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Reddy JS, Heath L, Linden AV, Allen M, Lopes KDP, 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. Alzheimers Dement 2024. [PMID: 39215503 DOI: 10.1002/alz.14208] [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: 04/14/2024] [Revised: 07/24/2024] [Accepted: 07/27/2024] [Indexed: 09/04/2024]
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 Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTS We generated multi-omics data and curated and harmonized phenotypic data from BA (n = 306), LA (n = 326), or BA 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 BA 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 The inclusion of traditionally underrepresented groups in multi-omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD. HIGHLIGHTS Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi-ethnic populations. Brain multi-omics data is generated from Black American, Latin American, and non-Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.
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Affiliation(s)
| | | | | | | | - Katia de Paiva Lopes
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Fatemeh Seifar
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Erming Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Yiyi Ma
- Columbia University Irving Medical Center, New York, New York, USA
| | | | | | | | - Yanling Wang
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Duc M Duong
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Luming Yin
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Kaiming Xu
- Emory University School of Medicine, Atlanta, Georgia, USA
| | | | | | - Eric B Dammer
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Lingyan Ping
- Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Jo Scanlan
- Sage Bionetworks, Seattle, Washington, USA
| | | | | | - Merve Atik
- Mayo Clinic Florida, Jacksonville, Florida, USA
| | | | | | | | | | - David X Marquez
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
- University of Illinois Chicago, Chicago, Illinois, USA
| | - Hasini Reddy
- Columbia University Irving Medical Center, New York, New York, USA
| | - Harrison Xiao
- Columbia University Irving Medical Center, New York, New York, USA
| | - Sudha Seshadri
- The Glen Biggs Institute for Alzheimer's & Neurodegenerative Diseases, University of Texas, San Antonio, Texas, USA
| | - Richard Mayeux
- Columbia University Irving Medical Center, New York, New York, USA
| | | | - Edward B Lee
- Center for Neurodegenerative Disease Brain Bank at the University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Geidy E Serrano
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Thomas G Beach
- Banner Sun Health Research Institute, Sun City, Arizona, USA
| | - Andrew F Teich
- Columbia University Irving Medical Center, New York, New York, USA
| | - Varham Haroutunian
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Edward J Fox
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Marla Gearing
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Aliza Wingo
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Thomas Wingo
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - James J Lah
- Emory University School of Medicine, Atlanta, Georgia, USA
| | - Allan I Levey
- Emory University School of Medicine, Atlanta, Georgia, USA
| | | | - Lisa L Barnes
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
| | - Philip De Jager
- Columbia University Irving Medical Center, New York, New York, USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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İş Ö, Wang X, Reddy JS, Min Y, Yilmaz E, Bhattarai P, Patel T, Bergman J, Quicksall Z, Heckman MG, Tutor-New FQ, Can Demirdogen B, White L, Koga S, Krause V, Inoue Y, Kanekiyo T, Cosacak MI, Nelson N, Lee AJ, Vardarajan B, Mayeux R, Kouri N, Deniz K, Carnwath T, Oatman SR, Lewis-Tuffin LJ, Nguyen T, Carrasquillo MM, Graff-Radford J, Petersen RC, Jr Jack CR, Kantarci K, Murray ME, Nho K, Saykin AJ, Dickson DW, Kizil C, Allen M, Ertekin-Taner N. Gliovascular transcriptional perturbations in Alzheimer's disease reveal molecular mechanisms of blood brain barrier dysfunction. Nat Commun 2024; 15:4758. [PMID: 38902234 PMCID: PMC11190273 DOI: 10.1038/s41467-024-48926-6] [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: 10/09/2023] [Accepted: 05/17/2024] [Indexed: 06/22/2024] Open
Abstract
To uncover molecular changes underlying blood-brain-barrier dysfunction in Alzheimer's disease, we performed single nucleus RNA sequencing in 24 Alzheimer's disease and control brains and focused on vascular and astrocyte clusters as main cell types of blood-brain-barrier gliovascular-unit. The majority of the vascular transcriptional changes were in pericytes. Of the vascular molecular targets predicted to interact with astrocytic ligands, SMAD3, upregulated in Alzheimer's disease pericytes, has the highest number of ligands including VEGFA, downregulated in Alzheimer's disease astrocytes. We validated these findings with external datasets comprising 4,730 pericyte and 150,664 astrocyte nuclei. Blood SMAD3 levels are associated with Alzheimer's disease-related neuroimaging outcomes. We determined inverse relationships between pericytic SMAD3 and astrocytic VEGFA in human iPSC and zebrafish models. Here, we detect vast transcriptome changes in Alzheimer's disease at the gliovascular-unit, prioritize perturbed pericytic SMAD3-astrocytic VEGFA interactions, and validate these in cross-species models to provide a molecular mechanism of blood-brain-barrier disintegrity in Alzheimer's disease.
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Affiliation(s)
- Özkan İş
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Joseph S Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Yuhao Min
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Elanur Yilmaz
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Prabesh Bhattarai
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Tulsi Patel
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Michael G Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | | | - Birsen Can Demirdogen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
- Department of Biomedical Engineering, TOBB University of Economics and Technology, Ankara, Turkey
| | - Launia White
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL, USA
| | - Shunsuke Koga
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Vincent Krause
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Yasuteru Inoue
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Mehmet Ilyas Cosacak
- German Center for Neurodegenerative Diseases (DZNE) within Helmholtz Association, Dresden, Germany
| | - Nastasia Nelson
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Annie J Lee
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Badri Vardarajan
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Richard Mayeux
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Naomi Kouri
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Kaancan Deniz
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Troy Carnwath
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | - Laura J Lewis-Tuffin
- Mayo Clinic Florida Cytometry and Cell Imaging Laboratory, Mayo Clinic, Jacksonville, FL, USA
| | - Thuy Nguyen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | | | | | - Ronald C Petersen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Alzheimer's Disease Research Center, Rochester, MN, USA
| | | | - Kejal Kantarci
- Mayo Clinic Alzheimer's Disease Research Center, Rochester, MN, USA
| | | | - Kwangsik Nho
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Caghan Kizil
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
- The Gertrude H. Sergievsky Center, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, Jacksonville, FL, USA.
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA.
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3
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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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Tsartsalis S, Sleven H, Fancy N, Wessely F, Smith AM, Willumsen N, Cheung TKD, Rokicki MJ, Chau V, Ifie E, Khozoie C, Ansorge O, Yang X, Jenkyns MH, Davey K, McGarry A, Muirhead RCJ, Debette S, Jackson JS, Montagne A, Owen DR, Miners JS, Love S, Webber C, Cader MZ, Matthews PM. A single nuclear transcriptomic characterisation of mechanisms responsible for impaired angiogenesis and blood-brain barrier function in Alzheimer's disease. Nat Commun 2024; 15:2243. [PMID: 38472200 PMCID: PMC10933340 DOI: 10.1038/s41467-024-46630-z] [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: 10/18/2021] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Brain perfusion and blood-brain barrier (BBB) integrity are reduced early in Alzheimer's disease (AD). We performed single nucleus RNA sequencing of vascular cells isolated from AD and non-diseased control brains to characterise pathological transcriptional signatures responsible for this. We show that endothelial cells (EC) are enriched for expression of genes associated with susceptibility to AD. Increased β-amyloid is associated with BBB impairment and a dysfunctional angiogenic response related to a failure of increased pro-angiogenic HIF1A to increased VEGFA signalling to EC. This is associated with vascular inflammatory activation, EC senescence and apoptosis. Our genomic dissection of vascular cell risk gene enrichment provides evidence for a role of EC pathology in AD and suggests that reducing vascular inflammatory activation and restoring effective angiogenesis could reduce vascular dysfunction contributing to the genesis or progression of early AD.
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Affiliation(s)
- Stergios Tsartsalis
- Department of Brain Sciences, Imperial College London, London, UK
- Department of Psychiatry, University of Geneva, Geneva, Switzerland
| | - Hannah Sleven
- Nuffield Department of Clinical Neurosciences, Kavli Institute for Nanoscience Discovery, Dorothy Crowfoot Hodgkin Building, Sherrington Road, University of Oxford, Oxford, UK
| | - Nurun Fancy
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Frank Wessely
- UK Dementia Research Institute Centre, Cardiff University, Cardiff, UK
| | - Amy M Smith
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
- Centre for Brain Research and Department of Pharmacology and Clinical Pharmacology, University of Auckland, Auckland, New Zealand
| | - Nanet Willumsen
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - To Ka Dorcas Cheung
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Michal J Rokicki
- UK Dementia Research Institute Centre, Cardiff University, Cardiff, UK
| | - Vicky Chau
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Eseoghene Ifie
- Neuropathology Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Combiz Khozoie
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Olaf Ansorge
- Neuropathology Unit, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Xin Yang
- Department of Brain Sciences, Imperial College London, London, UK
- St Edmund Hall, University of Oxford, Oxford, UK
| | - Marion H Jenkyns
- Department of Brain Sciences, Imperial College London, London, UK
| | - Karen Davey
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Aisling McGarry
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Robert C J Muirhead
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Stephanie Debette
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, Team ELEANOR, UMR 1219, 33000, Bordeaux, France
| | - Johanna S Jackson
- Department of Brain Sciences, Imperial College London, London, UK
- UK Dementia Research Institute Centre, Imperial College London, London, UK
| | - Axel Montagne
- Centre for Clinical Brain Sciences, and UK Dementia Research Institute, University of Edinburgh, Edinburgh, EH16 4SB, UK
| | - David R Owen
- Department of Brain Sciences, Imperial College London, London, UK
| | - J Scott Miners
- Dementia Research Group, University of Bristol, Bristol, UK
| | - Seth Love
- Dementia Research Group, University of Bristol, Bristol, UK
| | - Caleb Webber
- UK Dementia Research Institute Centre, Cardiff University, Cardiff, UK
| | - M Zameel Cader
- Nuffield Department of Clinical Neurosciences, Kavli Institute for Nanoscience Discovery, Dorothy Crowfoot Hodgkin Building, Sherrington Road, University of Oxford, Oxford, UK
| | - Paul M Matthews
- Department of Brain Sciences, Imperial College London, London, UK.
- UK Dementia Research Institute Centre, Imperial College London, London, UK.
- St Edmund Hall, University of Oxford, Oxford, UK.
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5
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Xia H, Luan X, Bao Z, Zhu Q, Wen C, Wang M, Song W. A multi-cohort study of the hippocampal radiomics model and its associated biological changes in Alzheimer's Disease. Transl Psychiatry 2024; 14:111. [PMID: 38395947 PMCID: PMC10891125 DOI: 10.1038/s41398-024-02836-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Revised: 02/08/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
There have been no previous reports of hippocampal radiomics features associated with biological functions in Alzheimer's Disease (AD). This study aims to develop and validate a hippocampal radiomics model from structural magnetic resonance imaging (MRI) data for identifying patients with AD, and to explore the mechanism underlying the developed radiomics model using peripheral blood gene expression. In this retrospective multi-study, a radiomics model was developed based on the radiomics discovery group (n = 420) and validated in other cohorts. The biological functions underlying the model were identified in the radiogenomic analysis group using paired MRI and peripheral blood transcriptome analyses (n = 266). Mediation analysis and external validation were applied to further validate the key module and hub genes. A 12 radiomics features-based prediction model was constructed and this model showed highly robust predictive power for identifying AD patients in the validation and other three cohorts. Using radiogenomics mapping, myeloid leukocyte and neutrophil activation were enriched, and six hub genes were identified from the key module, which showed the highest correlation with the radiomics model. The correlation between hub genes and cognitive ability was confirmed using the external validation set of the AddneuroMed dataset. Mediation analysis revealed that the hippocampal radiomics model mediated the association between blood gene expression and cognitive ability. The hippocampal radiomics model can accurately identify patients with AD, while the predictive radiomics model may be driven by neutrophil-related biological pathways.
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Affiliation(s)
- Huwei Xia
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China
| | - Xiaoqian Luan
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Zhengkai Bao
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Qinxin Zhu
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Caiyun Wen
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Meihao Wang
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China
| | - Weihong Song
- Center for Geriatric Medicine and Institute of Aging, Key Laboratory of Alzheimer's Disease of Zhejiang Province, Zhejiang Provincial Clinical Research for Mental Disorders, Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, Zhejiang, 325000, China.
- Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325035, China.
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Zhang Y, Shen S, Li X, Wang S, Xiao Z, Cheng J, Li R. A multiclass extreme gradient boosting model for evaluation of transcriptomic biomarkers in Alzheimer's disease prediction. Neurosci Lett 2024; 821:137609. [PMID: 38157927 DOI: 10.1016/j.neulet.2023.137609] [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: 08/25/2023] [Revised: 10/30/2023] [Accepted: 12/19/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Patients with young-onset Alzheimer's disease (AD) (before the age of 50 years old) often lack obvious imaging changes and amyloid protein deposition, which can lead to misdiagnosis with other cognitive impairments. Considering the association between immunological dysfunction and progression of neurodegenerative disease, recent research has focused on identifying blood transcriptomic signatures for precise prediction of AD. METHODS In this study, we extracted blood biomarkers from large-scale transcriptomics to construct multiclass eXtreme Gradient Boosting models (XGBoost), and evaluated their performance in distinguishing AD from cognitive normal (CN) and mild cognitive impairment (MCI). RESULTS Independent testing with external dataset revealed that the combination of blood transcriptomic signatures achieved an area under the receiver operating characteristic curve (AUC of ROC) of 0.81 for multiclass classification (sensitivity = 0.81; specificity = 0.63), 0.83 for classification of AD vs. CN (sensitivity = 0.72; specificity = 0.73), and 0.85 for classification of AD vs. MCI (sensitivity = 0.77; specificity = 0.73). These candidate signatures were significantly enriched in 62 chromosome regions, such as Chr.19p12-19p13.3, Chr.1p22.1-1p31.1, and Chr.1q21.2-1p23.1 (adjusted p < 0.05), and significantly overrepresented by 26 transcription factors, including E2F2, FOXO3, and GATA1 (adjusted p < 0.05). Biological analysis of these signatures pointed to systemic dysregulation of immune responses, hematopoiesis, exocytosis, and neuronal support in neurodegenerative disease (adjusted p < 0.05). CONCLUSIONS Blood transcriptomic biomarkers hold great promise in clinical use for the accurate assessment and prediction of AD.
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Affiliation(s)
- Yi Zhang
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China.
| | - Shasha Shen
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China
| | - Xiaokai Li
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China
| | - Songlin Wang
- Medical College, Panzhihua University, Panzhihua 617000, China
| | - Zongni Xiao
- Medical College, Panzhihua University, Panzhihua 617000, China
| | - Jun Cheng
- Medical College, Panzhihua University, Panzhihua 617000, China
| | - Ruifeng Li
- Institute of Neuroscience, Panzhihua University, Panzhihua 617000, China
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Xiao L, Tang Y, Deng C, Li J, Li R, Zhu H, Guo D, Yang Z, Long H, Feng L, Hu S. Differences in whole-brain metabolism are associated with the expression of genes related to neurovascular unit integrity and synaptic plasticity in temporal lobe epilepsy. Eur J Nucl Med Mol Imaging 2023; 51:168-179. [PMID: 37707571 DOI: 10.1007/s00259-023-06433-8] [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: 03/08/2023] [Accepted: 09/06/2023] [Indexed: 09/15/2023]
Abstract
PURPOSE Temporal lobe epilepsy (TLE) is a common, polygenic epilepsy syndrome that involves glucose hypometabolism in the epileptogenic zone. However, the transcriptional and cellular signatures underlying the metabolism in TLE remain unclear. METHODS In this retrospective study, 2-[18F]-fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET) scans of TLE patients (n = 104) who underwent anterior temporal lobectomy were consecutively collected between 2016 and 2021. The transcriptional profiles of TLE risk genes across the brain were identified by the gene expression analyses from six TLE patients and twelve postmortem donors (six from the Allen Human Brain Atlas). Integrating the neuroimaging and transcriptomic data, we examined the relationship between the expression of TLE-associated genes and metabolic alterations in TLE. Furthermore, we performed functional enrichment analyses of the genes with higher weight in partial least squares regression using Metascape. RESULTS A total of 104 patients with TLE (mean age 29 ± 9 years, 50% male) and 30 healthy controls (HCs) (mean age 31 ± 6 years, 53% male) were enrolled. Compared to that of HCs, patients with TLE showed hypometabolism in the temporal lobes and adjacent structures but hypermetabolism in the thalamus and basal ganglia. The cortical map of inter-group differences in cerebral metabolism was spatially correlated with the expression of a weighted combination of genes enriched in ontology terms and pathways related to neurovascular unit (NVU) integrity and synaptic plasticity. DISCUSSION Our findings, combined with the analysis of neuroimaging and transcriptional data, suggest that genes related to NVU integrity and synaptic plasticity may drive alterations to brain metabolism that mediate the genetic risk of TLE.
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Affiliation(s)
- Ling Xiao
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China
| | - Yongxiang Tang
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China
| | - Chijun Deng
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Jian Li
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China
| | - Rong Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of China, Chengdu, Sichuan, China
| | - Haoyue Zhu
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China
| | - Danni Guo
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China
| | - Zhiquan Yang
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hongyu Long
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China.
| | - Li Feng
- Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China.
- Department of Neurology, Xiangya Hospital, Central South University (Jiangxi Branch), Nanchang, Jiangxi, China.
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Shuo Hu
- Department of Nuclear Medicine, Xiangya Hospital, Central South University, 87 Xiangya Rd, Changsha, 410008, Hunan, China.
- National Clinical Research Center for Geriatric Diseases, Xiangya Hospital, Central South University, Changsha, Hunan, China.
- Key Laboratory of Biological Nanotechnology of National Health Commission, Xiangya Hospital, Central South University, Changsha, Hunan, China.
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8
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Su WM, Gu XJ, Dou M, Duan QQ, Jiang Z, Yin KF, Cai WC, Cao B, Wang Y, Chen YP. Systematic druggable genome-wide Mendelian randomisation identifies therapeutic targets for Alzheimer's disease. J Neurol Neurosurg Psychiatry 2023; 94:954-961. [PMID: 37349091 PMCID: PMC10579488 DOI: 10.1136/jnnp-2023-331142] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 06/05/2023] [Indexed: 06/24/2023]
Abstract
BACKGROUND Alzheimer's disease (AD) is the leading cause of dementia. Currently, there are no effective disease-modifying treatments for AD. Mendelian randomisation (MR) has been widely used to repurpose licensed drugs and discover novel therapeutic targets. Thus, we aimed to identify novel therapeutic targets for AD and analyse their pathophysiological mechanisms and potential side effects. METHODS A two-sample MR integrating the identified druggable genes was performed to estimate the causal effects of blood and brain druggable expression quantitative trait loci (eQTLs) on AD. A repeat study was conducted using different blood and brain eQTL data sources to validate the identified genes. Using AD markers with available genome-wide association studies data, we evaluated the causal relationship between established AD markers to explore possible mechanisms. Finally, the potential side effects of the druggable genes for AD treatment were assessed using a phenome-wide MR. RESULTS Overall, 5883 unique druggable genes were aggregated; 33 unique potential druggable genes for AD were identified in at least one dataset (brain or blood), and 5 were validated in a different dataset. Among them, three prior druggable genes (epoxide hydrolase 2 (EPHX2), SERPINB1 and SIGLEC11) reached significant levels in both blood and brain tissues. EPHX2 may mediate the pathogenesis of AD by affecting the entire hippocampal volume. Further phenome-wide MR analysis revealed no potential side effects of treatments targeting EPHX2, SERPINB1 or SIGLEC11. CONCLUSIONS This study provides genetic evidence supporting the potential therapeutic benefits of targeting the three druggable genes for AD treatment, which will be useful for prioritising AD drug development.
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Affiliation(s)
- Wei-Ming Su
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Xiao-Jing Gu
- Department of Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
| | - Meng Dou
- Chengdu Computer Application Institute, Chinese Academy of Sciences, Chengdu, China
| | - Qing-Qing Duan
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Zheng Jiang
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Kang-Fu Yin
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wei-Chen Cai
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Bei Cao
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Yi Wang
- Department of Pathophysiology, West China College of Basic medical sciences & Forensic Medicine, Sichuan University, Chengdu, China
| | - Yong-Ping Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
- Institute of Brain Science and Brain-inspired Technology, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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9
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Suh EH, Lee G, Jung SH, Wen Z, Bao J, Nho K, Huang H, Davatzikos C, Saykin AJ, Thompson PM, Shen L, Kim D. An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification. Front Aging Neurosci 2023; 15:1281748. [PMID: 37953885 PMCID: PMC10637854 DOI: 10.3389/fnagi.2023.1281748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 10/06/2023] [Indexed: 11/14/2023] Open
Abstract
Introduction Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.
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Affiliation(s)
- Erica H. Suh
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Garam Lee
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Innovative Medical Technology Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Sang-Hyuk Jung
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Zixuan Wen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Jingxuan Bao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Heng Huang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences, School of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Dokyoon Kim
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
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10
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Chen Y, Liu X, Li L, He X, Zheng F, Zhang Y, Gao H, Jin Z, Wu D, Wang Q, Tao H, Zhao Y, Liu W, Zou L. Methyltransferase-like 3 aggravates endoplasmic reticulum stress in preeclampsia by targeting TMBIM6 in YTHDF2-dependent manner. Mol Med 2023; 29:19. [PMID: 36747144 PMCID: PMC9901113 DOI: 10.1186/s10020-023-00604-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 01/06/2023] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND With the increasing morbidity and mortality of preeclampsia (PE), it has posed a huge challenge to public health. Previous studies have reported endoplasmic reticulum (ER) stress could contribute to trophoblastic dysfunction which was associated with the N6-methyladenosine (m6A) modification by methyltransferase-like 3 (METTL3), resulting in PE. However, little was known about the relationship between METTL3 and ER stress in PE. Thus, in vitro and in vivo studies were performed to clarify the mechanism about how METTL3 affects the trophoblasts under ER stress in PE and to explore a therapeutic approach for PE. METHODS An ER stress model in HTR-8/SVneo cells and a preeclamptic rat model were used to study the mechanism and explore a therapeutic approach for PE. Western blot, immunohistochemistry, quantitative reverse transcription-polymerase chain reaction (qRT-PCR), and methylated RNA immunoprecipitation (MeRIP)-qPCR were performed to detect the protein, RNA, and methylated transmembrane BAX inhibitor motif containing 6 (TMBIM6) expression levels. The m6A colorimetric and mRNA stability assays were used to measure the m6A levels and TMBIM6 stability, respectively. Short hairpin RNAs (shRNAs) were used to knockdown METTL3 and YTH N6-methyladenosine RNA binding protein 2 (YTHDF2). Flow cytometry and Transwell assays were performed to evaluate the apoptosis and invasion abilities of trophoblasts. RESULTS Upregulated METTL3 and m6A levels and downregulated TMBIM6 levels were observed in preeclamptic placentas under ER stress. The ER stress model was successfully constructed, and knockdown of METTL3 had a beneficial effect on HTR-8/SVneo cells under ER stress as it decreased the levels of methylated TMBIM6 mRNA. Moreover, overexpression of TMBIM6 was beneficial to HTR-8/SVneo cells under ER stress as it could neutralize the harmful effects of METTL3 overexpression. Similar to the knockdown of METTL3, downregulation of YTHDF2 expression resulted in the increased expression and mRNA stability of TMBIM6. Finally, improved systemic symptoms as well as protected placentas and fetuses were demonstrated in vivo. CONCLUSIONS METTL3/YTHDF2/TMBIM6 axis exerts a significant role in trophoblast dysfunction resulting in PE while inhibiting METTL3 may provide a novel therapeutic approach for PE.
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Affiliation(s)
- Yangyang Chen
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Xiaoxia Liu
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Lun Li
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Xiyang He
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Fanghui Zheng
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Yang Zhang
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Hui Gao
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Zhishan Jin
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Di Wu
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Qianhua Wang
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Hui Tao
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Yin Zhao
- grid.33199.310000 0004 0368 7223Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Weifang Liu
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Li Zou
- Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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11
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Ji C, Yang Y, Fu Y, Pu X, Xu G. Improvement of Ganoderma lucidum water extract on the learning and memory impairment and its mechanism in d-galactose-induced aging mice. J Funct Foods 2022. [DOI: 10.1016/j.jff.2022.105322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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12
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Pasqualetti G, Thayanandan T, Edison P. Influence of genetic and cardiometabolic risk factors in Alzheimer's disease. Ageing Res Rev 2022; 81:101723. [PMID: 36038112 DOI: 10.1016/j.arr.2022.101723] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 08/18/2022] [Accepted: 08/21/2022] [Indexed: 01/31/2023]
Abstract
Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder. Cardiometabolic and genetic risk factors play an important role in the trajectory of AD. Cardiometabolic risk factors including diabetes, mid-life obesity, mid-life hypertension and elevated cholesterol have been linked with cognitive decline in AD subjects. These potential risk factors associated with cerebral metabolic changes which fuel AD pathogenesis have been suggested to be the reason for the disappointing clinical trial results. In appreciation of the risks involved, using search engines such as PubMed, Scopus, MEDLINE and Google Scholar, a relevant literature search on cardiometabolic and genetic risk factors in AD was conducted. We discuss the role of genetic as well as established cardiovascular risk factors in the neuropathology of AD. Moreover, we show new evidence of genetic interaction between several genes potentially involved in different pathways related to both neurodegenerative process and cardiovascular damage.
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Affiliation(s)
| | - Tony Thayanandan
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, UK
| | - Paul Edison
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, UK; School of Medicine, Cardiff University, UK.
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13
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Kim B, Vasanthakumar A, Li QS, Nudelman KN, Risacher SL, Davis JW, Idler K, Lee J, Seo SW, Waring JF, Saykin AJ, Nho K. Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2022; 14:e12354. [PMID: 36187194 PMCID: PMC9489162 DOI: 10.1002/dad2.12354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 08/01/2022] [Accepted: 08/01/2022] [Indexed: 11/17/2022]
Abstract
Introduction The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genesinvolved in biological aging in AD. Methods We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers. Results WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers. Conclusion Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD. Highlights Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes.Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging.Among the hub genes of the module, CX3CR1 was downregulated in AD.The CX3CR1 expression level was associated with cognitive performance and brain atrophy.
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Affiliation(s)
- Bo‐Hyun Kim
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Samsung Alzheimer Research CenterSamsung Medical CenterSeoulRepublic of Korea
- Department of Health Sciences and TechnologySHAISTSungkyunkwan UniversitySeoulRepublic of Korea
| | | | - Qingqin S. Li
- Neuroscience Therapeutic AreaJanssen Research & Development, LLCTitusvilleNew JerseyUSA
| | - Kelly N.H. Nudelman
- National Centralized Repository for Alzheimer's Disease and Related DementiasIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Shannon L. Risacher
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
| | | | - Kenneth Idler
- Genomics Research CenterAbbVieNorth ChicagoIllinoisUSA
| | - Jong‐Min Lee
- Department of Biomedical EngineeringHanyang UniversitySeoulRepublic of Korea
| | - Sang Won Seo
- Samsung Alzheimer Research CenterSamsung Medical CenterSeoulRepublic of Korea
- Department of NeurologySamsung Medical CenterSungkyunkwan University School of MedicineSeoulRepublic of Korea
- Department of Health Sciences and TechnologySHAISTSungkyunkwan UniversitySeoulRepublic of Korea
| | | | - Andrew J. Saykin
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Kwangsik Nho
- Center for NeuroimagingDepartment of Radiology and Imaging SciencesIndiana University School of MedicineIndianapolisIndianaUSA
- Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
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14
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Donaghy PC, Cockell SJ, Martin-Ruiz C, Coxhead J, Kane J, Erskine D, Koss D, Taylor JP, Morris CM, O'Brien JT, Thomas AJ. Blood mRNA Expression in Alzheimer's Disease and Dementia With Lewy Bodies. Am J Geriatr Psychiatry 2022; 30:964-975. [PMID: 35283023 DOI: 10.1016/j.jagp.2022.02.003] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 02/01/2022] [Accepted: 02/03/2022] [Indexed: 11/19/2022]
Abstract
OBJECTIVES The objective of this study was to investigate the expression of genes in Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), both at the mild cognitive impairment (MCI) and dementia stages, to improve our understanding of disease pathophysiology and investigate the potential for diagnostic and prognostic biomarkers based on mRNA expression. DESIGN Cross-sectional observational study. SETTING University research center. PARTICIPANTS People with MCI with Lewy bodies (MCI-LB, n=55), MCI-AD (n=19), DLB (n=38), AD (n=24) and a cognitively unimpaired comparison group (n=28). MEASUREMENTS Ribonucleic acid sequencing of whole blood. Differentially expressed genes (DEGs) were identified and gene set enrichment analysis was carried out. RESULTS Compared with the cognitively unimpaired group, there were 22 DEGs in MCI-LB/DLB and 61 DEGs in MCI-AD/AD. DEGS were also identified when comparing the two disease groups. Expression of ANP32A was associated with more rapid cognitive decline in MCI-AD/AD. Gene set enrichment analysis identified downregulation in gene sets including MYC targets and oxidative phosphorylation in MCI-LB/DLB; upregulation of immune and inflammatory responses in MCI-AD/AD; and upregulation of interferon-α and -γ responses in MCI-AD/AD compared with MCI-LB/DLB. CONCLUSION This study identified multiple DEGs in MCI-LB/DLB and MCI-AD/AD. One of these DEGs, ANP32A, may be a prognostic marker in AD. Genes related to mitochondrial function were downregulated in MCI-LB/DLB. Previously reported upregulation of genes associated with inflammation and immune responses in MCI-AD/AD was confirmed in this cohort. Differences in interferon responses between MCI-AD/AD and MCI-LB/DLB suggest that there are key differences in peripheral immune responses between these diseases.
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Affiliation(s)
- Paul C Donaghy
- Translational and Clinical Research Institute (PCD, DE, DK, JPT, CMM, AJT), Newcastle University, Newcastle upon Tyne, United Kingdom.
| | - Simon J Cockell
- School of Biomedical, Nutrition and Sports Sciences (SJC), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Carmen Martin-Ruiz
- Biosciences Institute (CMR, JC), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jonathan Coxhead
- Biosciences Institute (CMR, JC), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Joseph Kane
- Centre for Public Health (JK), Queen's University Belfast, Belfast, United Kingdom
| | - Daniel Erskine
- Translational and Clinical Research Institute (PCD, DE, DK, JPT, CMM, AJT), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - David Koss
- Translational and Clinical Research Institute (PCD, DE, DK, JPT, CMM, AJT), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - John-Paul Taylor
- Translational and Clinical Research Institute (PCD, DE, DK, JPT, CMM, AJT), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Christopher M Morris
- Translational and Clinical Research Institute (PCD, DE, DK, JPT, CMM, AJT), Newcastle University, Newcastle upon Tyne, United Kingdom
| | - John T O'Brien
- Department of Psychiatry (JTO), University of Cambridge, Cambridge, United Kingdom
| | - Alan J Thomas
- Translational and Clinical Research Institute (PCD, DE, DK, JPT, CMM, AJT), Newcastle University, Newcastle upon Tyne, United Kingdom
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15
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Xu X, Wang H, Bennett DA, Zhang QY, Wang G, Zhang HY. Systems Genetic Identification of Mitochondrion-Associated Alzheimer's Disease Genes and Implications for Disease Risk Prediction. Biomedicines 2022; 10:1782. [PMID: 35892682 PMCID: PMC9330299 DOI: 10.3390/biomedicines10081782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 07/21/2022] [Accepted: 07/22/2022] [Indexed: 11/28/2022] Open
Abstract
Cumulative evidence has revealed the association between mitochondrial dysfunction and Alzheimer’s disease (AD). Because the number of mitochondrial genes is very limited, the mitochondrial pathogenesis of AD must involve certain nuclear genes. In this study, we employed systems genetic methods to identify mitochondrion-associated nuclear genes that may participate in the pathogenesis of AD. First, we performed a mitochondrial genome-wide association study (MiWAS, n = 809) to identify mitochondrial single-nucleotide polymorphisms (MT-SNPs) associated with AD. Then, epistasis analysis was performed to examine interacting SNPs between the mitochondrial and nuclear genomes. Weighted co-expression network analysis (WGCNA) was applied to transcriptomic data from the same sample (n = 743) to identify AD-related gene modules, which were further enriched by mitochondrion-associated genes. Using hub genes derived from these modules, random forest models were constructed to predict AD risk in four independent datasets (n = 743, n = 542, n = 161, and n = 540). In total, 9 potentially significant MT-SNPs and 14,340 nominally significant MT-nuclear interactive SNPs were identified for AD, which were validated by functional analysis. A total of 6 mitochondrion-related modules involved in AD pathogenesis were found by WGCNA, from which 91 hub genes were screened and used to build AD risk prediction models. For the four independent datasets, these models perform better than those derived from AD genes identified by genome-wide association studies (GWASs) or differential expression analysis (DeLong’s test, p < 0.05). Overall, through systems genetics analyses, mitochondrion-associated SNPs/genes with potential roles in AD pathogenesis were identified and preliminarily validated, illustrating the power of mitochondrial genetics in AD pathogenesis elucidation and risk prediction.
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Affiliation(s)
- Xuan Xu
- Hubei Key Laboratory of Agricultural Bioinformaics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; (X.X.); (Q.-Y.Z.)
| | - Hui Wang
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
- Penn Neurodegeneration Genomics Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA;
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Qing-Ye Zhang
- Hubei Key Laboratory of Agricultural Bioinformaics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; (X.X.); (Q.-Y.Z.)
| | - Gang Wang
- Hubei Key Laboratory of Central Nervous System Tumor and Intervention, Wuhan 430070, China;
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformaics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; (X.X.); (Q.-Y.Z.)
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16
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A large-scale genome-wide cross-trait analysis reveals shared genetic architecture between Alzheimer's disease and gastrointestinal tract disorders. Commun Biol 2022; 5:691. [PMID: 35851147 PMCID: PMC9293965 DOI: 10.1038/s42003-022-03607-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 06/20/2022] [Indexed: 12/16/2022] Open
Abstract
Consistent with the concept of the gut-brain phenomenon, observational studies suggest a relationship between Alzheimer's disease (AD) and gastrointestinal tract (GIT) disorders; however, their underlying mechanisms remain unclear. Here, we analyse several genome-wide association studies (GWAS) summary statistics (N = 34,652-456,327), to assess the relationship of AD with GIT disorders. Findings reveal a positive significant genetic overlap and correlation between AD and gastroesophageal reflux disease (GERD), peptic ulcer disease (PUD), gastritis-duodenitis, irritable bowel syndrome and diverticulosis, but not inflammatory bowel disease. Cross-trait meta-analysis identifies several loci (Pmeta-analysis < 5 × 10-8) shared by AD and GIT disorders (GERD and PUD) including PDE4B, BRINP3, ATG16L1, SEMA3F, HLA-DRA, SCARA3, MTSS2, PHB, and TOMM40. Colocalization and gene-based analyses reinforce these loci. Pathway-based analyses demonstrate significant enrichment of lipid metabolism, autoimmunity, lipase inhibitors, PD-1 signalling, and statin mechanisms, among others, for AD and GIT traits. Our findings provide genetic insights into the gut-brain relationship, implicating shared but non-causal genetic susceptibility of GIT disorders with AD's risk. Genes and biological pathways identified are potential targets for further investigation in AD, GIT disorders, and their comorbidity.
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17
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Xie J, Zhong C, Wang T, He D, Lu L, Yang J, Yuan Z, Zhang J. Better Bioactivity, Cerebral Metabolism and Pharmacokinetics of Natural Medicine and Its Advanced Version. Front Pharmacol 2022; 13:937075. [PMID: 35833035 PMCID: PMC9271619 DOI: 10.3389/fphar.2022.937075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Currently, many people are afflicted by cerebral diseases that cause dysfunction in the brain and perturb normal daily life of people. Cerebral diseases are greatly affected by cerebral metabolism, including the anabolism and catabolism of neurotransmitters, hormones, neurotrophic molecules and other brain-specific chemicals. Natural medicines (NMs) have the advantages of low cost and low toxicity. NMs are potential treatments for cerebral diseases due to their ability to regulate cerebral metabolism. However, most NMs have low bioavailability due to their low solubility/permeability. The study is to summarize the better bioactivity, cerebral metabolism and pharmacokinetics of NMs and its advanced version. This study sums up research articles on the NMs to treat brain diseases. NMs affect cerebral metabolism and the related mechanisms are revealed. Nanotechnologies are applied to deliver NMs. Appropriate delivery systems (exosomes, nanoparticles, liposomes, lipid polymer hybrid nanoparticles, nanoemulsions, protein conjugation and nanosuspensions, etc.) provide better pharmacological and pharmacokinetic characteristics of NMs. The structure-based metabolic reactions and enzyme-modulated catalytic reactions related to advanced versions of NMs alter the pharmacological activities of NMs.
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Affiliation(s)
- Jiaxi Xie
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Cailing Zhong
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Tingting Wang
- Biochemistry and Molecular Biology Laboratory, Experimental Teaching and Management Center, Chongqing Medical University, Chongqing, China
| | - Dan He
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Luyang Lu
- College of Pharmacy, Southwest Minzu University, Chengdu, China
| | - Jie Yang
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Ziyi Yuan
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing, China
| | - Jingqing Zhang
- Chongqing Research Center for Pharmaceutical Engineering, College of Pharmacy, Chongqing Medical University, Chongqing, China
- *Correspondence: Jingqing Zhang,
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18
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Baik JY, Kim M, Bao J, Long Q, Shen L. Identifying Alzheimer's genes via brain transcriptome mapping. BMC Med Genomics 2022; 15:116. [PMID: 35590321 PMCID: PMC9118564 DOI: 10.1186/s12920-022-01260-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 05/04/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is one of the most common neurodegenerative disorders characterized by progressive decline in cognitive function. Targeted genetic analyses, genome-wide association studies, and imaging genetic analyses have been performed to detect AD risk and protective genes and have successfully identified dozens of AD susceptibility loci. Recently, brain imaging transcriptomics analyses have also been conducted to investigate the relationship between neuroimaging traits and gene expression measures to identify interesting gene-traits associations. These imaging transcriptomic studies typically do not involve the disease outcome in the analysis, and thus the identified brain or transcriptomic markers may not be related or specific to the disease outcome. RESULTS We propose an innovative two-stage approach to identify genes whose expression profiles are related to diagnosis phenotype via brain transcriptome mapping. Specifically, we first map the effects of a diagnosis phenotype onto imaging traits across the brain using a linear regression model. Then, the gene-diagnosis association is assessed by spatially correlating the brain transcriptome map with the diagnostic effect map on the brain-wide imaging traits. To demonstrate the promise of our approach, we apply it to the integrative analysis of the brain transcriptome data from the Allen Human Brain Atlas (AHBA) and the amyloid imaging data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our method identifies 12 genes whose brain-wide transcriptome patterns are highly correlated with six different diagnostic effect maps on the amyloid imaging traits. These 12 genes include four confirmatory findings (i.e., AD genes reported in DisGeNET) and eight novel genes that have not be associated with AD in DisGeNET. CONCLUSION We have proposed a novel disease-related brain transcriptomic mapping method to identify genes whose expression profiles spatially correlated with regional diagnostic effects on a studied brain trait. Our empirical study on the AHBA and ADNI data shows the promise of the approach, and the resulting AD gene discoveries provide valuable information for better understanding biological pathways from transcriptomic signatures to intermediate brain traits and to phenotypic disease outcomes.
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Affiliation(s)
- Jae Young Baik
- grid.25879.310000 0004 1936 8972School of Arts and Sciences, University of Pennsylvania, Philadelphia, USA
| | - Mansu Kim
- grid.411947.e0000 0004 0470 4224Department of Artificial intelligence, Catholic University of Korea, Bucheon, Republic of Korea
| | - Jingxuan Bao
- grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Qi Long
- grid.25879.310000 0004 1936 8972Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, USA.
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19
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Cha HJ, Shen J, Kang J. Regulation of gene expression by the APP family in the adult cerebral cortex. Sci Rep 2022; 12:66. [PMID: 34997052 PMCID: PMC8741778 DOI: 10.1038/s41598-021-04027-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 12/14/2021] [Indexed: 12/13/2022] Open
Abstract
Amyloid precursor protein (APP) is associated with both familial and sporadic forms of Alzheimer's disease. APP has two homologs, amyloid precursor-like protein 1 and 2 (APLP1 and APLP2), and they have functional redundancy. APP intracellular c-terminal domain (AICD), produced by sequential α- or β- and γ-secretase cleavages, is thought to control gene expression, similarly as the ICD of Notch. To investigate the role of APP family in transcriptional regulation, we examined gene expression changes in the cerebral cortex of APP/APLP1/APLP2 conditional triple knockout (cTKO) mice, in which APP family members are selectively inactivated in excitatory neurons of the postnatal forebrain. Of the 12 previously reported AICD target genes, only Nep and Npas4 mRNA levels were significantly reduced in the cerebral cortex of cTKO mice, compared to littermate controls. We further examined global transcriptional changes by RNA-seq and identified 189 and 274 differentially expressed genes in the neocortex and hippocampus, respectively, of cTKO mice relative to controls. Gene Ontology analysis indicated that these genes are involved in a variety of cellular functions, including extracellular organization, learning and memory, and ion channels. Thus, inactivation of APP family alters transcriptional profiles of the cerebral cortex and affects wide-ranging molecular pathways.
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Affiliation(s)
- Hye Ji Cha
- Division of Hematology/Oncology, Boston Children's Hospital and Department of Pediatric Oncology, Dana-Farber Cancer Institute (DFCI), Harvard Stem Cell Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Jie Shen
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Program in Neuroscience, Harvard Medical School, Boston, MA, 02115, USA
| | - Jongkyun Kang
- Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
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20
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Weber CJ, Carrillo MC, Jagust W, Jack CR, Shaw LM, Trojanowski JQ, Saykin AJ, Beckett LA, Sur C, Rao NP, Mendez PC, Black SE, Li K, Iwatsubo T, Chang C, Sosa AL, Rowe CC, Perrin RJ, Morris JC, Healan AM, Hall SE, Weiner MW. The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI-3 updates and global perspectives. ALZHEIMER'S & DEMENTIA (NEW YORK, N. Y.) 2021; 7:e12226. [PMID: 35005206 PMCID: PMC8719344 DOI: 10.1002/trc2.12226] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 11/05/2021] [Indexed: 11/06/2022]
Abstract
The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW-ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public-private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI-1, ADNI-GO, ADNI-2, and ADNI-3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW-ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW-ADNI researchers who shared updates from ADNI-3 and their vision for ADNI-4.
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Affiliation(s)
| | | | - William Jagust
- School of Public Health and Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | | | - Leslie M. Shaw
- Department of Pathology and Laboratory MedicinePerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory MedicinePerelman School of MedicineInstitute on AgingPerelman School of MedicineAlzheimer's Disease Core Center, Perelman School of MedicineUdall Parkinson's Research CenterPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterDepartment of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Cyrille Sur
- Merck Research LaboratoriesMerckKenilworthNew JerseyUSA
| | - Naren P. Rao
- Department of PsychiatryNational Institute of Mental Health and NeurosciencesBengaluruKarnatakaIndia
| | | | - Sandra E. Black
- Department of Medicine (Neurology)Hurvitz Brain Sciences ProgramCanadian Partnership for Stroke Recovery, and LC Campbell Cognitive Neurology Research UnitHurvitz Brain Sciences Research ProgramSunnybrook Research InstituteSunnybrook Health Sciences CentreUniversity of TorontoTorontoCanada
| | - Kuncheng Li
- Department of RadiologyXuanwu Hospital of Capital Medical UniversityBeijingChina
| | - Takeshi Iwatsubo
- Department of NeuropathologyGraduate School of MedicineThe University of TokyoTokyoJapan
| | - Chiung‐Chih Chang
- Department of General Neurology and Institute for Translational Research in BiomedicineKaohsiung Chang Gung Memorial HospitalChang Gung University College of MedicineKaohsiungTaiwan
| | - Ana Luisa Sosa
- National Institute of Neurology and Neurosurgery of MexicoMexico CityMexico
| | - Christopher C. Rowe
- Department of Molecular Imaging and TherapyAustin Health and Florey Department of Neuroscience and Mental HealthUniversity of MelbourneMelbourneVictoriaAustralia
| | - Richard J. Perrin
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of Pathology and ImmunologyDepartment of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
| | - John C. Morris
- Charles F. and Joanne Knight Alzheimer Disease Research CenterDepartment of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | | | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesDepartment of RadiologyDepartment of MedicineDepartment of PsychiatryDepartment of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
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21
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Song W, Wang W, Liu Z, Cai W, Yu S, Zhao M, Lin GN. A Comprehensive Evaluation of Cross-Omics Blood-Based Biomarkers for Neuropsychiatric Disorders. J Pers Med 2021; 11:1247. [PMID: 34945719 PMCID: PMC8703948 DOI: 10.3390/jpm11121247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 12/03/2022] Open
Abstract
The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.
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Affiliation(s)
- Weichen Song
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Weidi Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Zhe Liu
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Wenxiang Cai
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
| | - Shunying Yu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, China; (S.Y.); (M.Z.)
| | - Min Zhao
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, China; (S.Y.); (M.Z.)
| | - Guan Ning Lin
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; (W.S.); (W.W.); (Z.L.); (W.C.)
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22
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Lee T, Lee H. Identification of Disease-Related Genes That Are Common between Alzheimer's and Cardiovascular Disease Using Blood Genome-Wide Transcriptome Analysis. Biomedicines 2021; 9:biomedicines9111525. [PMID: 34829754 PMCID: PMC8614900 DOI: 10.3390/biomedicines9111525] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/15/2021] [Accepted: 10/18/2021] [Indexed: 01/09/2023] Open
Abstract
Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.
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Affiliation(s)
- Taesic Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- Department of Family Medicine, Yonsei University Wonju College of Medicine, Wonju 26426, Korea
| | - Hyunju Lee
- Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju 61005, Korea;
- School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea
- Correspondence: ; Tel.: +82-62-715-2213
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23
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Liszewski MK, Atkinson JP. Membrane cofactor protein (MCP; CD46): deficiency states and pathogen connections. Curr Opin Immunol 2021; 72:126-134. [PMID: 34004375 PMCID: PMC8123722 DOI: 10.1016/j.coi.2021.04.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 04/14/2021] [Accepted: 04/14/2021] [Indexed: 02/07/2023]
Abstract
Membrane cofactor protein (MCP; CD46), a ubiquitously expressed complement regulatory protein, serves as a cofactor for serine protease factor I to cleave and inactivate C3b and C4b deposited on host cells. However, CD46 also plays roles in human reproduction, autophagy, modulating T cell activation and effector functions and is a member of the newly identified intracellular complement system (complosome). CD46 also is a receptor for 11 pathogens ('pathogen magnet'). While CD46 deficiencies contribute to inflammatory disorders, its overexpression in cancers and role as a receptor for some adenoviruses has led to its targeting by oncolytic agents and adenoviral-based therapeutic vectors, including coronavirus disease of 2019 (COVID-19) vaccines. This review focuses on recent advances in identifying disease-causing CD46 variants and its pathogen connections.
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Affiliation(s)
- M Kathryn Liszewski
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
| | - John P Atkinson
- Division of Rheumatology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, 63110, USA.
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24
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Patel D, Zhang X, Farrell JJ, Lunetta KL, Farrer LA. Set-Based Rare Variant Expression Quantitative Trait Loci in Blood and Brain from Alzheimer Disease Study Participants. Genes (Basel) 2021; 12:419. [PMID: 33804025 PMCID: PMC7999141 DOI: 10.3390/genes12030419] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 03/04/2021] [Accepted: 03/10/2021] [Indexed: 12/12/2022] Open
Abstract
Because studies of rare variant effects on gene expression have limited power, we investigated set-based methods to identify rare expression quantitative trait loci (eQTL) related to Alzheimer disease (AD). Gene-level and pathway-level cis rare-eQTL mapping was performed genome-wide using gene expression data derived from blood donated by 713 Alzheimer's Disease Neuroimaging Initiative participants and from brain tissues donated by 475 Religious Orders Study/Memory and Aging Project participants. The association of gene or pathway expression with a set of all cis potentially regulatory low-frequency and rare variants within 1 Mb of genes was evaluated using SKAT-O. A total of 65 genes expressed in the brain were significant targets for rare expression single nucleotide polymorphisms (eSNPs) among which 17% (11/65) included established AD genes HLA-DRB1 and HLA-DRB5. In the blood, 307 genes were significant targets for rare eSNPs. In the blood and the brain, GNMT, LDHC, RBPMS2, DUS2, and HP were targets for significant eSNPs. Pathway enrichment analysis revealed significant pathways in the brain (n = 9) and blood (n = 16). Pathways for apoptosis signaling, cholecystokinin receptor (CCKR) signaling, and inflammation mediated by chemokine and cytokine signaling were common to both tissues. Significant rare eQTLs in inflammation pathways included five genes in the blood (ALOX5AP, CXCR2, FPR2, GRB2, IFNAR1) that were previously linked to AD. This study identified several significant gene- and pathway-level rare eQTLs, which further confirmed the importance of the immune system and inflammation in AD and highlighted the advantages of using a set-based eQTL approach for evaluating the effect of low-frequency and rare variants on gene expression.
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Affiliation(s)
- Devanshi Patel
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA;
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - Xiaoling Zhang
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - John J. Farrell
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
| | - Kathryn L. Lunetta
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA;
| | - Lindsay A. Farrer
- Bioinformatics Graduate Program, Boston University, Boston, MA 02215, USA;
- Department of Medicine (Biomedical Genetics), Boston University School of Medicine, Boston, MA 02118, USA; (X.Z.); (J.J.F.)
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02118, USA;
- Department of Ophthalmology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Neurology, Boston University School of Medicine, Boston, MA 02118, USA
- Department of Epidemiology, Boston University School of Public Health, Boston, MA 02118, USA
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