine, McGill University, Montréal, Canada
- Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montréal, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, McGill University, Montréal, Canada
| | - Jo Anne Stratton
- Neurology and Neurosurgery Department, Montreal Neurological Institute (MNI), Faculty of Medicine, McGill University, Montréal, Canada
| | - Guy Wolf
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada
- Department of Mathematics & Statistics, Université de Montréal, Montréal, Canada
| | - Smita Krishnaswamy
- Department of Computer Science, Department of Genetics, Yale University, New Haven, CT, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Danilo Bzdok
- Mila - Quebec Artificial Intelligence Institute, Montréal, QC, Canada.
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montréal, QC, Canada.
- The Neuro - Montréal Neurological Institute, McConnell Brain Imaging Centre, Faculty of Medicine, McGill University, Montréal, QC, Canada.
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5
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Liu Y, Kwok W, Yoon H, Ryu JC, Stevens P, Hawkinson TR, Shedlock CJ, Ribas RA, Medina T, Keohane SB, Scharre D, Bruschweiler-Li L, Bruschweiler R, Gaultier A, Obrietan K, Sun RC, Yoon SO. Imbalance in Glucose Metabolism Regulates the Transition of Microglia from Homeostasis to Disease-Associated Microglia Stage 1. J Neurosci 2024; 44:e1563232024. [PMID: 38565291 PMCID: PMC11097271 DOI: 10.1523/jneurosci.1563-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
Microglia undergo two-stage activation in neurodegenerative diseases, known as disease-associated microglia (DAM). TREM2 mediates the DAM2 stage transition, but what regulates the first DAM1 stage transition is unknown. We report that glucose dyshomeostasis inhibits DAM1 activation and PKM2 plays a role. As in tumors, PKM2 was aberrantly elevated in both male and female human AD brains, but unlike in tumors, it is expressed as active tetramers, as well as among TREM2+ microglia surrounding plaques in 5XFAD male and female mice. snRNAseq analyses of microglia without Pkm2 in 5XFAD mice revealed significant increases in DAM1 markers in a distinct metabolic cluster, which is enriched in genes for glucose metabolism, DAM1, and AD risk. 5XFAD mice incidentally exhibited a significant reduction in amyloid pathology without microglial Pkm2 Surprisingly, microglia in 5XFAD without Pkm2 exhibited increases in glycolysis and spare respiratory capacity, which correlated with restoration of mitochondrial cristae alterations. In addition, in situ spatial metabolomics of plaque-bearing microglia revealed an increase in respiratory activity. These results together suggest that it is not only glycolytic but also respiratory inputs that are critical to the development of DAM signatures in 5XFAD mice.
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Affiliation(s)
- Yuxi Liu
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
| | - Witty Kwok
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
| | - Hyojung Yoon
- Department of Neuroscience, The Ohio State University, Columbus, Ohio 43210
| | - Jae Cheon Ryu
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
| | - Patrick Stevens
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210
| | - Tara R Hawkinson
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Cameron J Shedlock
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Roberto A Ribas
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Terrymar Medina
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Shannon B Keohane
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Douglas Scharre
- Department of Neurology, The Ohio State University, Columbus, Ohio 43210
| | - Lei Bruschweiler-Li
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Rafael Bruschweiler
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210
| | - Alban Gaultier
- Center for Brain Immunology and Glia, University of Virginia, Charlottesville, Virginia, 22908
| | - Karl Obrietan
- Department of Neuroscience, The Ohio State University, Columbus, Ohio 43210
| | - Ramon C Sun
- Department of Biochemistry and Molecular Biology, College of Medicine, University of Florida, Gainesville, Florida 32610
- Center for Advanced Spatial Biomolecule Research, University of Florida, Gainesville, Florida, 32610
| | - Sung Ok Yoon
- Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210
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Sampatakakis SN, Mourtzi N, Charisis S, Mamalaki E, Ntanasi E, Hatzimanolis A, Ramirez A, Lambert JC, Yannakoulia M, Kosmidis MH, Dardiotis E, Hadjigeorgiou G, Megalou M, Sakka P, Scarmeas N. Walking time and genetic predisposition for Alzheimer's disease: Results from the HELIAD study. Clin Neuropsychol 2024:1-17. [PMID: 38741352 DOI: 10.1080/13854046.2024.2344869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Accepted: 04/15/2024] [Indexed: 05/16/2024]
Abstract
Objective: Our study aimed to explore whether physical condition might affect the association between genetic predisposition for Alzheimer's Disease (AD) and AD incidence. Methods: The sample of participants consisted of 561 community-dwelling adults over 64 years old, without baseline dementia (508 cognitively normal and 53 with mild cognitive impairment), deriving from the HELIAD, an ongoing longitudinal study with follow-up evaluations every 3 years. Physical condition was assessed at baseline through walking time (WT), while a Polygenic Risk Score for late onset AD (PRS-AD) was used to estimate genetic predisposition. The association between WT and PRS-AD with AD incidence was evaluated with Cox proportional hazard models adjusted for age, sex, education years, global cognition score and APOE ε-4 genotype. Then, the association between WT and AD incidence was investigated after stratifying participants by low and high PRS-AD. Finally, we examined the association between PRS-AD and AD incidence after stratifying participants by WT. Results: Both WT and PRS-AD were connected with increased AD incidence (p < 0.05), after adjustments. In stratified analyses, in the slow WT group participants with a greater genetic risk had a 2.5-fold higher risk of developing AD compared to participants with lower genetic risk (p = 0.047). No association was observed in the fast WT group or when participants were stratified based on PRS-AD. Conclusions: Genetic predisposition for AD is more closely related to AD incidence in the group of older adults with slow WT. Hence, physical condition might be a modifier in the relationship of genetic predisposition with AD incidence.
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Affiliation(s)
- Stefanos N Sampatakakis
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Niki Mourtzi
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Sokratis Charisis
- Department of Neurology, UT Health San Antonio, San Antonio, TX, USA
| | - Eirini Mamalaki
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Eva Ntanasi
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
| | - Alex Hatzimanolis
- Department of Psychiatry, National and Kapodistrian University of Athens Medical School, Aiginition Hospital, Athens, Greece
| | - Alfredo Ramirez
- Division of Neurogenetics and Molecular Psychiatry, Department of Psychiatry and Psychotherapy, Medical Faculty, University of Cologne, Cologne, Germany
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
- German Center for Neurodegenerative Diseases (DZNE Bonn), Bonn, Germany
- Department of Psychiatry, Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, San Antonio, TX, USA
- Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Jean-Charles Lambert
- U1167-RID-AGE facteurs de risque et déterminants moléculaires des maladies liés au vieillissement, Univ. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, Lille, France
| | - Mary Yannakoulia
- Department of Nutrition and Dietetics, Harokopio University, Athens, Greece
| | - Mary H Kosmidis
- Lab of Cognitive Neuroscience, School of Psychology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Efthimios Dardiotis
- Department of Neurology, Faculty of Medicine, School of Health Sciences, University Hospital of Larissa, University of Thessaly, Larissa, Greece
| | | | | | - Paraskevi Sakka
- Athens Association of Alzheimer's Disease and Related Disorders, Marousi, Greece
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aiginition Hospital, National and Kapodistrian University of Athens Medical School, Athens, Greece
- Department of Neurology, The Gertrude H. Sergievsky Center, Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA
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7
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Parrish RL, Buchman AS, Tasaki S, Wang Y, Avey D, Xu J, De Jager PL, Bennett DA, Epstein MP, Yang J. SR-TWAS: Leveraging Multiple Reference Panels to Improve TWAS Power by Ensemble Machine Learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.06.20.23291605. [PMID: 37425698 PMCID: PMC10327185 DOI: 10.1101/2023.06.20.23291605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Multiple reference panels of a given tissue or multiple tissues often exist, and multiple regression methods could be used for training gene expression imputation models for TWAS. To leverage expression imputation models (i.e., base models) trained with multiple reference panels, regression methods, and tissues, we develop a Stacked Regression based TWAS (SR-TWAS) tool which can obtain optimal linear combinations of base models for a given validation transcriptomic dataset. Both simulation and real studies showed that SR-TWAS improved power, due to increased effective training sample sizes and borrowed strength across multiple regression methods and tissues. Leveraging base models across multiple reference panels, tissues, and regression methods, our real application studies identified 6 independent significant risk genes for Alzheimer's disease (AD) dementia for supplementary motor area tissue and 9 independent significant risk genes for Parkinson's disease (PD) for substantia nigra tissue. Relevant biological interpretations were found for these significant risk genes.
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8
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Lee CY, Riffle D, Xiong Y, Momtaz N, Lei Y, Pariser JM, Sikdar D, Hwang A, Duan Z, Zhang J. Characterizing dysregulations via cell-cell communications in Alzheimer's brains using single-cell transcriptomes. BMC Neurosci 2024; 25:24. [PMID: 38741048 DOI: 10.1186/s12868-024-00867-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 04/01/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is a devastating neurodegenerative disorder affecting 44 million people worldwide, leading to cognitive decline, memory loss, and significant impairment in daily functioning. The recent single-cell sequencing technology has revolutionized genetic and genomic resolution by enabling scientists to explore the diversity of gene expression patterns at the finest resolution. Most existing studies have solely focused on molecular perturbations within each cell, but cells live in microenvironments rather than in isolated entities. Here, we leveraged the large-scale and publicly available single-nucleus RNA sequencing in the human prefrontal cortex to investigate cell-to-cell communication in healthy brains and their perturbations in AD. We uniformly processed the snRNA-seq with strict QCs and labeled canonical cell types consistent with the definitions from the BRAIN Initiative Cell Census Network. From ligand and receptor gene expression, we built a high-confidence cell-to-cell communication network to investigate signaling differences between AD and healthy brains. RESULTS Specifically, we first performed broad communication pattern analyses to highlight that biologically related cell types in normal brains rely on largely overlapping signaling networks and that the AD brain exhibits the irregular inter-mixing of cell types and signaling pathways. Secondly, we performed a more focused cell-type-centric analysis and found that excitatory neurons in AD have significantly increased their communications to inhibitory neurons, while inhibitory neurons and other non-neuronal cells globally decreased theirs to all cells. Then, we delved deeper with a signaling-centric view, showing that canonical signaling pathways CSF, TGFβ, and CX3C are significantly dysregulated in their signaling to the cell type microglia/PVM and from endothelial to neuronal cells for the WNT pathway. Finally, after extracting 23 known AD risk genes, our intracellular communication analysis revealed a strong connection of extracellular ligand genes APP, APOE, and PSEN1 to intracellular AD risk genes TREM2, ABCA1, and APP in the communication from astrocytes and microglia to neurons. CONCLUSIONS In summary, with the novel advances in single-cell sequencing technologies, we show that cellular signaling is regulated in a cell-type-specific manner and that improper regulation of extracellular signaling genes is linked to intracellular risk genes, giving the mechanistic intra- and inter-cellular picture of AD.
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Affiliation(s)
- Che Yu Lee
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Dylan Riffle
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Yifeng Xiong
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Nadia Momtaz
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yutong Lei
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Joseph M Pariser
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Diptanshu Sikdar
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Ahyeon Hwang
- Department of Computer Science, University of California, Irvine, CA, USA
- Mathematical, Computational and Systems Biology, University of California, Irvine, CA, USA
| | - Ziheng Duan
- Department of Computer Science, University of California, Irvine, CA, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, CA, USA.
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9
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Guo H, Urban AE, Wong WH. Prioritizing disease-related rare variants by integrating gene expression data. RESEARCH SQUARE 2024:rs.3.rs-4355589. [PMID: 38766095 PMCID: PMC11100897 DOI: 10.21203/rs.3.rs-4355589/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. We also found strong excess of rare variants among the top prioritized genes in diseased patients compared to that in healthy individuals. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.
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10
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Mitra S, Bp K, C R S, Saikumar NV, Philip P, Narayanan M. Alzheimer's disease rewires gene coexpression networks coupling different brain regions. NPJ Syst Biol Appl 2024; 10:50. [PMID: 38724582 PMCID: PMC11082197 DOI: 10.1038/s41540-024-00376-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 04/17/2024] [Indexed: 05/12/2024] Open
Abstract
Connectome studies have shown how Alzheimer's disease (AD) disrupts functional and structural connectivity among brain regions. But the molecular basis of such disruptions is less studied, with most genomic/transcriptomic studies performing within-brain-region analyses. To inspect how AD rewires the correlation structure among genes in different brain regions, we performed an Inter-brain-region Differential Correlation (Inter-DC) analysis of RNA-seq data from Mount Sinai Brain Bank on four brain regions (frontal pole, superior temporal gyrus, parahippocampal gyrus and inferior frontal gyrus, comprising 264 AD and 372 control human post-mortem samples). An Inter-DC network was assembled from all pairs of genes across two brain regions that gained (or lost) correlation strength in the AD group relative to controls at FDR 1%. The differentially correlated (DC) genes in this network complemented known differentially expressed genes in AD, and likely reflects cell-intrinsic changes since we adjusted for cell compositional effects. Each brain region used a distinctive set of DC genes when coupling with other regions, with parahippocampal gyrus showing the most rewiring, consistent with its known vulnerability to AD. The Inter-DC network revealed master dysregulation hubs in AD (at genes ZKSCAN1, SLC5A3, RCC1, IL17RB, PLK4, etc.), inter-region gene modules enriched for known AD pathways (synaptic signaling, endocytosis, etc.), and candidate signaling molecules that could mediate region-region communication. The Inter-DC network generated in this study is a valuable resource of gene pairs, pathways and signaling molecules whose inter-brain-region functional coupling is disrupted in AD, thereby offering a new perspective of AD etiology.
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Affiliation(s)
- Sanga Mitra
- Bioinformatics and Integrative Data Science group, Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Kailash Bp
- Bioinformatics and Integrative Data Science group, Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Srivatsan C R
- Bioinformatics and Integrative Data Science group, Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Naga Venkata Saikumar
- Bioinformatics and Integrative Data Science group, Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India
| | - Philge Philip
- Centre for Integrative Biology and Systems Medicine, IIT Madras, Chennai, India
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India
| | - Manikandan Narayanan
- Bioinformatics and Integrative Data Science group, Department of Computer Science and Engineering, Indian Institute of Technology (IIT) Madras, Chennai, India.
- Centre for Integrative Biology and Systems Medicine, IIT Madras, Chennai, India.
- Robert Bosch Centre for Data Science and Artificial Intelligence, IIT Madras, Chennai, India.
- Sudha Gopalakrishnan Brain Centre, IIT Madras, Chennai, India.
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11
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Mu C, Dang X, Luo XJ. Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders. Nat Hum Behav 2024:10.1038/s41562-024-01879-8. [PMID: 38724650 DOI: 10.1038/s41562-024-01879-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 04/03/2024] [Indexed: 05/19/2024]
Abstract
Dysfunction of brain resting-state functional networks has been widely reported in psychiatric disorders. However, the causal relationships between brain resting-state functional networks and psychiatric disorders remain largely unclear. Here we perform bidirectional two-sample Mendelian randomization (MR) analyses to investigate the causalities between 191 resting-state functional magnetic resonance imaging (rsfMRI) phenotypes (n = 34,691 individuals) and 12 psychiatric disorders (n = 14,307 to 698,672 individuals). Forward MR identified 8 rsfMRI phenotypes causally associated with the risk of psychiatric disorders. For example, the increase in the connectivity of motor, subcortical-cerebellum and limbic network was associated with lower risk of autism spectrum disorder. In adddition, increased connectivity in the default mode and central executive network was associated with lower risk of post-traumatic stress disorder and depression. Reverse MR analysis revealed significant associations between 4 psychiatric disorders and 6 rsfMRI phenotypes. For instance, the risk of attention-deficit/hyperactivity disorder increases the connectivity of the attention, salience, motor and subcortical-cerebellum network. The risk of schizophrenia mainly increases the connectivity of the default mode and central executive network and decreases the connectivity of the attention network. In summary, our findings reveal causal relationships between brain functional networks and psychiatric disorders, providing important interventional and therapeutic targets for psychiatric disorders at the brain functional network level.
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Affiliation(s)
- Changgai Mu
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China
| | - Xinglun Dang
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China
| | - Xiong-Jian Luo
- Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Advanced Institute for Life and Health, Southeast University, Nanjing, China.
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12
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Genius P, Calle ML, Rodríguez-Fernández B, Minguillon C, Cacciaglia R, Garrido-Martin D, Esteller M, Navarro A, Gispert JD, Vilor-Tejedor N. Compositional structural brain signatures capture Alzheimer's genetic risk on brain structure along the disease continuum. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.08.24307046. [PMID: 38766190 PMCID: PMC11100942 DOI: 10.1101/2024.05.08.24307046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
INTRODUCTION Traditional brain imaging genetics studies have primarily focused on how genetic factors influence the volume of specific brain regions, often neglecting the overall complexity of brain architecture and its genetic underpinnings. METHODS This study analyzed data from participants across the Alzheimer's disease (AD) continuum from the ALFA and ADNI studies. We exploited compositional data analysis to examine relative brain volumetric variations that (i) differentiate cognitively unimpaired (CU) individuals, defined as amyloid-negative (A-) based on CSF profiling, from those at different AD stages, and (ii) associated with increased genetic susceptibility to AD, assessed using polygenic risk scores. RESULTS Distinct brain signatures differentiated CU A-individuals from amyloid-positive MCI and AD. Moreover, disease stage-specific signatures were associated with higher genetic risk of AD. DISCUSSION The findings underscore the complex interplay between genetics and disease stages in shaping brain structure, which could inform targeted preventive strategies and interventions in preclinical AD.
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13
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Haghayegh S, Gao C, Sugg E, Zheng X, Yang HW, Saxena R, Rutter MK, Weedon M, Ibanez A, Bennett DA, Li P, Gao L, Hu K. Association of Rest-Activity Rhythm and Risk of Developing Dementia or Mild Cognitive Impairment in the Middle-Aged and Older Population: Prospective Cohort Study. JMIR Public Health Surveill 2024; 10:e55211. [PMID: 38713911 PMCID: PMC11109857 DOI: 10.2196/55211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 02/21/2024] [Accepted: 03/16/2024] [Indexed: 05/09/2024] Open
Abstract
BACKGROUND The relationship between 24-hour rest-activity rhythms (RARs) and risk for dementia or mild cognitive impairment (MCI) remains an area of growing interest. Previous studies were often limited by small sample sizes, short follow-ups, and older participants. More studies are required to fully explore the link between disrupted RARs and dementia or MCI in middle-aged and older adults. OBJECTIVE We leveraged the UK Biobank data to examine how RAR disturbances correlate with the risk of developing dementia and MCI in middle-aged and older adults. METHODS We analyzed the data of 91,517 UK Biobank participants aged between 43 and 79 years. Wrist actigraphy recordings were used to derive nonparametric RAR metrics, including the activity level of the most active 10-hour period (M10) and its midpoint, the activity level of the least active 5-hour period (L5) and its midpoint, relative amplitude (RA) of the 24-hour cycle [RA=(M10-L5)/(M10+L5)], interdaily stability, and intradaily variability, as well as the amplitude and acrophase of 24-hour rhythms (cosinor analysis). We used Cox proportional hazards models to examine the associations between baseline RAR and subsequent incidence of dementia or MCI, adjusting for demographic characteristics, comorbidities, lifestyle factors, shiftwork status, and genetic risk for Alzheimer's disease. RESULTS During the follow-up of up to 7.5 years, 555 participants developed MCI or dementia. The dementia or MCI risk increased for those with lower M10 activity (hazard ratio [HR] 1.28, 95% CI 1.14-1.44, per 1-SD decrease), higher L5 activity (HR 1.15, 95% CI 1.10-1.21, per 1-SD increase), lower RA (HR 1.23, 95% CI 1.16-1.29, per 1-SD decrease), lower amplitude (HR 1.32, 95% CI 1.17-1.49, per 1-SD decrease), and higher intradaily variability (HR 1.14, 95% CI 1.05-1.24, per 1-SD increase) as well as advanced L5 midpoint (HR 0.92, 95% CI 0.85-0.99, per 1-SD advance). These associations were similar in people aged <70 and >70 years, and in non-shift workers, and they were independent of genetic and cardiovascular risk factors. No significant associations were observed for M10 midpoint, interdaily stability, or acrophase. CONCLUSIONS Based on findings from a large sample of middle-to-older adults with objective RAR assessment and almost 8-years of follow-up, we suggest that suppressed and fragmented daily activity rhythms precede the onset of dementia or MCI and may serve as risk biomarkers for preclinical dementia in middle-aged and older adults.
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Affiliation(s)
- Shahab Haghayegh
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute, Cambridge, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Chenlu Gao
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute, Cambridge, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Elizabeth Sugg
- Massachusetts General Hospital, Boston, MA, United States
| | - Xi Zheng
- Brigham and Women's Hospital, Boston, MA, United States
| | - Hui-Wen Yang
- Brigham and Women's Hospital, Boston, MA, United States
| | - Richa Saxena
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute, Cambridge, MA, United States
| | - Martin K Rutter
- Faculty of Medicine, Biology and Health, University of Manchester, Manchester, United Kingdom
- Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, NIHR Manchester Biomedical Research Centre, Manchester, United Kingdom
| | | | | | | | - Peng Li
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute, Cambridge, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
| | - Lei Gao
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Kun Hu
- Massachusetts General Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
- Broad Institute, Cambridge, MA, United States
- Brigham and Women's Hospital, Boston, MA, United States
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14
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Kemper KE, Sidorenko J, Wang H, Hayes BJ, Wray NR, Yengo L, Keller MC, Goddard M, Visscher PM. Genetic influence on within-person longitudinal change in anthropometric traits in the UK Biobank. Nat Commun 2024; 15:3776. [PMID: 38710707 DOI: 10.1038/s41467-024-47802-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 04/10/2024] [Indexed: 05/08/2024] Open
Abstract
The causes of temporal fluctuations in adult traits are poorly understood. Here, we investigate the genetic determinants of within-person trait variability of 8 repeatedly measured anthropometric traits in 50,117 individuals from the UK Biobank. We found that within-person (non-directional) variability had a SNP-based heritability of 2-5% for height, sitting height, body mass index (BMI) and weight (P ≤ 2.4 × 10-3). We also analysed longitudinal trait change and show a loss of both average height and weight beyond about 70 years of age. A variant tracking the Alzheimer's risk APOE- E 4 allele (rs429358) was significantly associated with weight loss ( β = -0.047 kg per yr, s.e. 0.007, P = 2.2 × 10-11), and using 2-sample Mendelian Randomisation we detected a relationship consistent with causality between decreased lumbar spine bone mineral density and height loss (bxy = 0.011, s.e. 0.003, P = 3.5 × 10-4). Finally, population-level variance quantitative trait loci (vQTL) were consistent with within-person variability for several traits, indicating an overlap between trait variability assessed at the population or individual level. Our findings help elucidate the genetic influence on trait-change within an individual and highlight disease risks associated with these changes.
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Affiliation(s)
- Kathryn E Kemper
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
| | - Julia Sidorenko
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Huanwei Wang
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Ben J Hayes
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD, Australia
| | - Naomi R Wray
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Loic Yengo
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado, Boulder, CO, USA
| | - Michael Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Parkville, VIC, Australia
- Biosciences Research Division, Agriculture Victoria, Bundoora, VIC, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, UK.
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15
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Ahmed H, Wang Y, Griffiths WJ, Levey AI, Pikuleva I, Liang SH, Haider A. Brain cholesterol and Alzheimer's disease: challenges and opportunities in probe and drug development. Brain 2024; 147:1622-1635. [PMID: 38301270 PMCID: PMC11068113 DOI: 10.1093/brain/awae028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 12/20/2023] [Accepted: 01/13/2024] [Indexed: 02/03/2024] Open
Abstract
Cholesterol homeostasis is impaired in Alzheimer's disease; however, attempts to modulate brain cholesterol biology have not translated into tangible clinical benefits for patients to date. Several recent milestone developments have substantially improved our understanding of how excess neuronal cholesterol contributes to the pathophysiology of Alzheimer's disease. Indeed, neuronal cholesterol was linked to the formation of amyloid-β and neurofibrillary tangles through molecular pathways that were recently delineated in mechanistic studies. Furthermore, remarkable advances in translational molecular imaging have now made it possible to probe cholesterol metabolism in the living human brain with PET, which is an important prerequisite for future clinical trials that target the brain cholesterol machinery in Alzheimer's disease patients-with the ultimate aim being to develop disease-modifying treatments. This work summarizes current concepts of how the biosynthesis, transport and clearance of brain cholesterol are affected in Alzheimer's disease. Further, current strategies to reverse these alterations by pharmacotherapy are critically discussed in the wake of emerging translational research tools that support the assessment of brain cholesterol biology not only in animal models but also in patients with Alzheimer's disease.
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Affiliation(s)
- Hazem Ahmed
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT 06510, USA
- Center for Radiopharmaceutical Sciences ETH-PSI-USZ, Institute of Pharmaceutical Sciences ETH, 8093 Zurich, Switzerland
| | - Yuqin Wang
- Institute of Life Science, Swansea University Medical School, Swansea SA2 8PP, UK
| | - William J Griffiths
- Institute of Life Science, Swansea University Medical School, Swansea SA2 8PP, UK
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Irina Pikuleva
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Steven H Liang
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Ahmed Haider
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland
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16
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Mannens CCA, Hu L, Lönnerberg P, Schipper M, Reagor CC, Li X, He X, Barker RA, Sundström E, Posthuma D, Linnarsson S. Chromatin accessibility during human first-trimester neurodevelopment. Nature 2024:10.1038/s41586-024-07234-1. [PMID: 38693260 DOI: 10.1038/s41586-024-07234-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/02/2024] [Indexed: 05/03/2024]
Abstract
The human brain develops through a tightly organized cascade of patterning events, induced by transcription factor expression and changes in chromatin accessibility. Although gene expression across the developing brain has been described at single-cell resolution1, similar atlases of chromatin accessibility have been primarily focused on the forebrain2-4. Here we describe chromatin accessibility and paired gene expression across the entire developing human brain during the first trimester (6-13 weeks after conception). We defined 135 clusters and used multiomic measurements to link candidate cis-regulatory elements to gene expression. The number of accessible regions increased both with age and along neuronal differentiation. Using a convolutional neural network, we identified putative functional transcription factor-binding sites in enhancers characterizing neuronal subtypes. We applied this model to cis-regulatory elements linked to ESRRB to elucidate its activation mechanism in the Purkinje cell lineage. Finally, by linking disease-associated single nucleotide polymorphisms to cis-regulatory elements, we validated putative pathogenic mechanisms in several diseases and identified midbrain-derived GABAergic neurons as being the most vulnerable to major depressive disorder-related mutations. Our findings provide a more detailed view of key gene regulatory mechanisms underlying the emergence of brain cell types during the first trimester and a comprehensive reference for future studies related to human neurodevelopment.
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Affiliation(s)
- Camiel C A Mannens
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Lijuan Hu
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Peter Lönnerberg
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden
| | - Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Caleb C Reagor
- Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, New York, NY, USA
| | - Xiaofei Li
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Xiaoling He
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Roger A Barker
- John van Geest Centre for Brain Repair, Department of Clinical Neurosciences, Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Erik Sundström
- Division of Neurodegeneration, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Solna, Sweden
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research (CNCR), Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sten Linnarsson
- Division of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Solna, Sweden.
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17
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Nicolas G. Lessons from genetic studies in Alzheimer disease. Rev Neurol (Paris) 2024; 180:368-377. [PMID: 38429159 DOI: 10.1016/j.neurol.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 12/27/2023] [Indexed: 03/03/2024]
Abstract
Research on Alzheimer disease (AD) genetics has provided critical advances to the knowledge of AD pathophysiological mechanisms. The etiology of AD can be divided into monogenic (autosomal dominant inheritance) and complex (multifactorial determinism). In monogenic AD, recent advances mainly concern mutation-associated mechanisms, presymptomatic clinical studies, and the search for modifiers of ages of onset that are still ongoing. In complex AD, genetic factors can be further categorized into three classes: (i) the APOE-ɛ4 and ɛ2 common alleles that represent a category by themselves as they are both common and with a strong impact on AD risk; (ii) common variants with a modest effect, identified in genome-wide association studies (GWAS); and (iii) rare variants with a moderate-to-strong effect, identified in case-control sequencing studies. Regarding APOE, odds ratios, available in multiple ethnicities, can now be converted into penetrance curves, although such curves remain to be performed in diverse ethnicities. In addition, advances in the understanding of mechanisms have been recently reported and rare APOE variants add to the complexity. In the GWAS category, novel loci have been discovered thanks to larger studies, doubling the number of hits as compared to the previous reference meta-analysis. However, such modest risk factors cannot be used in the clinic, neither individually, nor in genetic risk scores. In the category of rare variants, two novel genes, ABCA1 and ATP8B4 now add to the three main ones, TREM2, SORL1, and ABCA7. The study of such rare variants suggests oligogenic inheritance in some families, as also suggested by digenic penetrance curves for SORL1 loss-of-function variants with APOE-ɛ4. Cumulate frequencies of definite (so-called) rare risk factors are 2.3% to 3.6% (depending on thresholds on odds ratios) in control databases and many more remain to be classified and identified, showing how important these risk factors may be as part of the complex determinism of AD. A better understanding of these rare risk factors and their combined effects on each other, with common variants, and with environmental factors, should allow for a prediction of AD risk and, eventually, preventive medicine. Taken together, most genetic determinants of AD, in monogenic and in complex forms, point toward the aggregation of Aβ as a pivotal triggering factor, such that targeting it may be efficient as prevention in at-risk individuals. The role of neuroinflammation, microglia, and Tau pathology modulation are important sources of research for disease modification.
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Affiliation(s)
- G Nicolas
- Univ Rouen Normandie, Normandie Univ, Inserm U1245 and CHU Rouen, Department of Genetics and CNRMAJ, 76000 Rouen, France.
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18
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Eulalio T, Sun MW, Gevaert O, Greicius MD, Montine TJ, Nachun D, Montgomery SB. regionalpcs: improved discovery of DNA methylation associations with complex traits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.01.590171. [PMID: 38746367 PMCID: PMC11092597 DOI: 10.1101/2024.05.01.590171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
We have developed the regional principal components (rPCs) method, a novel approach for summarizing gene-level methylation. rPCs address the challenge of deciphering complex epigenetic mechanisms in diseases like Alzheimer's disease (AD). In contrast to traditional averaging, rPCs leverage principal components analysis to capture complex methylation patterns across gene regions. Our method demonstrated a 54% improvement in sensitivity over averaging in simulations, offering a robust framework for identifying subtle epigenetic variations. Applying rPCs to the AD brain methylation data in ROSMAP, combined with cell type deconvolution, we uncovered 838 differentially methylated genes associated with neuritic plaque burden-significantly outperforming conventional methods. Integrating methylation quantitative trait loci (meQTL) with genome-wide association studies (GWAS) identified 17 genes with potential causal roles in AD, including MS4A4A and PICALM. Our approach is available in the Bioconductor package regionalpcs, opening avenues for research and facilitating a deeper understanding of the epigenetic landscape in complex diseases.
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Affiliation(s)
- Tiffany Eulalio
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Min Woo Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Olivier Gevaert
- Department of Biomedical Data Science, Stanford University, Stanford, CA, 94305, USA
| | - Michael D Greicius
- Department of Neurology & Neurological Sciences, Stanford University, Stanford, CA, 94305, USA
| | - Thomas J Montine
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
| | - Daniel Nachun
- Department of Pathology, Stanford University, Stanford, CA, 94305, USA
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19
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Pierson SR, Kolling LJ, James TD, Pushpavathi SG, Marcinkiewcz CA. Serotonergic dysfunction may mediate the relationship between alcohol consumption and Alzheimer's disease. Pharmacol Res 2024; 203:107171. [PMID: 38599469 PMCID: PMC11088857 DOI: 10.1016/j.phrs.2024.107171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 03/14/2024] [Accepted: 04/02/2024] [Indexed: 04/12/2024]
Abstract
The impact of Alzheimer's disease (AD) and its related dementias is rapidly expanding, and its mitigation remains an urgent social and technical challenge. To date there are no effective treatments or interventions for AD, but recent studies suggest that alcohol consumption is correlated with the risk of developing dementia. In this review, we synthesize data from preclinical, clinical, and epidemiological models to evaluate the combined role of alcohol consumption and serotonergic dysfunction in AD, underscoring the need for further research on this topic. We first discuss the limitations inherent to current data-collection methods, and how neuropsychiatric symptoms common among AD, alcohol use disorder, and serotonergic dysfunction may mask their co-occurrence. We additionally describe how excess alcohol consumption may accelerate the development of AD via direct effects on serotonergic function, and we explore the roles of neuroinflammation and proteostasis in mediating the relationship between serotonin, alcohol consumption, and AD. Lastly, we argue for a shift in current research to disentangle the pathogenic effects of alcohol on early-affected brainstem structures in AD.
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Affiliation(s)
- Samantha R Pierson
- Department of Neuroscience and Pharmacology, University of Iowa, United States
| | - Louis J Kolling
- Department of Neuroscience and Pharmacology, University of Iowa, United States
| | - Thomas D James
- Department of Neuroscience and Pharmacology, University of Iowa, United States
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20
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Lee S, Hecker J, Hahn G, Mullin K, Lutz SM, Tanzi RE, Lange C, Prokopenko D. On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries. Alzheimers Dement 2024; 20:3397-3405. [PMID: 38563508 PMCID: PMC11095441 DOI: 10.1002/alz.13796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 02/14/2024] [Accepted: 02/23/2024] [Indexed: 04/04/2024]
Abstract
INTRODUCTION Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.
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Affiliation(s)
- Sanghun Lee
- Department of Medical ConsilienceDivision of MedicineGraduate schoolDankook UniversityYongin‐siGyeonggi‐doSouth Korea
- Channing Division of Network MedicineBrigham and Women's HospitalBostonMassachusettsUSA
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Julian Hecker
- Channing Division of Network MedicineBrigham and Women's HospitalBostonMassachusettsUSA
| | - Georg Hahn
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Kristina Mullin
- Genetics and Aging Unit and McCance Center for Brain HealthDepartment of NeurologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | | | - Sharon M. Lutz
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
- Department of Population MedicineHarvard Medical School and Harvard Pilgrim Healthcare InstituteBostonMassachusettsUSA
| | - Rudolph E. Tanzi
- Genetics and Aging Unit and McCance Center for Brain HealthDepartment of NeurologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Christoph Lange
- Channing Division of Network MedicineBrigham and Women's HospitalBostonMassachusettsUSA
- Department of BiostatisticsHarvard T.H. Chan School of Public HealthBostonMassachusettsUSA
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain HealthDepartment of NeurologyMassachusetts General HospitalCharlestownMassachusettsUSA
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21
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Parsi S, Zhu C, Motlagh NJ, Kim D, Küllenberg EG, Kim HH, Gillani RL, Chen JW. Basic Science of Neuroinflammation and Involvement of the Inflammatory Response in Disorders of the Nervous System. Magn Reson Imaging Clin N Am 2024; 32:375-384. [PMID: 38555147 PMCID: PMC10987041 DOI: 10.1016/j.mric.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Neuroinflammation is a key immune response observed in many neurologic diseases. Although an appropriate immune response can be beneficial, aberrant activation of this response recruits excessive proinflammatory cells to cause damage. Because the central nervous system is separated from the periphery by the blood-brain barrier (BBB) that creates an immune-privileged site, it has its own unique immune cells and immune response. Moreover, neuroinflammation can compromise the BBB causing an influx of peripheral immune cells and factors. Recent advances have brought a deeper understanding of neuroinflammation that can be leveraged to develop more potent therapies and improve patient selection.
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Affiliation(s)
- Sepideh Parsi
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Cindy Zhu
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Negin Jalali Motlagh
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daeki Kim
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Enrico G Küllenberg
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Hyung-Hwan Kim
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Rebecca L Gillani
- Department of Neurology, Neuroimmunology and Neuro-Infectious Diseases Division, Massachusetts Institute for Neurodegenerative Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - John W Chen
- Institute for Innovation in Imaging, Neurovascular Research Unit, Center for Systems Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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22
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Gomez AR, Byun HR, Wu S, Mohammad AG, Ikbariyeh J, Chen J, Muro A, Li L, Bernstein KE, Ainsworth R, Tourtellotte WG. Angiotensin Converting Enzyme (ACE) expression in microglia reduces amyloid β deposition and neurodegeneration by increasing SYK signaling and endolysosomal trafficking. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.24.590837. [PMID: 38712251 PMCID: PMC11071489 DOI: 10.1101/2024.04.24.590837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Genome-wide association studies (GWAS) have identified many gene polymorphisms associated with an increased risk of developing Late Onset Alzheimer's Disease (LOAD). Many of these LOAD risk-associated alleles alter disease pathogenesis by influencing microglia innate immune responses and lipid metabolism pathways. Angiotensin Converting Enzyme (ACE), a GWAS LOAD risk-associated gene best known for its role in regulating systemic blood pressure, also enhances innate immunity and lipid processing in peripheral myeloid cells, but a role for ACE in modulating the function of myeloid-derived microglia remains unexplored. Using novel mice engineered to express ACE in microglia and CNS associated macrophages (CAMs), we find that ACE expression in microglia reduces Aβ plaque load, preserves vulnerable neurons and excitatory synapses, and greatly reduces learning and memory abnormalities in the 5xFAD amyloid mouse model of Alzheimer's Disease (AD). ACE-expressing microglia show enhanced Aβ phagocytosis and endolysosomal trafficking, increased clustering around amyloid plaques, and increased SYK tyrosine kinase activation downstream of the major Aβ receptors, TREM2 and CLEC7A. Single microglia sequencing and digital spatial profiling identifies downstream SYK signaling modules that are differentially expressed by ACE expression in microglia that mediate endolysosomal biogenesis and trafficking, mTOR and PI3K/AKT signaling, and increased oxidative phosphorylation, while pharmacologic inhibition of SYK activity in ACE-expressing microglia abrogates the potentiated Aβ engulfment and endolysosomal trafficking. These findings establish a role for ACE in enhancing microglial immune function and they identify potential utility for ACE-expressing microglia as a cell-based therapy to augment endogenous microglial responses to Aβ in AD.
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23
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Gaur P, Bryois J, Calini D, Foo L, Hoozemans JJM, Malhotra D, Menon V. Single-nucleus and spatial transcriptomic profiling of human temporal cortex and white matter reveals novel associations with AD pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590816. [PMID: 38712204 PMCID: PMC11071354 DOI: 10.1101/2024.04.23.590816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder with complex pathological manifestations and is the leading cause of cognitive decline and dementia in elderly individuals. A major goal in AD research is to identify new therapeutic pathways by studying the molecular and cellular changes in the disease, either downstream or upstream of the pathological hallmarks. In this study, we present a comprehensive investigation of cellular heterogeneity from the temporal cortex region of 40 individuals, comprising healthy donors and individuals with differing tau and amyloid burden. Using single-nucleus transcriptome analysis of 430,271 nuclei from both gray and white matter of these individuals, we identified cell type-specific subclusters in both neuronal and glial cell types with varying degrees of association with AD pathology. In particular, these associations are present in layer specific glutamatergic (excitatory) neuronal types, along with GABAergic (inhibitory) neurons and glial subtypes. These associations were observed in early as well as late pathological progression. We extended this analysis by performing multiplexed in situ hybridization using the CARTANA platform, capturing 155 genes in 13 individuals with varying levels of tau pathology. By modeling the spatial distribution of these genes and their associations with the pathology, we not only replicated key findings from our snRNA data analysis, but also identified a set of cell type-specific genes that show selective enrichment or depletion near pathological inclusions. Together, our findings allow us to prioritize specific cell types and pathways for targeted interventions at various stages of pathological progression in AD.
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Affiliation(s)
- Pallavi Gaur
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, NY, USA
| | - Julien Bryois
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, CH-4070, Basel, Switzerland
| | - Daniela Calini
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, CH-4070, Basel, Switzerland
| | - Lynette Foo
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, CH-4070, Basel, Switzerland
| | - Jeroen J M Hoozemans
- Department of Pathology, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, Netherlands
| | - Dheeraj Malhotra
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, CH-4070, Basel, Switzerland
- MS Research Unit, Biogen, Cambridge, MA, USA
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, NY, USA
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24
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Kikuchi M, Viet J, Nagata K, Sato M, David G, Audic Y, Silverman MA, Yamamoto M, Akatsu H, Hashizume Y, Takeda S, Akamine S, Miyamoto T, Uozumi R, Gotoh S, Mori K, Ikeda M, Paillard L, Morihara T. Gene-gene functional relationships in Alzheimer's disease: CELF1 regulates KLC1 alternative splicing. Biochem Biophys Res Commun 2024; 721:150025. [PMID: 38768546 DOI: 10.1016/j.bbrc.2024.150025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 04/16/2024] [Accepted: 04/26/2024] [Indexed: 05/22/2024]
Abstract
The causes of Alzheimer's disease (AD) are poorly understood, although many genes are known to be involved in this pathology. To gain insights into the underlying molecular mechanisms, it is essential to identify the relationships between individual AD genes. Previous work has shown that the splice variant E of KLC1 (KLC1_vE) promotes AD, and that the CELF1 gene, which encodes an RNA-binding protein involved in splicing regulation, is at a risk locus for AD. Here, we identified a functional link between CELF1 and KLC1 in AD pathogenesis. Transcriptomic data from human samples from different ethnic groups revealed that CELF1 mRNA levels are low in AD brains, and the splicing pattern of KLC1 is strongly correlated with CELF1 expression levels. Specifically, KLC1_vE is negatively correlated with CELF1. Depletion and overexpression experiments in cultured cells demonstrated that the CELF1 protein down-regulates KLC1_vE. In a cross-linking and immunoprecipitation sequencing (CLIP-seq) database, CELF1 directly binds to KLC1 RNA, following which it likely modulates terminal exon usage, hence KLC1_vE formation. These findings reveal a new pathogenic pathway where a risk allele of CELF1 is associated with reduced CELF1 expression, which up-regulates KLC1_vE to promote AD.
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Affiliation(s)
- Masataka Kikuchi
- Department of Genome Informatics, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Justine Viet
- Université de Rennes, CNRS, IGDR (Institut de Génétique et Développement de Rennes), UMR 6290, F-35000, Rennes, France
| | - Kenichi Nagata
- Department of Functional Anatomy and Neuroscience, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Masahiro Sato
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Geraldine David
- Université de Rennes, CNRS, IGDR (Institut de Génétique et Développement de Rennes), UMR 6290, F-35000, Rennes, France
| | - Yann Audic
- Université de Rennes, CNRS, IGDR (Institut de Génétique et Développement de Rennes), UMR 6290, F-35000, Rennes, France
| | - Michael A Silverman
- Department of Biological Sciences, Centre for Cell Biology, Development, and Disease, Simon Fraser University, Burnaby, Canada
| | - Mitsuko Yamamoto
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Hiroyasu Akatsu
- Department of Community-based Medical Education, Graduate School of Medicine, Nagoya City University, Nagoya, Japan; Choju Medical/Neuropathological Institute, Fukushimura Hospital, Toyohashi, Japan
| | | | - Shuko Takeda
- Department of Clinical Gene Therapy, Graduate School of Medicine, Osaka University, Suita, Japan; Osaka Psychiatric Medical Center, Osaka Psychiatric Research Center, Hirakata, Japan
| | - Shoshin Akamine
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Tesshin Miyamoto
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Ryota Uozumi
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Shiho Gotoh
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Kohji Mori
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Manabu Ikeda
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan
| | - Luc Paillard
- Université de Rennes, CNRS, IGDR (Institut de Génétique et Développement de Rennes), UMR 6290, F-35000, Rennes, France.
| | - Takashi Morihara
- Department of Psychiatry, Graduate School of Medicine, Osaka University, Suita, Japan; Toyonaka Municipal Hospital, Toyonaka, Japan.
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25
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Cerneckis J, Cai H, Shi Y. Induced pluripotent stem cells (iPSCs): molecular mechanisms of induction and applications. Signal Transduct Target Ther 2024; 9:112. [PMID: 38670977 PMCID: PMC11053163 DOI: 10.1038/s41392-024-01809-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 03/09/2024] [Accepted: 03/17/2024] [Indexed: 04/28/2024] Open
Abstract
The induced pluripotent stem cell (iPSC) technology has transformed in vitro research and holds great promise to advance regenerative medicine. iPSCs have the capacity for an almost unlimited expansion, are amenable to genetic engineering, and can be differentiated into most somatic cell types. iPSCs have been widely applied to model human development and diseases, perform drug screening, and develop cell therapies. In this review, we outline key developments in the iPSC field and highlight the immense versatility of the iPSC technology for in vitro modeling and therapeutic applications. We begin by discussing the pivotal discoveries that revealed the potential of a somatic cell nucleus for reprogramming and led to successful generation of iPSCs. We consider the molecular mechanisms and dynamics of somatic cell reprogramming as well as the numerous methods available to induce pluripotency. Subsequently, we discuss various iPSC-based cellular models, from mono-cultures of a single cell type to complex three-dimensional organoids, and how these models can be applied to elucidate the mechanisms of human development and diseases. We use examples of neurological disorders, coronavirus disease 2019 (COVID-19), and cancer to highlight the diversity of disease-specific phenotypes that can be modeled using iPSC-derived cells. We also consider how iPSC-derived cellular models can be used in high-throughput drug screening and drug toxicity studies. Finally, we discuss the process of developing autologous and allogeneic iPSC-based cell therapies and their potential to alleviate human diseases.
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Affiliation(s)
- Jonas Cerneckis
- Department of Neurodegenerative Diseases, Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA
- Irell & Manella Graduate School of Biological Sciences, Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA
| | - Hongxia Cai
- Department of Neurodegenerative Diseases, Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA
| | - Yanhong Shi
- Department of Neurodegenerative Diseases, Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA.
- Irell & Manella Graduate School of Biological Sciences, Beckman Research Institute of City of Hope, Duarte, CA, 91010, USA.
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Xiu Z, Sun L, Cao H, Qu H, Glessner JT, Ding Z, Zheng G, Wang N, Xia Q, Li J, Hakonarson H, Liu W, Li J. Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders. Brain Behav Immun 2024:S0889-1591(24)00375-1. [PMID: 38677625 DOI: 10.1016/j.bbi.2024.04.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/27/2024] [Accepted: 04/22/2024] [Indexed: 04/29/2024] Open
Abstract
The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory. Response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.
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Affiliation(s)
- Zhanjie Xiu
- Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China; Department of Bioinformatics, Tianjin Medical University, Tianjin, China
| | - Ling Sun
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Haiyan Cao
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Huiqi Qu
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Joseph T Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Zhiyong Ding
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Gang Zheng
- National Supercomputer Center in Tianjin (NSCC-TJ), Tianjin, China
| | - Nan Wang
- Mills Institute for Personalized Cancer Care, Fynn Biotechnologies Ltd., Jinan, China
| | - Qianghua Xia
- Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China; Department of Bioinformatics, Tianjin Medical University, Tianjin, China
| | - Jie Li
- Laboratory of Biological Psychiatry, Institute of Mental Health, Tianjin Anding Hospital, Tianjin Medical University, Tianjin, China
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, United States; Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States.
| | - Wei Liu
- Tianjin Children's Hospital (Tianjin University Children's Hospital), Tianjin, China; Tianjin Key Laboratory of Birth Defects for Prevention and Treatment, Tianjin, China.
| | - Jin Li
- Department of Cell Biology, 2011 Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China; Department of Rheumatology and Immunology, Tianjin Medical University General Hospital, Tianjin, China.
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Hudgins AD, Zhou S, Arey RN, Rosenfeld MG, Murphy CT, Suh Y. A systems biology-based identification and in vivo functional screening of Alzheimer's disease risk genes reveal modulators of memory function. Neuron 2024:S0896-6273(24)00247-2. [PMID: 38692279 DOI: 10.1016/j.neuron.2024.04.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/18/2023] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Abstract
Genome-wide association studies (GWASs) have uncovered over 75 genomic loci associated with risk for late-onset Alzheimer's disease (LOAD), but identification of the underlying causal genes remains challenging. Studies of induced pluripotent stem cell (iPSC)-derived neurons from LOAD patients have demonstrated the existence of neuronal cell-intrinsic functional defects. Here, we searched for genetic contributions to neuronal dysfunction in LOAD using an integrative systems approach that incorporated multi-evidence-based gene mapping and network-analysis-based prioritization. A systematic perturbation screening of candidate risk genes in Caenorhabditis elegans (C. elegans) revealed that neuronal knockdown of the LOAD risk gene orthologs vha-10 (ATP6V1G2), cmd-1 (CALM3), amph-1 (BIN1), ephx-1 (NGEF), and pho-5 (ACP2) alters short-/intermediate-term memory function, the cognitive domain affected earliest during LOAD progression. These results highlight the impact of LOAD risk genes on evolutionarily conserved memory function, as mediated through neuronal endosomal dysfunction, and identify new targets for further mechanistic interrogation.
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Affiliation(s)
- Adam D Hudgins
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA
| | - Shiyi Zhou
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Rachel N Arey
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Michael G Rosenfeld
- Department of Medicine, School of Medicine, University of California, La Jolla, CA, USA; Howard Hughes Medical Institute, University of California, La Jolla, CA, USA
| | - Coleen T Murphy
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA; LSI Genomics, Princeton University, Princeton, NJ, USA.
| | - Yousin Suh
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, NY, USA; Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY, USA.
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Qiu Y, Hou Y, Gohel D, Zhou Y, Xu J, Bykova M, Yang Y, Leverenz JB, Pieper AA, Nussinov R, Caldwell JZK, Brown JM, Cheng F. Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease. Cell Rep 2024; 43:114128. [PMID: 38652661 DOI: 10.1016/j.celrep.2024.114128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/06/2024] [Accepted: 04/03/2024] [Indexed: 04/25/2024] Open
Abstract
Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.
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Affiliation(s)
- Yunguang Qiu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Dhruv Gohel
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yadi Zhou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jielin Xu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Marina Bykova
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuxin Yang
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Ruth Nussinov
- Computational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Cancer Innovation Laboratory, National Cancer Institute, Frederick, MD 21702, USA; Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv 69978, Israel
| | - Jessica Z K Caldwell
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Las Vegas, NV 89106, USA
| | - J Mark Brown
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Department of Cancer Biology, Lerner Research Institute Cleveland Clinic, Cleveland, OH 44195, USA; Center for Microbiome and Human Health, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Feixiong Cheng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA; Case Comprehensive Cancer Center, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA.
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Reddy JS, Heath L, Vander Linden A, Allen M, de Paiva Lopes K, Seifar F, Wang E, Ma Y, Poehlman WL, Quicksall ZS, Runnels A, Wang Y, Duong DM, Yin L, Xu K, Modeste ES, Shantaraman A, Dammer EB, Ping L, Oatman SR, Scanlan J, Ho C, Carrasquillo MM, Atik M, Yepez G, Mitchell AO, Nguyen TT, Chen X, Marquez DX, Reddy H, Xiao H, Seshadri S, Mayeux R, Prokop S, Lee EB, Serrano GE, Beach TG, Teich AF, Haroutunian V, Fox EJ, Gearing M, Wingo A, Wingo T, Lah JJ, Levey AI, Dickson DW, Barnes LL, De Jager P, Zhang B, Bennett D, Seyfried NT, Greenwood AK, Ertekin-Taner N. Bridging the Gap: Multi-Omics Profiling of Brain Tissue in Alzheimer's Disease and Older Controls in Multi-Ethnic Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.16.589592. [PMID: 38659743 PMCID: PMC11042309 DOI: 10.1101/2024.04.16.589592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
INTRODUCTION Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked African Americans (AA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS To bridge this gap, Accelerating Medicines Partnership in AD (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTS We generated multi-omics data and curated and harmonized phenotypic data from AA (n=306), LA (n=326), or AA and LA (n=4) brain donors plus Non-Hispanic White (n=252) and other (n=20) ethnic groups, to establish a foundational dataset enriched for AA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION Inclusion of traditionally underrepresented groups in multi-omics studies is essential to discover the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD.
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Affiliation(s)
- Joseph S Reddy
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | - Laura Heath
- Sage Bionetworks, 2901 3rd Ave #330, Seattle, WA 98121
| | | | - Mariet Allen
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | - Katia de Paiva Lopes
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison St, Chicago, IL 60612
| | - Fatemeh Seifar
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Erming Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1428 Madison Ave, New York, NY 10029
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029
| | - Yiyi Ma
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032
| | | | | | - Alexi Runnels
- New York Genome Center, 101 6th Ave, New York, NY 10013
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison St, Chicago, IL 60612
| | - Duc M Duong
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Luming Yin
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Kaiming Xu
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Erica S Modeste
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | | | - Eric B Dammer
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Lingyan Ping
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | | | - Jo Scanlan
- Sage Bionetworks, 2901 3rd Ave #330, Seattle, WA 98121
| | - Charlotte Ho
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | | | - Merve Atik
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | - Geovanna Yepez
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | | | - Thuy T Nguyen
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | - Xianfeng Chen
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | - David X Marquez
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison St, Chicago, IL 60612
- University of Illinois Chicago, 1200 West Harrison St., Chicago, Illinois 60607
| | - Hasini Reddy
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032
| | - Harrison Xiao
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032
| | - Sudha Seshadri
- The Glen Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas, 8300 Floyd Curl Drive, San Antonio TX 78229
| | - Richard Mayeux
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032
| | | | - Edward B Lee
- Center for Neurodegenerative Disease Brain Bank at the University of Pennsylvania, 3600 Spruce Street, Philadelphia, PA 19104-2676
| | - Geidy E Serrano
- Banner Sun Health Research Institute, 10515 W Santa Fe Dr, Sun City, AZ 85351
| | - Thomas G Beach
- Banner Sun Health Research Institute, 10515 W Santa Fe Dr, Sun City, AZ 85351
| | - Andrew F Teich
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032
| | - Varham Haroutunian
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1428 Madison Ave, New York, NY 10029
| | - Edward J Fox
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Marla Gearing
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Aliza Wingo
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Thomas Wingo
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - James J Lah
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Allan I Levey
- Emory University School of Medicine, 1440 Clifton Rd, Atlanta, GA 30322
| | - Dennis W Dickson
- Mayo Clinic Florida, 4500 San Pablo Rd S, Jacksonville, FL 32224
| | - Lisa L Barnes
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison St, Chicago, IL 60612
| | - Philip De Jager
- Columbia University Irving Medical Center, 622 W 168th St, New York, NY 10032
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1428 Madison Ave, New York, NY 10029
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029
| | - David Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, 1750 W Harrison St, Chicago, IL 60612
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Mews MA, Naj AC, Griswold AJ, Below JE, Bush WS. Brain and Blood Transcriptome-Wide Association Studies Identify Five Novel Genes Associated with Alzheimer's Disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.04.17.24305737. [PMID: 38699333 PMCID: PMC11065015 DOI: 10.1101/2024.04.17.24305737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2024]
Abstract
INTRODUCTION Transcriptome-wide Association Studies (TWAS) extend genome-wide association studies (GWAS) by integrating genetically-regulated gene expression models. We performed the most powerful AD-TWAS to date, using summary statistics from cis -eQTL meta-analyses and the largest clinically-adjudicated Alzheimer's Disease (AD) GWAS. METHODS We implemented the OTTERS TWAS pipeline, leveraging cis -eQTL data from cortical brain tissue (MetaBrain; N=2,683) and blood (eQTLGen; N=31,684) to predict gene expression, then applied these models to AD-GWAS data (Cases=21,982; Controls=44,944). RESULTS We identified and validated five novel gene associations in cortical brain tissue ( PRKAG1 , C3orf62 , LYSMD4 , ZNF439 , SLC11A2 ) and six genes proximal to known AD-related GWAS loci (Blood: MYBPC3 ; Brain: MTCH2 , CYB561 , MADD , PSMA5 , ANXA11 ). Further, using causal eQTL fine-mapping, we generated sparse models that retained the strength of the AD-TWAS association for MTCH2 , MADD , ZNF439 , CYB561 , and MYBPC3 . DISCUSSION Our comprehensive AD-TWAS discovered new gene associations and provided insights into the functional relevance of previously associated variants.
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Enduru N, Fernandes BS, Zhao Z. Dissecting the shared genetic architecture between Alzheimer's disease and frailty: a cross-trait meta-analyses of genome-wide association studies. Front Genet 2024; 15:1376050. [PMID: 38706793 PMCID: PMC11069310 DOI: 10.3389/fgene.2024.1376050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 04/04/2024] [Indexed: 05/07/2024] Open
Abstract
Introduction: Frailty is the most common medical condition affecting the aging population, and its prevalence increases in the population aged 65 or more. Frailty is commonly diagnosed using the frailty index (FI) or frailty phenotype (FP) assessments. Observational studies have indicated the association of frailty with Alzheimer's disease (AD). However, the shared genetic and biological mechanism of these comorbidity has not been studied. Methods: To assess the genetic relationship between AD and frailty, we examined it at single nucleotide polymorphism (SNP), gene, and pathway levels. Results: Overall, 16 genome-wide significant loci (15 unique loci) (p meta-analysis < 5 × 10-8) and 22 genes (21 unique genes) were identified between AD and frailty using cross-trait meta-analysis. The 8 shared loci implicated 11 genes: CLRN1-AS1, CRHR1, FERMT2, GRK4, LINC01929, LRFN2, MADD, RP11-368P15.1, RP11-166N6.2, RNA5SP459, and ZNF652 between AD and FI, and 8 shared loci between AD and FFS implicated 11 genes: AFF3, C1QTNF4, CLEC16A, FAM180B, FBXL19, GRK4, LINC01104, MAD1L1, RGS12, ZDHHC5, and ZNF521. The loci 4p16.3 (GRK4) was identified in both meta-analyses. The colocalization analysis supported the results of our meta-analysis in these loci. The gene-based analysis revealed 80 genes between AD and frailty, and 4 genes were initially identified in our meta-analyses: C1QTNF4, CRHR1, MAD1L1, and RGS12. The pathway analysis showed enrichment for lipoprotein particle plasma, amyloid fibril formation, protein kinase regulator, and tau protein binding. Conclusion: Overall, our results provide new insights into the genetics of AD and frailty, suggesting the existence of non-causal shared genetic mechanisms between these conditions.
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Affiliation(s)
- Nitesh Enduru
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Brisa S. Fernandes
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Faillace Department of Psychiatry and Behavioral Sciences, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX, United States
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Casanova F, Tian Q, Atkins JL, Wood AR, Williamson D, Qian Y, Zweibaum D, Ding J, Melzer D, Ferrucci L, Pilling LC. Iron and risk of dementia: Mendelian randomisation analysis in UK Biobank. J Med Genet 2024; 61:435-442. [PMID: 38191510 DOI: 10.1136/jmg-2023-109295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024]
Abstract
BACKGROUND Brain iron deposition is common in dementia, but whether serum iron is a causal risk factor is unknown. We aimed to determine whether genetic predisposition to higher serum iron status biomarkers increased risk of dementia and atrophy of grey matter. METHODS We analysed UK Biobank participants clustered into European (N=451284), African (N=7477) and South Asian (N=9570) groups by genetic similarity to the 1000 genomes project. Using Mendelian randomisation methods, we estimated the association between genetically predicted serum iron (transferrin saturation [TSAT] and ferritin), grey matter volume and genetic liability to clinically defined dementia (including Alzheimer's disease [AD], non-AD dementia, and vascular dementia) from hospital and primary care records. We also performed time-to-event (competing risks) analysis of the TSAT polygenic score on risk of clinically defined non-AD dementia. RESULTS In Europeans, higher genetically predicted TSAT increased genetic liability to dementia (Odds Ratio [OR]: 1.15, 95% Confidence Intervals [CI] 1.04 to 1.26, p=0.0051), non-AD dementia (OR: 1.27, 95% CI 1.12 to 1.45, p=0.00018) and vascular dementia (OR: 1.37, 95% CI 1.12 to 1.69, p=0.0023), but not AD (OR: 1.00, 95% CI 0.86 to 1.15, p=0.97). Higher TSAT was also associated with increased risk of non-AD dementia in participants of African, but not South Asian groups. In survival analysis using a TSAT polygenic score, the effect was independent of apolipoprotein-E ε4 genotype (with adjustment subdistribution Hazard Ratio: 1.74, 95% CI 1.33 to 2.28, p=0.00006). Genetically predicted TSAT was associated with lower grey matter volume in caudate, putamen and thalamus, and not in other areas of interest. DISCUSSION Genetic evidence supports a causal relationship between higher TSAT and risk of clinically defined non-AD and vascular dementia, in European and African groups. This association appears to be independent of apolipoprotein-E ε4.
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Affiliation(s)
- Francesco Casanova
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Qu Tian
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, Bethesda, Maryland, USA
| | - Janice L Atkins
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | | | - Yong Qian
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, Bethesda, Maryland, USA
| | - David Zweibaum
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, Bethesda, Maryland, USA
| | - Jun Ding
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, Bethesda, Maryland, USA
| | - David Melzer
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
| | - Luigi Ferrucci
- Translational Gerontology Branch Longitudinal Studies Section, National Institute on Aging, Bethesda, Maryland, USA
| | - Luke C Pilling
- Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK
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Yao X, Ouyang S, Lian Y, Peng Q, Zhou X, Huang F, Hu X, Shi F, Xia J. PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies. Genome Med 2024; 16:56. [PMID: 38627848 PMCID: PMC11020195 DOI: 10.1186/s13073-024-01330-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 04/02/2024] [Indexed: 04/19/2024] Open
Abstract
Despite the abundance of genotype-phenotype association studies, the resulting association outcomes often lack robustness and interpretations. To address these challenges, we introduce PheSeq, a Bayesian deep learning model that enhances and interprets association studies through the integration and perception of phenotype descriptions. By implementing the PheSeq model in three case studies on Alzheimer's disease, breast cancer, and lung cancer, we identify 1024 priority genes for Alzheimer's disease and 818 and 566 genes for breast cancer and lung cancer, respectively. Benefiting from data fusion, these findings represent moderate positive rates, high recall rates, and interpretation in gene-disease association studies.
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Affiliation(s)
- Xinzhi Yao
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Sizhuo Ouyang
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Yulong Lian
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Qianqian Peng
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Xionghui Zhou
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feier Huang
- College of Life Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Xuehai Hu
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China
| | - Feng Shi
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Jingbo Xia
- College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
- Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
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Neven J, Issayama LK, Dewachter I, Wilson DM. Genomic stress and impaired DNA repair in Alzheimer disease. DNA Repair (Amst) 2024; 139:103678. [PMID: 38669748 DOI: 10.1016/j.dnarep.2024.103678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024]
Abstract
Alzheimer disease (AD) is the most prominent form of dementia and has received considerable attention due to its growing burden on economic, healthcare and basic societal infrastructures. The two major neuropathological hallmarks of AD, i.e., extracellular amyloid beta (Aβ) peptide plaques and intracellular hyperphosphorylated Tau neurofibrillary tangles, have been the focus of much research, with an eye on understanding underlying disease mechanisms and identifying novel therapeutic avenues. One often overlooked aspect of AD is how Aβ and Tau may, through indirect and direct mechanisms, affect genome integrity. Herein, we review evidence that Aβ and Tau abnormalities induce excessive genomic stress and impair genome maintenance mechanisms, events that can promote DNA damage-induced neuronal cell loss and associated brain atrophy.
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Affiliation(s)
- Jolien Neven
- Hasselt University, Biomedical Research Institute, BIOMED, Hasselt 3500, Belgium
| | - Luidy Kazuo Issayama
- Hasselt University, Biomedical Research Institute, BIOMED, Hasselt 3500, Belgium
| | - Ilse Dewachter
- Hasselt University, Biomedical Research Institute, BIOMED, Hasselt 3500, Belgium
| | - David M Wilson
- Hasselt University, Biomedical Research Institute, BIOMED, Hasselt 3500, Belgium.
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Das D, Sonthalia S, Stein-O 'Brien G, Wahbeh MH, Feuer K, Goff L, Colantuoni C, Mahairaki V, Avramopoulos D. Insights for disease modeling from single-cell transcriptomics of iPSC-derived Ngn2-induced neurons and astrocytes across differentiation time and co-culture. BMC Biol 2024; 22:75. [PMID: 38566045 PMCID: PMC10985965 DOI: 10.1186/s12915-024-01867-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 03/13/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Trans-differentiation of human-induced pluripotent stem cells into neurons via Ngn2-induction (hiPSC-N) has become an efficient system to quickly generate neurons a likely significant advance for disease modeling and in vitro assay development. Recent single-cell interrogation of Ngn2-induced neurons, however, has revealed some similarities to unexpected neuronal lineages. Similarly, a straightforward method to generate hiPSC-derived astrocytes (hiPSC-A) for the study of neuropsychiatric disorders has also been described. RESULTS Here, we examine the homogeneity and similarity of hiPSC-N and hiPSC-A to their in vivo counterparts, the impact of different lengths of time post Ngn2 induction on hiPSC-N (15 or 21 days), and the impact of hiPSC-N/hiPSC-A co-culture. Leveraging the wealth of existing public single-cell RNA-seq (scRNA-seq) data in Ngn2-induced neurons and in vivo data from the developing brain, we provide perspectives on the lineage origins and maturation of hiPSC-N and hiPSC-A. While induction protocols in different labs produce consistent cell type profiles, both hiPSC-N and hiPSC-A show significant heterogeneity and similarity to multiple in vivo cell fates, and both more precisely approximate their in vivo counterparts when co-cultured. Gene expression data from the hiPSC-N show enrichment of genes linked to schizophrenia (SZ) and autism spectrum disorders (ASD) as has been previously shown for neural stem cells and neurons. These overrepresentations of disease genes are strongest in our system at early times (day 15) in Ngn2-induction/maturation of neurons, when we also observe the greatest similarity to early in vivo excitatory neurons. We have assembled this new scRNA-seq data along with the public data explored here as an integrated biologist-friendly web-resource for researchers seeking to understand this system more deeply: https://nemoanalytics.org/p?l=DasEtAlNGN2&g=NES . CONCLUSIONS While overall we support the use of the investigated cellular models for the study of neuropsychiatric disease, we also identify important limitations. We hope that this work will contribute to understanding and optimizing cellular modeling for complex brain disorders.
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Affiliation(s)
- D Das
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA
| | - S Sonthalia
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, USA
| | - G Stein-O 'Brien
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA
| | - M H Wahbeh
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA
| | - K Feuer
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA
| | - L Goff
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, USA
| | - C Colantuoni
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
- Institute of Genome Sciences, University of Maryland School of Medicine, Baltimore, USA
| | - V Mahairaki
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA
| | - D Avramopoulos
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, 733 E. Broadway, Baltimore, MD, 21205, USA.
- Department of Psychiatry, Johns Hopkins University School of Medicine, Baltimore, USA.
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Wachter A, Woodbury ME, Lombardo S, Abdourahman A, Wuest C, McGlame E, Pastika T, Tamm J, Romanul N, Yanamandra K, Bennett R, Lin G, Kwon T, Liao F, Klein C, Grinberg Y, Jaisa-Aad M, Li H, Frosch MP, Kummer MP, Das S, Dellovade T, Karran EH, Langlois X, Ried JS, Serrano-Pozo A, Talanian RV, Biber K, Hyman BT. Landscape of brain myeloid cell transcriptome along the spatiotemporal progression of Alzheimer's disease reveals distinct sequential responses to Aβ and tau. Acta Neuropathol 2024; 147:65. [PMID: 38557897 PMCID: PMC10984903 DOI: 10.1007/s00401-024-02704-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 02/01/2024] [Accepted: 02/10/2024] [Indexed: 04/04/2024]
Abstract
Human microglia are critically involved in Alzheimer's disease (AD) progression, as shown by genetic and molecular studies. However, their role in tau pathology progression in human brain has not been well described. Here, we characterized 32 human donors along progression of AD pathology, both in time-from early to late pathology-and in space-from entorhinal cortex (EC), inferior temporal gyrus (ITG), prefrontal cortex (PFC) to visual cortex (V2 and V1)-with biochemistry, immunohistochemistry, and single nuclei-RNA-sequencing, profiling a total of 337,512 brain myeloid cells, including microglia. While the majority of microglia are similar across brain regions, we identified a specific subset unique to EC which may contribute to the early tau pathology present in this region. We calculated conversion of microglia subtypes to diseased states and compared conversion patterns to those from AD animal models. Targeting genes implicated in this conversion, or their upstream/downstream pathways, could halt gene programs initiated by early tau progression. We used expression patterns of early tau progression to identify genes whose expression is reversed along spreading of spatial tau pathology (EC > ITG > PFC > V2 > V1) and identified their potential involvement in microglia subtype conversion to a diseased state. This study provides a data resource that builds on our knowledge of myeloid cell contribution to AD by defining the heterogeneity of microglia and brain macrophages during both temporal and regional pathology aspects of AD progression at an unprecedented resolution.
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Affiliation(s)
| | | | | | | | - Carolin Wuest
- AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | | | | | | | | | - Rachel Bennett
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - Gen Lin
- AbbVie Pte Ltd, Singapore, Singapore
| | | | | | - Corinna Klein
- AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | | | - Methasit Jaisa-Aad
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - Huan Li
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
| | - Matthew P Frosch
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, USA
| | | | - Sudeshna Das
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, USA
| | | | | | | | - Janina S Ried
- AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | - Alberto Serrano-Pozo
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, USA
| | | | - Knut Biber
- AbbVie Deutschland GmbH & Co. KG, Ludwigshafen, Germany
| | - Bradley T Hyman
- Massachusetts General Hospital, Boston, USA
- Harvard Medical School, Boston, USA
- Massachusetts Alzheimer's Disease Research Center, Charlestown, USA
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Bousiges O, Cretin B, Muller C, Botzung A, Sanna L, Anthony P, Philippi N, Demuynck C, Blanc F. Involvement of ApoE4 in dementia with Lewy bodies in the prodromal and demented stages: evaluation of the Strasbourg cohort. GeroScience 2024; 46:1527-1542. [PMID: 37653269 PMCID: PMC10828291 DOI: 10.1007/s11357-023-00883-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 07/19/2023] [Indexed: 09/02/2023] Open
Abstract
ApoE4 as a risk factor for dementia with Lewy bodies (DLB) is still an issue. We sought to determine the involvement of ApoE4 according to different clinical parameters in our cohort of patients from Strasbourg, France. ApoE genotyping was performed on the AlphaLewyMA cohort. In this cohort, 197 patients were genotyped: 105 DLB patients, 37 Alzheimer's disease (AD) patients, 29 patients with AD/DLB comorbidity, and 26 control subjects (CS). The groups of patients were also classified according to the stage of evolution of the disease: prodromal or demented. We analyzed other parameters in relation to ApoE4 status, such as years of education (YOE) and Alzheimer CSF biomarkers. We observed a higher proportion of ApoE4 carriers in the AD (51.4%) and AD/DLB (72.4%) groups compared to the DLB (25.7%) and CS (11.5%) groups (p < 0.0001). We found a correlation between age at disease onset and YOE in the AD group (p = 0.039) but not in the DLB group (p = 0.056). Interestingly, in the DLB group, the subgroup of patients with high YOE (≥ 11) had significantly more patients with ApoE4 than the subgroup with low YOE (< 11). AD biomarkers did not seem to be impacted by the presence of ApoE4, except for Aβ42: DLB ApoE4-positive demented patients showed a more marked Aβ42 decrease. ApoE4 does not appear to be a risk factor for "pure" DLB patients. These results suggest a strong link between ApoE4 and amyloidopathy and consequently with AD. Trial registration: AlphaLewyMa, Identifier: NCT01876459, date of registration: June 12, 2013.
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Affiliation(s)
- Olivier Bousiges
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS Team and IMAGeS Team, University of Strasbourg and CNRS, Strasbourg, France.
- Laboratory of Biochemistry and Molecular Biology, University Hospital of Strasbourg, Strasbourg, France.
| | - Benjamin Cretin
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS Team and IMAGeS Team, University of Strasbourg and CNRS, Strasbourg, France
- CM2R (Research and Resources Memory Center), Geriatrics Department, Geriatric Day Hospital, Neurogeriatric Service, University Hospital of Strasbourg, Strasbourg, France
| | - Candice Muller
- CM2R (Research and Resources Memory Center), Geriatrics Department, Geriatric Day Hospital, Neurogeriatric Service, University Hospital of Strasbourg, Strasbourg, France
| | - Anne Botzung
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS Team and IMAGeS Team, University of Strasbourg and CNRS, Strasbourg, France
- CM2R (Research and Resources Memory Center), Geriatrics Department, Geriatric Day Hospital, Neurogeriatric Service, University Hospital of Strasbourg, Strasbourg, France
| | - Lea Sanna
- CM2R (Research and Resources Memory Center), Geriatrics Department, Geriatric Day Hospital, Neurogeriatric Service, University Hospital of Strasbourg, Strasbourg, France
| | - Pierre Anthony
- CM2R, Neuropsychology Unit, Head and Neck Department, Neurology Department, University of Strasbourg, Strasbourg, France
- CM2R, Geriatrics Department and Neurology Department, Colmar General Hospital, Colmar, France
| | - Nathalie Philippi
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS Team and IMAGeS Team, University of Strasbourg and CNRS, Strasbourg, France
- CM2R, Neuropsychology Unit, Head and Neck Department, Neurology Department, University of Strasbourg, Strasbourg, France
| | - Catherine Demuynck
- CM2R (Research and Resources Memory Center), Geriatrics Department, Geriatric Day Hospital, Neurogeriatric Service, University Hospital of Strasbourg, Strasbourg, France
| | - Frédéric Blanc
- ICube Laboratory UMR 7357 and FMTS (Fédération de Médecine Translationnelle de Strasbourg), IMIS Team and IMAGeS Team, University of Strasbourg and CNRS, Strasbourg, France
- CM2R (Research and Resources Memory Center), Geriatrics Department, Geriatric Day Hospital, Neurogeriatric Service, University Hospital of Strasbourg, Strasbourg, France
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Williams ZAP, Lang L, Nicolas S, Clarke G, Cryan J, Vauzour D, Nolan YM. Do microbes play a role in Alzheimer's disease? Microb Biotechnol 2024; 17:e14462. [PMID: 38593310 PMCID: PMC11003713 DOI: 10.1111/1751-7915.14462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Accepted: 03/19/2024] [Indexed: 04/11/2024] Open
Abstract
Alzheimer's disease is a complex and progressive condition that affects essential neurological functions such as memory and reasoning. In the brain, neuronal loss, synaptic dysfunction, proteinopathy, neurofibrillary tangles, and neuroinflammation are the hallmarks of Alzheimer's disease pathophysiology. In addition, recent evidence has highlighted that microbes, whether commensal or pathogenic, also have the ability to interact with their host and to regulate its immune system, therefore participating in the exchanges that lead to peripheral inflammation and neuropathology. Because of this intimate relationship, bacteria, viruses, fungi, and protozoa have been implicated in the development of Alzheimer's disease. Here, we bring together current and most recent evidence of the role of microbes in Alzheimer's disease, raising burning questions that need to be addressed to guide therapeutic approaches and potential prophylactic strategies.
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Affiliation(s)
- Zoë A. P. Williams
- Department of Anatomy and NeuroscienceUniversity College CorkCorkIreland
- APC Microbiome IrelandUniversity College CorkCorkIreland
| | - Leonie Lang
- Norwich Medical School, Faculty of Medicine and Health SciencesUniversity of East AngliaNorwichUK
| | - Sarah Nicolas
- Department of Anatomy and NeuroscienceUniversity College CorkCorkIreland
- APC Microbiome IrelandUniversity College CorkCorkIreland
| | - Gerard Clarke
- APC Microbiome IrelandUniversity College CorkCorkIreland
- Department of Psychiatry and Neurobehavioural ScienceUniversity College CorkCorkIreland
| | - John Cryan
- Department of Anatomy and NeuroscienceUniversity College CorkCorkIreland
- APC Microbiome IrelandUniversity College CorkCorkIreland
| | - David Vauzour
- Norwich Medical School, Faculty of Medicine and Health SciencesUniversity of East AngliaNorwichUK
| | - Yvonne M. Nolan
- Department of Anatomy and NeuroscienceUniversity College CorkCorkIreland
- APC Microbiome IrelandUniversity College CorkCorkIreland
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39
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Sanders KL, Manuel AM, Liu A, Leng B, Chen X, Zhao Z. Unveiling Gene Interactions in Alzheimer's Disease by Integrating Genetic and Epigenetic Data with a Network-Based Approach. EPIGENOMES 2024; 8:14. [PMID: 38651367 PMCID: PMC11036294 DOI: 10.3390/epigenomes8020014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 03/26/2024] [Accepted: 03/28/2024] [Indexed: 04/25/2024] Open
Abstract
Alzheimer's Disease (AD) is a complex disease and the leading cause of dementia in older people. We aimed to uncover aspects of AD's pathogenesis that may contribute to drug repurposing efforts by integrating DNA methylation and genetic data. Implementing the network-based tool, a dense module search of genome-wide association studies (dmGWAS), we integrated a large-scale GWAS dataset with DNA methylation data to identify gene network modules associated with AD. Our analysis yielded 286 significant gene network modules. Notably, the foremost module included the BIN1 gene, showing the largest GWAS signal, and the GNAS gene, the most significantly hypermethylated. We conducted Web-based Cell-type-Specific Enrichment Analysis (WebCSEA) on genes within the top 10% of dmGWAS modules, highlighting monocyte as the most significant cell type (p < 5 × 10-12). Functional enrichment analysis revealed Gene Ontology Biological Process terms relevant to AD pathology (adjusted p < 0.05). Additionally, drug target enrichment identified five FDA-approved targets (p-value = 0.03) for further research. In summary, dmGWAS integration of genetic and epigenetic signals unveiled new gene interactions related to AD, offering promising avenues for future studies.
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Affiliation(s)
- Keith L. Sanders
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Astrid M. Manuel
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Andi Liu
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
| | - Boyan Leng
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Xiangning Chen
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
| | - Zhongming Zhao
- Center for Precision Health, McWilliams School of Biomedical Informatics, Houston, TX 77030, USA; (K.L.S.); (A.M.M.); (A.L.); (X.C.)
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, Houston, TX 77030, USA
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40
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Nicolas G. Recent advances in Alzheimer disease genetics. Curr Opin Neurol 2024; 37:154-165. [PMID: 38235704 DOI: 10.1097/wco.0000000000001242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
PURPOSE OF REVIEW Genetics studies provide important insights into Alzheimer disease (AD) etiology and mechanisms. Critical advances have been made recently, mainly thanks to the access to novel techniques and larger studies. RECENT FINDINGS In monogenic AD, progress has been made with a better understanding of the mechanisms associated with pathogenic variants and the input of clinical studies in presymptomatic individuals. In complex AD, increasing sample sizes in both DNA chip-based (genome-wide association studies, GWAS) and exome/genome sequencing case-control studies unveiled novel common and rare risk factors, while the understanding of their combined effect starts to suggest the existence of rare families with oligogenic inheritance of early-onset, nonmonogenic, AD. SUMMARY Most genetic risk factors with a known consequence designate the aggregation of the Aβ peptide as a core etiological factor in complex AD thus confirming that the research based on monogenic AD - where the amyloid cascade seems more straightforward - is relevant to complex AD as well. Novel mechanistic insights and risk factor studies unveiling novel factors and attempting to combine the effect of common and rare variants will offer promising perspectives for future AD prevention, at least regarding early-onset AD, and probably in case of later onset as well.
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Affiliation(s)
- Gaël Nicolas
- Univ Rouen Normandie, Normandie Univ, Inserm U1245 and CHU Rouen, Department of Genetics and CNRMAJ, F-76000 Rouen, France
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41
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Ceylan B, Düz E, Çakir T. Personalized Protein-Protein Interaction Networks Towards Unraveling the Molecular Mechanisms of Alzheimer's Disease. Mol Neurobiol 2024; 61:2120-2135. [PMID: 37855983 DOI: 10.1007/s12035-023-03690-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 10/02/2023] [Indexed: 10/20/2023]
Abstract
Alzheimer's disease (AD) is a highly heterogenous neurodegenerative disease, and several omic-based datasets were generated in the last decade from the patients with the disease. However, the vast majority of studies evaluate these datasets in bulk by considering all the patients as a single group, which obscures the molecular differences resulting from the heterogeneous nature of the disease. In this study, we adopted a personalized approach and analyzed the transcriptome data from 403 patients individually by mapping the data on a human protein-protein interaction network. Patient-specific subnetworks were discovered and analyzed in terms of the genes in the subnetworks, enriched functional terms, and known AD genes. We identified several affected pathways that could not be captured by the bulk comparison. We also showed that our personalized findings point to patterns of alterations consistent with the recently suggested AD subtypes.
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Affiliation(s)
- Betül Ceylan
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Elif Düz
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey
| | - Tunahan Çakir
- Department of Bioengineering, Gebze Technical University, 41400, Gebze, Kocaeli, Turkey.
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42
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Fujita M, Gao Z, Zeng L, McCabe C, White CC, Ng B, Green GS, Rozenblatt-Rosen O, Phillips D, Amir-Zilberstein L, Lee H, Pearse RV, Khan A, Vardarajan BN, Kiryluk K, Ye CJ, Klein HU, Wang G, Regev A, Habib N, Schneider JA, Wang Y, Young-Pearse T, Mostafavi S, Bennett DA, Menon V, De Jager PL. Cell subtype-specific effects of genetic variation in the Alzheimer's disease brain. Nat Genet 2024; 56:605-614. [PMID: 38514782 DOI: 10.1038/s41588-024-01685-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 02/08/2024] [Indexed: 03/23/2024]
Abstract
The relationship between genetic variation and gene expression in brain cell types and subtypes remains understudied. Here, we generated single-nucleus RNA sequencing data from the neocortex of 424 individuals of advanced age; we assessed the effect of genetic variants on RNA expression in cis (cis-expression quantitative trait loci) for seven cell types and 64 cell subtypes using 1.5 million transcriptomes. This effort identified 10,004 eGenes at the cell type level and 8,099 eGenes at the cell subtype level. Many eGenes are only detected within cell subtypes. A new variant influences APOE expression only in microglia and is associated with greater cerebral amyloid angiopathy but not Alzheimer's disease pathology, after adjusting for APOEε4, providing mechanistic insights into both pathologies. Furthermore, only a TMEM106B variant affects the proportion of cell subtypes. Integration of these results with genome-wide association studies highlighted the targeted cell type and probable causal gene within Alzheimer's disease, schizophrenia, educational attainment and Parkinson's disease loci.
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Affiliation(s)
- Masashi Fujita
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Zongmei Gao
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Lu Zeng
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Charles C White
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Bernard Ng
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Gilad Sahar Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Devan Phillips
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | | | - Hyo Lee
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Atlas Khan
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Badri N Vardarajan
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Krzysztof Kiryluk
- Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, NY, USA
| | - Chun Jimmie Ye
- Institute for Human Genetics, University of California, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
| | - Hans-Ulrich Klein
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Gao Wang
- Department of Neurology, College of Physicians and Surgeons, Columbia University and the New York Presbyterian Hospital, New York, NY, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Julie A Schneider
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Tracy Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sara Mostafavi
- Department of Statistics, Centre for Molecular Medicine and Therapeutics, British Columbia Children's Hospital, University of British Columbia, Vancouver, British Columbia, Canada
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Vilas Menon
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA.
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Soni N, Hohsfield LA, Tran KM, Kawauchi S, Walker A, Javonillo D, Phan J, Matheos D, Da Cunha C, Uyar A, Milinkeviciute G, Gomez‐Arboledas A, Tran K, Kaczorowski CC, Wood MA, Tenner AJ, LaFerla FM, Carter GW, Mortazavi A, Swarup V, MacGregor GR, Green KN. Genetic diversity promotes resilience in a mouse model of Alzheimer's disease. Alzheimers Dement 2024; 20:2794-2816. [PMID: 38426371 PMCID: PMC11032575 DOI: 10.1002/alz.13753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 01/29/2024] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
INTRODUCTION Alzheimer's disease (AD) is a neurodegenerative disorder with multifactorial etiology, including genetic factors that play a significant role in disease risk and resilience. However, the role of genetic diversity in preclinical AD studies has received limited attention. METHODS We crossed five Collaborative Cross strains with 5xFAD C57BL/6J female mice to generate F1 mice with and without the 5xFAD transgene. Amyloid plaque pathology, microglial and astrocytic responses, neurofilament light chain levels, and gene expression were assessed at various ages. RESULTS Genetic diversity significantly impacts AD-related pathology. Hybrid strains showed resistance to amyloid plaque formation and neuronal damage. Transcriptome diversity was maintained across ages and sexes, with observable strain-specific variations in AD-related phenotypes. Comparative gene expression analysis indicated correlations between mouse strains and human AD. DISCUSSION Increasing genetic diversity promotes resilience to AD-related pathogenesis, relative to an inbred C57BL/6J background, reinforcing the importance of genetic diversity in uncovering resilience in the development of AD. HIGHLIGHTS Genetic diversity's impact on AD in mice was explored. Diverse F1 mouse strains were used for AD study, via the Collaborative Cross. Strain-specific variations in AD pathology, glia, and transcription were found. Strains resilient to plaque formation and plasma neurofilament light chain (NfL) increases were identified. Correlations with human AD transcriptomics were observed.
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Affiliation(s)
- Neelakshi Soni
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Lindsay A. Hohsfield
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Kristine M. Tran
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Shimako Kawauchi
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Transgenic Mouse Facility, ULAROffice of ResearchUniversity of CaliforniaIrvineCaliforniaUSA
| | - Amber Walker
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Transgenic Mouse Facility, ULAROffice of ResearchUniversity of CaliforniaIrvineCaliforniaUSA
| | - Dominic Javonillo
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Jimmy Phan
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Dina Matheos
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
| | - Celia Da Cunha
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Asli Uyar
- The Jackson LaboratoryBar HarborMaineUSA
| | - Giedre Milinkeviciute
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Angela Gomez‐Arboledas
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Katelynn Tran
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | | | - Marcelo A. Wood
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Andrea J. Tenner
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Molecular Biology and BiochemistryUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Pathology and Laboratory MedicineUniversity of CaliforniaIrvineCaliforniaUSA
| | - Frank M. LaFerla
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | | | - Ali Mortazavi
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Developmental and Cellular BiologyUniversity of CaliforniaIrvineCaliforniaUSA
- Center for Complex Biological SystemsUniversity of CaliforniaIrvineCaliforniaUSA
| | - Vivek Swarup
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
| | - Grant R. MacGregor
- Transgenic Mouse Facility, ULAROffice of ResearchUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Developmental and Cellular BiologyUniversity of CaliforniaIrvineCaliforniaUSA
| | - Kim N. Green
- Department of Neurobiology and BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Institute for Memory Impairments and Neurological DisordersUniversity of CaliforniaIrvineCaliforniaUSA
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Xicota L, Cosentino S, Vardarajan B, Mayeux R, Perls TT, Andersen SL, Zmuda JM, Thyagarajan B, Yashin A, Wojczynski MK, Krinsky‐McHale S, Handen BL, Christian BT, Head E, Mapstone ME, Schupf N, Lee JH, Barral S. Whole genome-wide sequence analysis of long-lived families (Long-Life Family Study) identifies MTUS2 gene associated with late-onset Alzheimer's disease. Alzheimers Dement 2024; 20:2670-2679. [PMID: 38380866 PMCID: PMC11032545 DOI: 10.1002/alz.13718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 11/17/2023] [Accepted: 01/04/2024] [Indexed: 02/22/2024]
Abstract
INTRODUCTION Late-onset Alzheimer's disease (LOAD) has a strong genetic component. Participants in Long-Life Family Study (LLFS) exhibit delayed onset of dementia, offering a unique opportunity to investigate LOAD genetics. METHODS We conducted a whole genome sequence analysis of 3475 LLFS members. Genetic associations were examined in six independent studies (N = 14,260) with a wide range of LOAD risk. Association analysis in a sub-sample of the LLFS cohort (N = 1739) evaluated the association of LOAD variants with beta amyloid (Aβ) levels. RESULTS We identified several single nucleotide polymorphisms (SNPs) in tight linkage disequilibrium within the MTUS2 gene associated with LOAD (rs73154407, p = 7.6 × 10-9). Association of MTUS2 variants with LOAD was observed in the five independent studies and was significantly stronger within high levels of Aβ42/40 ratio compared to lower amyloid. DISCUSSION MTUS2 encodes a microtubule associated protein implicated in the development and function of the nervous system, making it a plausible candidate to investigate LOAD biology. HIGHLIGHTS Long-Life Family Study (LLFS) families may harbor late onset Alzheimer's dementia (LOAD) variants. LLFS whole genome sequence analysis identified MTUS2 gene variants associated with LOAD. The observed LLFS variants generalized to cohorts with wide range of LOAD risk. The association of MTUS2 with LOAD was stronger within high levels of beta amyloid. Our results provide evidence for MTUS2 gene as a novel LOAD candidate locus.
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Affiliation(s)
- Laura Xicota
- Department of NeurologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Stephanie Cosentino
- Department of NeurologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Badri Vardarajan
- Department of NeurologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
- Gertrude H. Sergievsky CenterColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Richard Mayeux
- Department of NeurologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
- Gertrude H. Sergievsky CenterColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Thomas T. Perls
- Section of GeriatricsDepartment of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Stacy L. Andersen
- Section of GeriatricsDepartment of MedicineBoston University School of MedicineBostonMassachusettsUSA
| | - Joseph M. Zmuda
- Department of EpidemiologyGraduate School of Public Health, University of PittsburghPittsburghPennsylvaniaUSA
| | - Bharat Thyagarajan
- Department of Laboratory Medicine and PathologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Anatoli Yashin
- Biodemography of Aging Research Unit, Social Science Research Institute, Duke UniversityDurhamNorth CarolinaUSA
| | - Mary K. Wojczynski
- Division of Statistical GenomicsDepartment of GeneticsWashington University School of MedicineSt. LouisMissouriUSA
| | - Sharon Krinsky‐McHale
- Gertrude H. Sergievsky CenterColumbia University Irving Medical CenterNew York CityNew YorkUSA
- Department of PsychologyNew York Institute for Basic Research in Developmental DisabilitiesStaten IslandNew YorkUSA
| | - Benjamin L. Handen
- Department of PsychiatryUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Bradley T. Christian
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin‐Madison School of Medicine and Public HealthMadisonWisconsinUSA
- Department of Medical PhysicsUniversity of Wisconsin‐Madison School of Medicine, and Public HealthMadisonWisconsinUSA
| | - Elizabeth Head
- Department of Pathology and Laboratory MedicineUniversity of CaliforniaIrvineCaliforniaUSA
| | - Mark E. Mapstone
- Department of NeurologyInstitute for Memory Impairments and Neurological Disorders, University of CaliforniaIrvineCaliforniaUSA
| | - Nicole Schupf
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Joseph H. Lee
- Department of NeurologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
- Gertrude H. Sergievsky CenterColumbia University Irving Medical CenterNew York CityNew YorkUSA
| | - Sandra Barral
- Department of NeurologyColumbia University Irving Medical CenterNew York CityNew YorkUSA
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical CenterNew York CityNew YorkUSA
- Gertrude H. Sergievsky CenterColumbia University Irving Medical CenterNew York CityNew YorkUSA
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Selkoe DJ. The advent of Alzheimer treatments will change the trajectory of human aging. NATURE AGING 2024; 4:453-463. [PMID: 38641654 DOI: 10.1038/s43587-024-00611-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 03/08/2024] [Indexed: 04/21/2024]
Abstract
Slowing neurodegenerative disorders of late life has lagged behind progress on other chronic diseases. But advances in two areas, biochemical pathology and human genetics, have now identified early pathogenic events, enabling molecular hypotheses and disease-modifying treatments. A salient example is the discovery that antibodies to amyloid ß-protein, long debated as a causative factor in Alzheimer's disease (AD), clear amyloid plaques, decrease levels of abnormal tau proteins and slow cognitive decline. Approval of amyloid antibodies as the first disease-modifying treatments means a gradually rising fraction of the world's estimated 60 million people with symptomatic disease may decline less or even stabilize. Society is entering an era in which the unchecked devastation of AD is no longer inevitable. This Perspective considers the impact of slowing AD and other neurodegenerative disorders on the trajectory of aging, allowing people to survive into late life with less functional decline. The implications of this moment for medicine and society are profound.
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Affiliation(s)
- Dennis J Selkoe
- Ann Romney Center for Neurologic Diseases Brigham and Women's Hospital Harvard Medical School, Boston, MA, USA.
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Hu T, Dai Q, Epstein MP, Yang J. Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.28.24305044. [PMID: 38585769 PMCID: PMC10996749 DOI: 10.1101/2024.03.28.24305044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.
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Affiliation(s)
- Tingyang Hu
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qile Dai
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
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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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48
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Casten LG, Koomar T, Elsadany M, McKone C, Tysseling B, Sasidharan M, Tomblin JB, Michaelson JJ. Lingo: an automated, web-based deep phenotyping platform for language ability. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.29.24305034. [PMID: 38585791 PMCID: PMC10996758 DOI: 10.1101/2024.03.29.24305034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Background Language and the ability to communicate effectively are key factors in mental health and well-being. Despite this critical importance, research on language is limited by the lack of a scalable phenotyping toolkit. Methods Here, we describe and showcase Lingo - a flexible online battery of language and nonverbal reasoning skills based on seven widely used tasks (COWAT, picture narration, vocal rhythm entrainment, rapid automatized naming, following directions, sentence repetition, and nonverbal reasoning). The current version of Lingo takes approximately 30 minutes to complete, is entirely open source, and allows for a wide variety of performance metrics to be extracted. We asked > 1,300 individuals from multiple samples to complete Lingo, then investigated the validity and utility of the resulting data. Results We conducted an exploratory factor analysis across 14 features derived from the seven assessments, identifying five factors. Four of the five factors showed acceptable test-retest reliability (Pearson's R > 0.7). Factor 2 showed the highest reliability (Pearson's R = 0.95) and loaded primarily on sentence repetition task performance. We validated Lingo with objective measures of language ability by comparing performance to gold-standard assessments: CELF-5 and the VABS-3. Factor 2 was significantly associated with the CELF-5 "core language ability" scale (Pearson's R = 0.77, p-value < 0.05) and the VABS-3 "communication" scale (Pearson's R = 0.74, p-value < 0.05). Factor 2 was positively associated with phenotypic and genetic measures of socieconomic status. Interestingly, we found the parents of children with language impairments had lower Factor 2 scores (p-value < 0.01). Finally, we found Lingo factor scores were significantly predictive of numerous psychiatric and neurodevelopmental conditions. Conclusions Together, these analyses support Lingo as a powerful platform for scalable deep phenotyping of language and other cognitive abilities. Additionally, exploratory analyses provide supporting evidence for the heritability of language ability and the complex relationship between mental health and language.
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Affiliation(s)
- Lucas G. Casten
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Tanner Koomar
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Muhammad Elsadany
- Interdisciplinary Graduate Program in Genetics, University of Iowa, Iowa City, IA
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Caleb McKone
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | - Ben Tysseling
- Department of Psychiatry, University of Iowa, Iowa City, IA
| | | | - J. Bruce Tomblin
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA
| | - Jacob J. Michaelson
- Department of Psychiatry, University of Iowa, Iowa City, IA
- Department of Communication Sciences and Disorders, University of Iowa, Iowa City, IA
- Iowa Neuroscience Institute, University of Iowa, Iowa City, IA
- Hawkeye Intellectual and Developmental Disabilities Research Center (Hawk-IDDRC), University of Iowa, Iowa City, IA
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49
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Gao S, Wang T, Han Z, Hu Y, Zhu P, Xue Y, Huang C, Chen Y, Liu G. Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity. Brief Bioinform 2024; 25:bbae140. [PMID: 38711368 PMCID: PMC11074593 DOI: 10.1093/bib/bbae140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 02/22/2024] [Accepted: 03/14/2024] [Indexed: 05/08/2024] Open
Abstract
Common genetic variants and susceptibility loci associated with Alzheimer's disease (AD) have been discovered through large-scale genome-wide association studies (GWAS), GWAS by proxy (GWAX) and meta-analysis of GWAS and GWAX (GWAS+GWAX). However, due to the very low repeatability of AD susceptibility loci and the low heritability of AD, these AD genetic findings have been questioned. We summarize AD genetic findings from the past 10 years and provide a new interpretation of these findings in the context of statistical heterogeneity. We discovered that only 17% of AD risk loci demonstrated reproducibility with a genome-wide significance of P < 5.00E-08 across all AD GWAS and GWAS+GWAX datasets. We highlighted that the AD GWAS+GWAX with the largest sample size failed to identify the most significant signals, the maximum number of genome-wide significant genetic variants or maximum heritability. Additionally, we identified widespread statistical heterogeneity in AD GWAS+GWAX datasets, but not in AD GWAS datasets. We consider that statistical heterogeneity may have attenuated the statistical power in AD GWAS+GWAX and may contribute to explaining the low repeatability (17%) of genome-wide significant AD susceptibility loci and the decreased AD heritability (40-2%) as the sample size increased. Importantly, evidence supports the idea that a decrease in statistical heterogeneity facilitates the identification of genome-wide significant genetic loci and contributes to an increase in AD heritability. Collectively, current AD GWAX and GWAS+GWAX findings should be meticulously assessed and warrant additional investigation, and AD GWAS+GWAX should employ multiple meta-analysis methods, such as random-effects inverse variance-weighted meta-analysis, which is designed specifically for statistical heterogeneity.
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Affiliation(s)
- Shan Gao
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, No. 10, Xitoutiao, You’an Men Wai, Fengtai District, Beijing 100069, China
| | - Tao Wang
- Chinese Institute for Brain Research, No. 26, Kexueyuan Road, Changping District, Beijing 102206, China
| | - Zhifa Han
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, No. 5, Dongdan Santichao, Dongcheng District, Beijing 100193, China
| | - Yang Hu
- School of Computer Science and Technology, Harbin Institute of Technology, No. 92, Xidazhi Street, Nangang District, Harbin 150006, China
| | - Ping Zhu
- Beijing Institute of Brain Disorders, Laboratory of Brain Disorders, Ministry of Science and Technology, Collaborative Innovation Center for Brain Disorders, Capital Medical University, No. 10, Xitoutiao, You’an Men Wai, Fengtai District, Beijing 100069, China
| | - Yanli Xue
- School of Biomedical Engineering, Capital Medical University, No. 10 Xitoutiao, You'an Men Wai, Fengtai District, Beijing 100069, China
| | - Chen Huang
- Dr. Neher’s Biophysics Laboratory for Innovative Drug Discovery, State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Avenida WaiLong, Taipa 999078, Macao SAR, China
| | - Yan Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
- Institute of Chronic Disease Prevention and Control, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
| | - Guiyou Liu
- Chinese Institute for Brain Research, No. 26, Kexueyuan Road, Changping District, Beijing 102206, China
- Department of Epidemiology and Biostatistics, School of Public Health, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
- Institute of Chronic Disease Prevention and Control, Wannan Medical College, No. 22, Wenchang Road, Wuhu 241002, Anhui, China
- Key Laboratory of Cerebral Microcirculation in Universities of Shandong, Department of Neurology, Second Affiliated Hospital, Shandong First Medical University and Shandong Academy of Medical Sciences, Taian 271000, Shandong, China
- Beijing Key Laboratory of Hypoxia Translational Medicine, National Engineering Laboratory of Internet Medical Diagnosis and Treatment Technology, Xuanwu Hospital, Capital Medical University, No. 45, Changchun Road, Xicheng District, Beijing 100053, China
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50
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Guo H, Urban AE, Wong WH. Prioritizing disease-related rare variants by integrating gene expression data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.19.585836. [PMID: 38562756 PMCID: PMC10983955 DOI: 10.1101/2024.03.19.585836] [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
Rare variants, comprising a vast majority of human genetic variations, are likely to have more deleterious impact on human diseases compared to common variants. Here we present carrier statistic, a statistical framework to prioritize disease-related rare variants by integrating gene expression data. By quantifying the impact of rare variants on gene expression, carrier statistic can prioritize those rare variants that have large functional consequence in the diseased patients. Through simulation studies and analyzing real multi-omics dataset, we demonstrated that carrier statistic is applicable in studies with limited sample size (a few hundreds) and achieves substantially higher sensitivity than existing rare variants association methods. Application to Alzheimer's disease reveals 16 rare variants within 15 genes with extreme carrier statistics. The carrier statistic method can be applied to various rare variant types and is adaptable to other omics data modalities, offering a powerful tool for investigating the molecular mechanisms underlying complex diseases.
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Affiliation(s)
- Hanmin Guo
- Department of Statistics, Stanford University, Stanford, California 94305, USA
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Alexander Eckehart Urban
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California 94305, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Wing Hung Wong
- Department of Statistics, Stanford University, Stanford, California 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California 94305, USA
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