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Jiang D, Nan H, Chen Z, Zou WQ, Wu L. Genetic insights into drug targets for sporadic Creutzfeldt-Jakob disease: Integrative multi-omics analysis. Neurobiol Dis 2024; 199:106599. [PMID: 38996988 DOI: 10.1016/j.nbd.2024.106599] [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: 04/18/2024] [Revised: 07/07/2024] [Accepted: 07/08/2024] [Indexed: 07/14/2024] Open
Abstract
OBJECTIVE Sporadic Creutzfeldt-Jakob disease (sCJD) is a fatal rapidly progressive neurodegenerative disorder with no effective therapeutic interventions. We aimed to identify potential genetically-supported drug targets for sCJD by integrating multi-omics data. METHODS Multi-omics-wide association studies, Mendelian randomization, and colocalization analyses were employed to explore potential therapeutic targets using expression, single-cell expression, DNA methylation, and protein quantitative trait locus data from blood and brain tissues. Outcome data was from a case-control genome-wide association study, which included 4110 sCJD patients and 13,569 controls. Further investigations encompassed druggability, potential side effects, and associated biological pathways of the identified targets. RESULTS Integrative multi-omics analysis identified 23 potential therapeutic targets for sCJD, with five targets (STX6, XYLT2, PDIA4, FUCA2, KIAA1614) having higher levels of evidence. One target (XYLT2) shows promise for repurposing, two targets (XYLT2, PDIA4) are druggable, and three (STX6, KIAA1614, and FUCA2) targets represent potential future breakthrough points. The expression level of STX6 and XYLT2 in neurons and oligodendrocytes was closely associated with an increased risk of sCJD. Brain regions with high expression of STX6 or causal links to sCJD were often those areas commonly affected by sCJD. CONCLUSIONS Our study identified five potential therapeutic targets for sCJD. Further investigations are warranted to elucidate the mechanisms underlying the new targets for developing disease therapies or initiate clinical trials.
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Affiliation(s)
- Deming Jiang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Haitian Nan
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Zhongyun Chen
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Wen-Quan Zou
- Institute of Neurology, Jiangxi Academy of Clinical Medical Sciences, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, China
| | - Liyong Wu
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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2
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Frick EA, Emilsson V, Jonmundsson T, Steindorsdottir AE, Johnson ECB, Puerta R, Dammer EB, Shantaraman A, Cano A, Boada M, Valero S, García-González P, Gudmundsson EF, Gudjonsson A, Pitts R, Qiu X, Finkel N, Loureiro JJ, Orth AP, Seyfried NT, Levey AI, Ruiz A, Aspelund T, Jennings LL, Launer LJ, Gudmundsdottir V, Gudnason V. Serum proteomics reveal APOE-ε4-dependent and APOE-ε4-independent protein signatures in Alzheimer's disease. NATURE AGING 2024:10.1038/s43587-024-00693-1. [PMID: 39169269 DOI: 10.1038/s43587-024-00693-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 07/22/2024] [Indexed: 08/23/2024]
Abstract
A deeper understanding of the molecular processes underlying late-onset Alzheimer's disease (LOAD) could aid in biomarker and drug target discovery. Using high-throughput serum proteomics in the prospective population-based Age, Gene/Environment Susceptibility-Reykjavik Study (AGES) cohort of 5,127 older Icelandic adults (mean age, 76.6 ± 5.6 years), we identified 303 proteins associated with incident LOAD over a median follow-up of 12.8 years. Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status, were implicated in neuronal processes and overlapped with LOAD protein signatures in brain and cerebrospinal fluid. We identified 17 proteins whose associations with LOAD were strongly dependent on APOE-ε4 carrier status, with mostly consistent associations in cerebrospinal fluid. Remarkably, four of these proteins (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated due to LOAD, a finding replicated in external cohorts and possibly reflecting a response to disease onset. These findings highlight dysregulated pathways at the preclinical stages of LOAD, including those both independent of and dependent on APOE-ε4 status.
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Affiliation(s)
| | - Valur Emilsson
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | | | - Erik C B Johnson
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Raquel Puerta
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain
| | - Eric B Dammer
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Anantharaman Shantaraman
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Amanda Cano
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Mercè Boada
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Sergi Valero
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Pablo García-González
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | | | | | | | | | | | | | | | - Nicholas T Seyfried
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Allan I Levey
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Agustin Ruiz
- Research Center and Memory Clinic. Ace Alzheimer Center Barcelona - Universitat Internacional de Catalunya, Barcelona, Barcelona, Spain
- CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain
| | - Thor Aspelund
- Icelandic Heart Association, Kopavogur, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | | | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, National Institute on Aging, Bethesda, MD, USA
| | - Valborg Gudmundsdottir
- Icelandic Heart Association, Kopavogur, Iceland.
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
| | - Vilmundur Gudnason
- Icelandic Heart Association, Kopavogur, Iceland.
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland.
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3
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Chen L, Yang G, Qu F. Aptamer-based sensors for fluid biopsies of protein disease markers. Talanta 2024; 276:126246. [PMID: 38796994 DOI: 10.1016/j.talanta.2024.126246] [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: 02/18/2024] [Revised: 05/08/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024]
Abstract
Fluid biopsy technology, characterized by its minimally invasive nature, speed, and continuity, has become a rapidly advancing and widely applied real-time diagnostic technique. Among various biomarkers, proteins represent the most abundant class of disease indicators. The sensitive and accurate detection of protein markers in bodily fluids is significantly influenced by the control exerted by recognition ligands. Aptamers, which are structurally dynamic functional oligonucleotides, exhibit high affinity, specific recognition of targets, and notable characteristics of high editability and modularity. These features make aptamer universal "recognition-capture" components, contribute to a significant leap in their applications within the biosensor domain. In this context, we provide a comprehensive review of the extensive application of aptamer-based biosensors in fluid biopsy. We systematically compile the characteristics and construction strategies of aptamer-based biosensors tailored for fluid biopsy, including aptamer sequences, affinity (KD), fluid background, sensing technologies, sensor construction strategies, incubation time, detection performance, and influencing factors. Furthermore, a comparative analysis of their advantages and disadvantages was conducted. In conclusion, we delineate and deliberate on prospective research trajectories and challenges that lie ahead in the realm of aptamer-based biosensors for fluid biopsy.
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Affiliation(s)
- Li Chen
- School of Life Science, Key Laboratory of Molecular Medicine and Biotherapy, Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Beijing Institute of Technology, Beijing, 100081, China
| | - Ge Yang
- CAMS Key Laboratory of Antiviral Drug Research, Beijing Key Laboratory of Antimicrobial Agents, NHC Key Laboratory of Biotechnology of Antibiotics, Institute of Medicinal Biotechnology, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China.
| | - Feng Qu
- School of Life Science, Key Laboratory of Molecular Medicine and Biotherapy, Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering, Beijing Institute of Technology, Beijing, 100081, China.
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4
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Duggan MR, Gomez GT, Joynes CM, Bilgel M, Chen J, Fattorelli N, Hohman TJ, Mancuso R, Cordon J, Castellano T, Koran MEI, Candia J, Lewis A, Moghekar A, Ashton NJ, Kac PR, Karikari TK, Blennow K, Zetterberg H, Martinez-Muriana A, De Strooper B, Thambisetty M, Ferrucci L, Gottesman RF, Coresh J, Resnick SM, Walker KA. Proteome-wide analysis identifies plasma immune regulators of amyloid-beta progression. Brain Behav Immun 2024; 120:604-619. [PMID: 38977137 DOI: 10.1016/j.bbi.2024.07.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 06/07/2024] [Accepted: 07/04/2024] [Indexed: 07/10/2024] Open
Abstract
While immune function is known to play a mechanistic role in Alzheimer's disease (AD), whether immune proteins in peripheral circulation influence the rate of amyloid-β (Aβ) progression - a central feature of AD - remains unknown. In the Baltimore Longitudinal Study of Aging, we quantified 942 immunological proteins in plasma and identified 32 (including CAT [catalase], CD36 [CD36 antigen], and KRT19 [keratin 19]) associated with rates of cortical Aβ accumulation measured with positron emission tomography (PET). Longitudinal changes in a subset of candidate proteins also predicted Aβ progression, and the mid- to late-life (20-year) trajectory of one protein, CAT, was associated with late-life Aβ-positive status in the Atherosclerosis Risk in Communities (ARIC) study. Genetic variation that influenced plasma levels of CAT, CD36 and KRT19 predicted rates of Aβ accumulation, including causal relationships with Aβ PET levels identified with two-sample Mendelian randomization. In addition to associations with tau PET and plasma AD biomarker changes, as well as expression patterns in human microglia subtypes and neurovascular cells in AD brain tissue, we showed that 31 % of candidate proteins were related to mid-life (20-year) or late-life (8-year) dementia risk in ARIC. Our findings reveal plasma proteins associated with longitudinal Aβ accumulation, and identify specific peripheral immune mediators that may contribute to the progression of AD pathophysiology.
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Affiliation(s)
- Michael R Duggan
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Gabriela T Gomez
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Cassandra M Joynes
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Jingsha Chen
- Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Nicola Fattorelli
- VIB Center for Brain and Disease Research, Flanders Institute for Biotechnology, Leuven, Belgium; Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Timothy J Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Renzo Mancuso
- Microglia and Inflammation in Neurological Disorders Laboratory, Center for Molecular Neurology, Flanders Institute for Biotechnology, Antwerp, Belgium; Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Jenifer Cordon
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Tonnar Castellano
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Mary Ellen I Koran
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Julián Candia
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Alexandria Lewis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Abhay Moghekar
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Maurice Wohl Clinical Neuroscience Institute, King's College London, London, UK; NIHR Biomedical Research Center for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK; Center for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Przemysław R Kac
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; ICM Institute, Pitié-Salpêtrière University Hospital, Sorbonne University, Paris, France; First Affiliated Hospital, University of Science and Technology of China, Anhui, PR China
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, University College London Institute of Neurology, London, UK; UK Dementia Research Institute, University College London, London, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong Special Administrative Region; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Anna Martinez-Muriana
- VIB Center for Brain and Disease Research, Flanders Institute for Biotechnology, Leuven, Belgium; Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Bart De Strooper
- VIB Center for Brain and Disease Research, Flanders Institute for Biotechnology, Leuven, Belgium; Department of Neurosciences and Leuven Brain Institute, KU Leuven, Leuven, Belgium; UK Dementia Research Institute, University College London, London, UK
| | - Madhav Thambisetty
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Luigi Ferrucci
- Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Rebecca F Gottesman
- Stroke Branch, National Institute of Neurological Disorders and Stroke, Bethesda, MD, USA
| | - Josef Coresh
- Departments of Population Health and Medicine, New York University Grossman School of Medicine, New York, NY, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA.
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5
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Wang SK, Wang QJ, Zhao X, Wang P, Li XY, Wang W, Lu SB. Therapeutic targets for muscle weakness in older adults: proteome-wide Mendelian randomization and colocalization analyses. J Nutr Health Aging 2024; 28:100325. [PMID: 39083861 DOI: 10.1016/j.jnha.2024.100325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 07/20/2024] [Accepted: 07/22/2024] [Indexed: 08/02/2024]
Abstract
BACKGROUND Recent research highlights the importance of muscular strength as a key factor in physical fitness, a strong indicator of overall mortality risk, and a vital target for preventing chronic diseases. This study used a proteome-wide Mendelian randomization analysis plus colocalization analysis for low hand grip strength to explore potential therapeutic targets for muscle weakness. METHODS We conducted two two-sample Mendelian randomization analyses from four cohorts to identify and validate the causal relationship between plasma proteins and low grip strength. We also employed bidirectional Mendelian randomization analysis with Steiger filtering, Bayesian co-localization, and phenotype scanning to detect reverse causality, thereby consolidating our Mendelian randomization findings. Downstream analyses were also undertaken of identified proteins, including knockout models, enrichment analyses, and protein-protein interaction networks. Finally, we assessed the druggability of the identified proteins. RESULTS At Bonferroni significance (P < 6.82 × 10-5), Mendelian randomization analysis revealed that three proteins were causally associated with low grip strength. Increased MGP (OR = 0.85) and HP (OR = 0.96) decreased the risk of low grip strength, whereas elevated ART4 (OR = 1.06) increased the risk of low grip strength. None of the three proteins had reverse causality with low grip strength. Bayesian co-localization suggested that MGP shared the same variant with low grip strength (coloc.abf-PPH4 = 0.826). Further downstream analyses showed that MGP, which is highly expressed in musculoskeletal system, is a potential novel target for muscle weakness. CONCLUSIONS The proteome-wide Mendelian randomization investigation identified three proteins associated with the risk of muscle weakness. MGP, HP, and ART4 deserve further investigation as potential therapeutic targets for muscle weakness.
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Affiliation(s)
- Shuai-Kang Wang
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China
| | - Qi-Jun Wang
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China
| | - Xuan Zhao
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China
| | - Peng Wang
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China
| | - Xiang-Yu Li
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China
| | - Wei Wang
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China.
| | - Shi-Bao Lu
- Department of Orthopedics and Elderly Spinal Surgery, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; National Clinical Research Center for Geriatric Diseases, Beijing 100053, China.
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6
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Cruchaga C, Yang C, Gorijala P, Timsina J, Wang L, Liu M, Wang C, Brock W, Wang Y, Sung YJ. European and African-specific plasma protein-QTL and metabolite-QTL analyses identify ancestry-specific T2D effector proteins and metabolites. RESEARCH SQUARE 2024:rs.3.rs-3617016. [PMID: 39108494 PMCID: PMC11302687 DOI: 10.21203/rs.3.rs-3617016/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/11/2024]
Abstract
Initially focused on the European population, multiple genome-wide association studies (GWAS) of complex diseases, such as type-2 diabetes (T2D), have now extended to other populations. However, to date, few ancestry-matched omics datasets have been generated or further integrated with the disease GWAS to nominate the key genes and/or molecular traits underlying the disease risk loci. In this study, we generated and integrated plasma proteomics and metabolomics with array-based genotype datasets of European (EUR) and African (AFR) ancestries to identify ancestry-specific muti-omics quantitative trait loci (QTLs). We further applied these QTLs to ancestry-stratified T2D risk to pinpoint key proteins and metabolites underlying the disease-associated genetic loci. We nominated five proteins and four metabolites in the European group and one protein and one metabolite in the African group to be part of the molecular pathways of T2D risk in an ancestry-stratified manner. Our study demonstrates the integration of genetic and omic studies of different ancestries can be used to identify distinct effector molecular traits underlying the same disease across diverse populations. Specifically, in the AFR proteomic findings on T2D, we prioritized the protein QSOX2; while in the AFR metabolomic findings, we pinpointed the metabolite GlcNAc sulfate conjugate of C21H34O2 steroid. Neither of these findings overlapped with the corresponding EUR results.
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Affiliation(s)
| | | | | | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Washington University School of Medicine
| | - Menghan Liu
- Washington University School of Medicine, St. Louis, MO, USA
| | | | - William Brock
- Washington University School of Medicine, St. Louis, MO, USA
| | - Yueyao Wang
- Washington University School of Medicine, St. Louis, MO, USA
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7
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Kraemer S, Schneider DJ, Paterson C, Perry D, Westacott MJ, Hagar Y, Katilius E, Lynch S, Russell TM, Johnson T, Astling DP, DeLisle RK, Cleveland J, Gold L, Drolet DW, Janjic N. Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome. J Proteome Res 2024. [PMID: 39038188 DOI: 10.1021/acs.jproteome.4c00411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.
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Affiliation(s)
- Stephan Kraemer
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Daniel J Schneider
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Clare Paterson
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Darryl Perry
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Matthew J Westacott
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Yolanda Hagar
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Evaldas Katilius
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Sean Lynch
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Theresa M Russell
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Ted Johnson
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - David P Astling
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Robert Kirk DeLisle
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Jason Cleveland
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Larry Gold
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Daniel W Drolet
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
| | - Nebojsa Janjic
- SomaLogic, 2495 Wilderness Place, Boulder, Colorado 80301, United States of America
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8
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Fernandez MV, Liu M, Beric A, Johnson M, Cetin A, Patel M, Budde J, Kohlfeld P, Bergmann K, Lowery J, Flynn A, Brock W, Sanchez Montejo B, Gentsch J, Sykora N, Norton J, Gentsch J, Valdez O, Gorijala P, Sanford J, Sun Y, Wang C, Western D, Timsina J, Mangetti Goncalves T, Do AN, Sung YJ, Zhao G, Morris JC, Moulder K, Holtzman DM, Bateman RJ, Karch C, Hassenstab J, Xiong C, Schindler SE, Balls-Berry JJ, Benzinger TLS, Perrin RJ, Denny A, Snider BJ, Stark SL, Ibanez L, Cruchaga C. Genetic and multi-omic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center. Sci Data 2024; 11:768. [PMID: 38997326 PMCID: PMC11245521 DOI: 10.1038/s41597-024-03485-9] [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: 01/24/2024] [Accepted: 06/06/2024] [Indexed: 07/14/2024] Open
Abstract
The Knight-Alzheimer Disease Research Center (Knight-ADRC) at Washington University in St. Louis has pioneered and led worldwide seminal studies that have expanded our clinical, social, pathological, and molecular understanding of Alzheimer Disease. Over more than 40 years, research volunteers have been recruited to participate in cognitive, neuropsychologic, imaging, fluid biomarkers, genomic and multi-omic studies. Tissue and longitudinal data collected to foster, facilitate, and support research on dementia and aging. The Genetics and high throughput -omics core (GHTO) have collected of more than 26,000 biological samples from 6,625 Knight-ADRC participants. Samples available include longitudinal DNA, RNA, non-fasted plasma, cerebrospinal fluid pellets, and peripheral blood mononuclear cells. The GHTO has performed deep molecular profiling (genomic, transcriptomic, epigenomic, proteomic, and metabolomic) from large number of brain (n = 2,117), CSF (n = 2,012) and blood/plasma (n = 8,265) samples with the goal of identifying novel risk and protective variants, identify novel molecular biomarkers and causal and druggable targets. Overall, the resources available at GHTO support the increase of our understanding of Alzheimer Disease.
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Affiliation(s)
- Maria Victoria Fernandez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Research Center and Memory Clinic, ACE Alzheimer Center, Barcelona, Spain
| | - Menghan Liu
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Aleksandra Beric
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Matt Johnson
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Arda Cetin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Maulik Patel
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - John Budde
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Pat Kohlfeld
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Kristy Bergmann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Joseph Lowery
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Allison Flynn
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - William Brock
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Brenda Sanchez Montejo
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jen Gentsch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Nicholas Sykora
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Joanne Norton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jen Gentsch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Olga Valdez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jessie Sanford
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yichen Sun
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Ciyang Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Dan Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | | | - Anh N Do
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Guoyan Zhao
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Pathology and Immunology Department, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - John C Morris
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Krista Moulder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Randall J Bateman
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA
| | - Celeste Karch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA
| | - Jason Hassenstab
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Joyce Joy Balls-Berry
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Tammie L S Benzinger
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA
- Radiology Department, Washington University School of Medicine, St. Louis, MO, 63110, USA
| | - Richard J Perrin
- Pathology and Immunology Department, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA
| | - Andrea Denny
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
| | - B Joy Snider
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA
| | - Susan L Stark
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA
- Occupational Therapy, Neurology and Social Work, St. Louis, USA
| | - Laura Ibanez
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA.
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, 63110, USA.
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA.
- Dominantly Inherited Alzheimer Disease Network (DIAN), St. Louis, USA.
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9
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Guo Y, Chen SD, You J, Huang SY, Chen YL, Zhang Y, Wang LB, He XY, Deng YT, Zhang YR, Huang YY, Dong Q, Feng JF, Cheng W, Yu JT. Multiplex cerebrospinal fluid proteomics identifies biomarkers for diagnosis and prediction of Alzheimer's disease. Nat Hum Behav 2024:10.1038/s41562-024-01924-6. [PMID: 38987357 DOI: 10.1038/s41562-024-01924-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 06/10/2024] [Indexed: 07/12/2024]
Abstract
Recent expansion of proteomic coverage opens unparalleled avenues to unveil new biomarkers of Alzheimer's disease (AD). Among 6,361 cerebrospinal fluid (CSF) proteins analysed from the ADNI database, YWHAG performed best in diagnosing both biologically (AUC = 0.969) and clinically (AUC = 0.857) defined AD. Four- (YWHAG, SMOC1, PIGR and TMOD2) and five- (ACHE, YWHAG, PCSK1, MMP10 and IRF1) protein panels greatly improved the accuracy to 0.987 and 0.975, respectively. Their superior performance was validated in an independent external cohort and in discriminating autopsy-confirmed AD versus non-AD, rivalling even canonical CSF ATN biomarkers. Moreover, they effectively predicted the clinical progression to AD dementia and were strongly associated with AD core biomarkers and cognitive decline. Synaptic, neurogenic and infectious pathways were enriched in distinct AD stages. Mendelian randomization did not support the significant genetic link between CSF proteins and AD. Our findings revealed promising high-performance biomarkers for AD diagnosis and prediction, with implications for clinical trials targeting different pathomechanisms.
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Affiliation(s)
- Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Shi-Dong Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jia You
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Shu-Yi Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi-Lin Chen
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yi Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Lin-Bo Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Xiao-Yu He
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yue-Ting Deng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ya-Ru Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yu-Yuan Huang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Qiang Dong
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan University, Shanghai, China.
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10
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Debette S, Caro I, Western D, Namba S, Sun N, Kawaguchi S, He Y, Fujita M, Roshchupkin G, D'Aoust T, Duperron MG, Sargurupremraj M, Tsuchida A, Koido M, Ahmadi M, Yang C, Timsina J, Ibanez L, Matsuda K, Suzuki Y, Oda Y, Kanai A, Jandaghi P, Munter HM, Auld D, Astafeva I, Puerta R, Rotter J, Psaty B, Bis J, Longstreth W, Couffinhal T, Garcia-Gonzalez P, Pytel V, Marquié M, Cano A, Boada M, Joliot M, Lathrop M, Le Grand Q, Launer L, Wardlaw J, Heiman M, Ruiz A, Matthews P, Seshadri S, Fornage M, Adams H, Mishra A, Trégouët DA, Okada Y, Kellis M, De Jager P, Tzourio C, Kamatani Y, Matsuda F, Cruchaga C. Proteogenomics in cerebrospinal fluid and plasma reveals new biological fingerprint of cerebral small vessel disease. RESEARCH SQUARE 2024:rs.3.rs-4535534. [PMID: 39011113 PMCID: PMC11247936 DOI: 10.21203/rs.3.rs-4535534/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Cerebral small vessel disease (cSVD) is a leading cause of stroke and dementia with no specific mechanism-based treatment. We used Mendelian randomization to combine a unique cerebrospinal fluid (CSF) and plasma pQTL resource with the latest European-ancestry GWAS of MRI-markers of cSVD (white matter hyperintensities, perivascular spaces). We describe a new biological fingerprint of 49 protein-cSVD associations, predominantly in the CSF. We implemented a multipronged follow-up, across fluids, platforms, and ancestries (Europeans and East-Asian), including testing associations of direct plasma protein measurements with MRI-cSVD. We highlight 16 proteins robustly associated in both CSF and plasma, with 24/4 proteins identified in CSF/plasma only. cSVD-proteins were enriched in extracellular matrix and immune response pathways, and in genes enriched in microglia and specific microglial states (integration with single-nucleus RNA sequencing). Immune-related proteins were associated with MRI-cSVD already at age twenty. Half of cSVD-proteins were associated with stroke, dementia, or both, and seven cSVD-proteins are targets for known drugs (used for other indications in directions compatible with beneficial therapeutic effects. This first cSVD proteogenomic signature opens new avenues for biomarker and therapeutic developments.
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Affiliation(s)
| | | | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Na Sun
- MIT Computer Science and Artificial Intelligence Laboratory; Broad Institute of MIT and Harvard
| | | | - Yunye He
- Graduate School of Frontier Sciences, The University of Tokyo
| | | | | | - Tim D'Aoust
- Bordeaux Population Health, Inserm U1219, University of Bordeaux
| | | | - Murali Sargurupremraj
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, team VINTAGE, UMR 1219, F-33000 Bordeaux, France; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases Unive
| | | | - Masaru Koido
- Graduate School of Frontier Sciences, The University of Tokyo
| | | | | | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo
| | | | - Yoshiya Oda
- Graduate School of Medicine, The University of Tokyo
| | | | | | | | - Dan Auld
- Victor Phillip Dahdaleh Institute of Genomic Medicine, McGill University
| | - Iana Astafeva
- Bordeaux Population Health, Inserm U1219, University of Bordeaux; Institute of Neurodegenerative Diseases
| | | | - Jerome Rotter
- The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center
| | | | | | | | - Thierry Couffinhal
- University of Bordeaux, The clinical unit of Exploration, Prevention and Care Center for Atherosclerosis (CEPTA), CHUB, Inserm U1034
| | | | - Vanesa Pytel
- Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya; CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III
| | | | | | | | | | - Mark Lathrop
- Department of Human Genetics, McGill University, 1205 Dr Penfield Avenue, Montreal, QC, H3A 1B1, Canada
| | - Quentin Le Grand
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219
| | - Lenore Launer
- National Institute on Aging, National Institutes of Health
| | | | | | - Agustin Ruiz
- Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Sciences Center; Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya;CIBERN
| | - Paul Matthews
- UK Dementia Research Institute Centre at Imperial College London
| | | | - Myriam Fornage
- 1. Institute of Molecular Medicine, McGovern Medical School, The University of Texas Health Science Center 2. Human Genetics Center, Department of Epidemiology, School of Public Health
| | - Hieab Adams
- Department of Human Genetics, Radboud University Medical Center; Latin American Brain Health (BrainLat), Universidad Adolfo Ibáñez
| | | | | | - Yukinori Okada
- Department of Genome Informatics, Graduate School of Medicine, The Univ. of Tokyo; Department of Statistical Genetics, Osaka Univ. Graduate School of Medicine; Laboratory for Systems Genetic, RIKEN
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11
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Zhang C, Jian L, Li X, Guo W, Deng W, Hu X, Li T. Mendelian randomization analysis of the brain, cerebrospinal fluid, and plasma proteome identifies potential drug targets for attention deficit hyperactivity disorder. EBioMedicine 2024; 105:105197. [PMID: 38876042 PMCID: PMC11225168 DOI: 10.1016/j.ebiom.2024.105197] [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: 01/13/2024] [Revised: 05/26/2024] [Accepted: 05/29/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND The need for new therapeutics for attention deficit hyperactivity disorder (ADHD) is evident. Brain, cerebrospinal fluid (CSF), and plasma protein biomarkers with causal genetic evidence could represent potential drug targets. However, a comprehensive screen of the proteome has not yet been conducted. METHODS We employed a three-pronged approach using Mendelian Randomization (MR) and Bayesian colocalization analysis. Firstly, we studied 608 brains, 214 CSF, and 612 plasma proteins as potential causal mediators of ADHD using MR analysis. Secondly, we analysed the consistency of the discovered biomarkers across three distinct subtypes of ADHD: childhood, persistent, and late-diagnosed ADHD. Finally, we extended our analysis to examine the correlation between identified biomarkers and Tourette syndrome and pervasive autism spectrum disorder (ASD), conditions often linked with ADHD. To validate the MR findings, we conducted sensitivity analysis. Additionally, we performed cell type analysis on the human brain to identify risk genes that are notably enriched in various brain cell types. FINDINGS After applying Bonferroni correction, we found that the risk of ADHD was increased by brain proteins GMPPB, NAA80, HYI, CISD2, and HYI, TIE1 in CSF and plasma. Proteins GMPPB, NAA80, ICA1L, CISD2, TIE1, and RMDN1 showed overlapped loci with ADHD risk through Bayesian colocalization. Overexpression of GMPPB protein was linked to an increase in the risk for all three ADHD subtypes. While ICA1L provided protection against both ASD and ADHD, CISD2 increased the probability of both disorders. Cell-specific studies revealed that GMPPB, NAA80, ICA1L, and CISD2 were predominantly present on the surface of excitatory-inhibitory neurons. INTERPRETATION Our comprehensive MR investigation of the brain, CSF, and plasma proteomes revealed seven proteins with causal connections to ADHD. Particularly, GMPPB and TIE1 emerged as intriguing targets for potential ADHD therapy. FUNDING This work was partly funded by the Key R & D Program of Zhejiang (T.L. 2022C03096); the National Natural Science Foundation of China Project (C.Z. 82001413); Postdoctoral Foundation of West China Hospital (C.Z. 2020HXBH163).
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Affiliation(s)
- Chengcheng Zhang
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Lingqi Jian
- Mental Health Center and Psychiatric Laboratory, The State Key Laboratory of Biotherapy, West China Hospital of Sichuan University, Chengdu, 610041, Sichuan, China
| | - Xiaojing Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Wanjun Guo
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Wei Deng
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China
| | - Xun Hu
- The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China
| | - Tao Li
- Affiliated Mental Health Center & Hangzhou Seventh People's Hospital and School of Brain Science and Brain Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China; Liangzhu Laboratory, MOE Frontier Science Center for Brain Science and Brain-machine Integration, State Key Laboratory of Brain-machine Intelligence, Zhejiang University, 1369 West Wenyi Road, Hangzhou, 311121, China; NHC and CAMS Key Laboratory of Medical Neurobiology, Zhejiang University, Hangzhou, 310058, China.
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12
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Zhao H, Liu Y, Zhang X, Liao Y, Zhang H, Han X, Guo L, Fan B, Wang W, Lu C. Identifying novel proteins for suicide attempt by integrating proteomes from brain and blood with genome-wide association data. Neuropsychopharmacology 2024; 49:1255-1265. [PMID: 38317018 PMCID: PMC11224332 DOI: 10.1038/s41386-024-01807-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 12/28/2023] [Accepted: 01/16/2024] [Indexed: 02/07/2024]
Abstract
Genome-wide association studies (GWASs) have identified risk loci for suicide attempt (SA), but deciphering how they confer risk for SA remains largely unknown. This study aims to identify the key proteins and gain insights into SA pathogenesis. We integrated data from the brain proteome (N = 376) and blood proteome (N = 35,559) and combined it with the largest SA GWAS summary statistics to date (N = 518,612). A comprehensive set of methods was employed, including Mendelian randomization (MR), Steiger filtering, Bayesian colocalization, proteome‑wide association studies (PWAS), transcript-levels, cell-type specificity, correlation, and protein-protein interaction (PPI) network analysis. Validation was performed using other protein datasets and the SA dataset from FinnGen study. We identified ten proteins (GLRX5, GMPPB, B3GALTL, FUCA2, TTLL12, ADCK1, MMAA, HIBADH, ACP1, DOC2A) associated with SA in brain proteomics. GLRX5, GMPPB, and FUCA2 showed strong colocalization evidence and were supported by PWAS and transcript-level analysis, and were predominantly expressed in glutamatergic neuronal cells. In blood proteomics, one significant protein (PEAR1) and three near-significant proteins (NDE1, EVA1C, B4GALT2) were identified, but lacked colocalization evidence. Moreover, despite the limited correlation between the same protein in brain and blood, the PPI network analysis provided new insights into the interaction between brain and blood in SA. Furthermore, GLRX5 was associated with the GSTP1, the target of Clozapine. The comprehensive analysis provides strong evidence supporting a causal association between three genetically determined brain proteins (GLRX5, GMPPB, and FUCA2) with SA. These findings offer valuable insights into SA's underlying mechanisms and potential therapeutic approaches.
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Affiliation(s)
- Hao Zhao
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| | - Yifeng Liu
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Xuening Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Yuhua Liao
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Huimin Zhang
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Xue Han
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China
| | - Lan Guo
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
| | - Beifang Fan
- Department of Psychiatry, Shenzhen Nanshan Center for Chronic Disease Control, Shenzhen, China.
| | - Wanxin Wang
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China.
| | - Ciyong Lu
- Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Sun Yat-sen University, Guangzhou, China
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13
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Sun BB, Suhre K, Gibson BW. Promises and Challenges of populational Proteomics in Health and Disease. Mol Cell Proteomics 2024; 23:100786. [PMID: 38761890 PMCID: PMC11193116 DOI: 10.1016/j.mcpro.2024.100786] [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: 02/06/2024] [Revised: 05/13/2024] [Accepted: 05/15/2024] [Indexed: 05/20/2024] Open
Abstract
Advances in proteomic assay technologies have significantly increased coverage and throughput, enabling recent increases in the number of large-scale population-based proteomic studies of human plasma and serum. Improvements in multiplexed protein assays have facilitated the quantification of thousands of proteins over a large dynamic range, a key requirement for detecting the lowest-ranging, and potentially the most disease-relevant, blood-circulating proteins. In this perspective, we examine how populational proteomic datasets in conjunction with other concurrent omic measures can be leveraged to better understand the genomic and non-genomic correlates of the soluble proteome, constructing biomarker panels for disease prediction, among others. Mass spectrometry workflows are discussed as they are becoming increasingly competitive with affinity-based array platforms in terms of speed, cost, and proteome coverage due to advances in both instrumentation and workflows. Despite much success, there remain considerable challenges such as orthogonal validation and absolute quantification. We also highlight emergent challenges associated with study design, analytical considerations, and data integration as population-scale studies are run in batches and may involve longitudinal samples collated over many years. Lastly, we take a look at the future of what the nascent next-generation proteomic technologies might provide to the analysis of large sets of blood samples, as well as the difficulties in designing large-scale studies that will likely require participation from multiple and complex funding sources and where data sharing, study designs, and financing must be solved.
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Affiliation(s)
- Benjamin B Sun
- Human Genetics, Informatics and Predictive Sciences, Bristol-Myers Squibb, Cambridge, Massachusetts, USA.
| | - Karsten Suhre
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar; Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Bradford W Gibson
- Pharmaceutical Chemistry, University of California, San Francisco, California, USA
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14
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Song J, Saglam A, Zuchero JB, Buch VP. Translating Molecular Approaches to Oligodendrocyte-Mediated Neurological Circuit Modulation. Brain Sci 2024; 14:648. [PMID: 39061389 PMCID: PMC11275066 DOI: 10.3390/brainsci14070648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/28/2024] Open
Abstract
The central nervous system (CNS) exhibits remarkable adaptability throughout life, enabled by intricate interactions between neurons and glial cells, in particular, oligodendrocytes (OLs) and oligodendrocyte precursor cells (OPCs). This adaptability is pivotal for learning and memory, with OLs and OPCs playing a crucial role in neural circuit development, synaptic modulation, and myelination dynamics. Myelination by OLs not only supports axonal conduction but also undergoes adaptive modifications in response to neuronal activity, which is vital for cognitive processing and memory functions. This review discusses how these cellular interactions and myelin dynamics are implicated in various neurocircuit diseases and disorders such as epilepsy, gliomas, and psychiatric conditions, focusing on how maladaptive changes contribute to disease pathology and influence clinical outcomes. It also covers the potential for new diagnostics and therapeutic approaches, including pharmacological strategies and emerging biomarkers in oligodendrocyte functions and myelination processes. The evidence supports a fundamental role for myelin plasticity and oligodendrocyte functionality in synchronizing neural activity and high-level cognitive functions, offering promising avenues for targeted interventions in CNS disorders.
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Affiliation(s)
- Jingwei Song
- Medical Scientist Training Program, School of Medicine, Stanford University, Stanford, CA 94305, USA;
| | - Aybike Saglam
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; (A.S.); (J.B.Z.)
| | - J. Bradley Zuchero
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; (A.S.); (J.B.Z.)
| | - Vivek P. Buch
- Department of Neurosurgery, Stanford University, Stanford, CA 94305, USA; (A.S.); (J.B.Z.)
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15
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Shi S, Chen Y, Chu X, Shi P, Wang B, Cai Q, He D, Zhang N, Qin X, Wei W, Zhao Y, Jia Y, Zhang F, Wen Y. Evaluating the associations between intelligence quotient and multi-tissue proteome from the brain, CSF and plasma. Brain Commun 2024; 6:fcae207. [PMID: 38961868 PMCID: PMC11220507 DOI: 10.1093/braincomms/fcae207] [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: 02/15/2023] [Revised: 01/16/2024] [Accepted: 06/20/2024] [Indexed: 07/05/2024] Open
Abstract
Intelligence quotient is a vital index to evaluate the ability of an individual to think rationally, learn from experience and deal with the environment effectively. However, limited efforts have been paid to explore the potential associations of intelligence quotient traits with the tissue proteins from the brain, CSF and plasma. The information of protein quantitative trait loci was collected from a recently released genome-wide association study conducted on quantification data of proteins from the tissues including the brain, CSF and plasma. Using the individual-level genotypic data from the UK Biobank cohort, we calculated the polygenic risk scores for each protein based on the protein quantitative trait locus data sets above. Then, Pearson correlation analysis was applied to evaluate the relationships between intelligence quotient traits (including 120 330 subjects for 'fluid intelligence score' and 38 949 subjects for 'maximum digits remembered correctly') and polygenic risk scores of each protein in the brain (17 protein polygenic risk scores), CSF (116 protein polygenic risk scores) and plasma (59 protein polygenic risk scores). The Bonferroni corrected P-value threshold was P < 1.30 × 10-4 (0.05/384). Finally, Mendelian randomization analysis was conducted to test the causal relationships between 'fluid intelligence score' and pre-specific proteins from correlation analysis results. Pearson correlation analysis identified significant association signals between the protein of macrophage-stimulating protein and fluid intelligence in brain and CSF tissues (P brain = 1.21 × 10-8, P CSF = 1.10 × 10-7), as well as between B-cell lymphoma 6 protein and fluid intelligence in CSF (P CSF = 1.23 × 10-4). Other proteins showed close-to-significant associations with the trait of 'fluid intelligence score', such as plasma protease C1 inhibitor (P CSF = 4.19 × 10-4, P plasma = 6.97 × 10-4), and with the trait of 'maximum digits remembered correctly', such as tenascin (P plasma = 3.42 × 10-4). Additionally, Mendelian randomization analysis results suggested that macrophage-stimulating protein (Mendelian randomization-Egger: β = 0.54, P = 1.64 × 10-61 in the brain; β = 0.09, P = 1.60 × 10-12 in CSF) had causal effects on fluid intelligence score. We observed functional relevance of specific tissue proteins to intelligence quotient and identified several candidate proteins, such as macrophage-stimulating protein. This study provided a novel insight to the relationship between tissue proteins and intelligence quotient traits.
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Affiliation(s)
- Sirong Shi
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Yujing Chen
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Xiaoge Chu
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Panxing Shi
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Bingyi Wang
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Qingqing Cai
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Dan He
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Na Zhang
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Xiaoyue Qin
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Wenming Wei
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Yijing Zhao
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Yumeng Jia
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Feng Zhang
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
| | - Yan Wen
- NHC Key Laboratory of Environment and Endemic Diseases, School of Public Health, Health Science Center, Xi’an Jiaotong University, Xi’an, Shaanxi, 710061, China
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16
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Seo D, Lee CM, Apio C, Heo G, Timsina J, Kohlfeld P, Boada M, Orellana A, Fernandez MV, Ruiz A, Morris JC, Schindler SE, Park T, Cruchaga C, Sung YJ. Sex and aging signatures of proteomics in human cerebrospinal fluid identify distinct clusters linked to neurodegeneration. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.18.24309102. [PMID: 38947020 PMCID: PMC11213043 DOI: 10.1101/2024.06.18.24309102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Sex and age are major risk factors for chronic diseases. Recent studies examining age-related molecular changes in plasma provided insights into age-related disease biology. Cerebrospinal fluid (CSF) proteomics can provide additional insights into brain aging and neurodegeneration. By comprehensively examining 7,006 aptamers targeting 6,139 proteins in CSF obtained from 660 healthy individuals aged from 43 to 91 years old, we subsequently identified significant sex and aging effects on 5,097 aptamers in CSF. Many of these effects on CSF proteins had different magnitude or even opposite direction as those on plasma proteins, indicating distinctive CSF-specific signatures. Network analysis of these CSF proteins revealed not only modules associated with healthy aging but also modules showing sex differences. Through subsequent analyses, several modules were highlighted for their proteins implicated in specific diseases. Module 2 and 6 were enriched for many aging diseases including those in the circulatory systems, immune mechanisms, and neurodegeneration. Together, our findings fill a gap of current aging research and provide mechanistic understanding of proteomic changes in CSF during a healthy lifespan and insights for brain aging and diseases.
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17
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Ibanez L, Liu M, Beric A, Timsina J, Kholfeld P, Bergmann K, Lowery J, Sykora N, Sanchez-Montejo B, Brock W, Budde JP, Bateman RJ, Barthelemy N, Schindler SE, Holtzman DM, Benzinger TLS, Xiong C, Tarawneh R, Moulder K, Morris JC, Sung YJ, Cruchaga C. Benchmarking of a multi-biomarker low-volume panel for Alzheimer's Disease and related dementia research. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.13.24308895. [PMID: 38947090 PMCID: PMC11213109 DOI: 10.1101/2024.06.13.24308895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Alzheimer's Disease (AD) biomarker measurement is key to aid in the diagnosis and prognosis of the disease. In the research setting, participant recruitment and retention and optimization of sample use, is one of the main challenges that observational studies face. Thus, obtaining accurate established biomarker measurements for stratification and maximizing use of the precious samples is key. Accurate technologies are currently available for established biomarkers, mainly immunoassays and immunoprecipitation liquid chromatography-mass spectrometry (IP-MS), and some of them are already being used in clinical settings. Although some immunoassays- and IP-MS based platforms provide multiplexing for several different coding proteins there is not a current platform that can measure all the stablished and emerging biomarkers in one run. The NUcleic acid Linked Immuno-Sandwich Assay (NULISA™) is a mid-throughput platform with antibody-based measurements with a sequencing output that requires 15μL of sample volume to measure more than 100 analytes, including those typically assayed for AD. Here we benchmarked and compared the AD-relevant biomarkers including in the NULISA against validated assays, in both CSF and plasma. Overall, we have found that CSF measures of Aß42/40, NfL, GFAP, and p-tau217 are highly correlated and have similar predictive performance when measured by immunoassay, mass-spectrometry or NULISA. In plasma, p-tau217 shows a performance similar to that reported with other technologies when predicting amyloidosis. Other established and exploratory biomarkers (total tau, p-tau181, NRGN, YKL40, sTREM2, VILIP1 among other) show a wide range of correlation values depending on the fluid and the platform. Our results indicate that the multiplexed immunoassay platform produces reliable results for established biomarkers in CSF that are useful in research settings, with the advantage of measuring additional novel biomarkers using minimal sample volume.
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Affiliation(s)
- Laura Ibanez
- Department of Psychiatry, Washington University School of Medicine
- Department of Neurology, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Menghan Liu
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Aleksandra Beric
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Pat Kholfeld
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Kristy Bergmann
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Joey Lowery
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Nick Sykora
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Brenda Sanchez-Montejo
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Will Brock
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - John P. Budde
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- The Tracy Family SILQ Center, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - Nicolas Barthelemy
- Department of Neurology, Washington University School of Medicine
- The Tracy Family SILQ Center, Washington University School of Medicine
| | - Suzanne E. Schindler
- Department of Neurology, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - David M Holtzman
- Department of Neurology, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - Tammie L. S. Benzinger
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
- Department of Radiology, Washington University School of Medicine
| | - Chengjie Xiong
- Division of Biostatistics, Washington University School of Medicine
| | - Rawan Tarawneh
- Department of Neurology, University of New Mexico School of Medicine
| | - Krista Moulder
- Department of Neurology, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine
- Department of Neurology, Washington University School of Medicine
- NeuroGenomics and Informatics Center, Washington University School of Medicine
- Hope Center for Neurologic Diseases, Washington University School of Medicine
- Knight Alzheimer Disease Research Center, Washington University School of Medicine
- Department of Genetics, Washington University School of Medicine
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18
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Yusufujiang A, Zeng S, Li H. Cathepsins and Parkinson's disease: insights from Mendelian randomization analyses. Front Aging Neurosci 2024; 16:1380483. [PMID: 38903897 PMCID: PMC11188310 DOI: 10.3389/fnagi.2024.1380483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/15/2024] [Indexed: 06/22/2024] Open
Abstract
Background Parkinson's disease (PD), the second most prevalent neurodegenerative condition, has a multifaceted etiology. Cathepsin-cysteine proteases situated within lysosomes participate in a range of physiological and pathological processes, including the degradation of harmful proteins. Prior research has pointed towards a potential link between cathepsins and PD; however, the precise causal relationship between the cathepsin family and PD remains unclear. Methods This study employed univariate and multivariate Mendelian randomization (MR) analyses to explore the causal relationship between the nine cathepsins and Parkinson's disease (PD) risk. For the primary analysis, genome-wide association study (GWAS) summary statistics for the plasma levels of the nine cathepsins and PD was obtained from the INTERVAL study and the International Parkinson's Disease Genomics Consortium. GWAS for PD replication analysis were obtained from the FinnGen consortium, and a meta-analysis was performed for the primary and replication analyses to evaluate the association between genetically predicted cathepsin plasma levels and PD risk. After identifying significant MR estimates, genetic co-localization analyses were conducted to determine whether shared or distinct causal variants influenced both cathepsins and PD. Results Elevated cathepsin B levels were associated with a decreased risk of PD in univariate MR analysis (odds ratio [OR] = 0.890, 95% confidence interval [CI]: 0.831-0.954, pFDR = 0.009). However, there was no indication that PD affected cathepsin B levels (OR = 0.965, 95% CI: 0.858-1.087, p = 0.852). In addition, after adjusting for the remaining cathepsins, cathepsin B levels independently and significantly contributed to the reduced risk of PD in multivariate MR analysis (OR = 0.887, 95% CI: 0.823-0.957, p = 0.002). The results of the replication MR analysis with the FinnGen GWAS for PD (OR = 0.921, 95% CI: 0.860-0.987, p = 0.020) and meta-analysis (OR = 0.905, 95% CI: 0.862-0.951, p < 0.001) were consistent with those of the primary analysis. Colocalization analysis did not provide any evidence of a shared causal variant between cathepsins and PD (PP.H4.abf = 0.005). Conclusion This genetic investigation supports the hypothesis that cathepsin B exerts a protective effect against PD. The quantification of cathepsin B levels could potentially serve as a predictive biomarker for susceptibility to PD, providing new insights into the pathomechanisms of the disease and possible interventions.
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Affiliation(s)
| | - Shan Zeng
- Department of Graduate School, Xinjiang Medical University, Ürümqi, China
- Department of Neurology, People’s Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, China
- Xinjiang Clinical Research Center for Stroke and Neurological Rare Disease, Ürümqi, China
| | - Hongyan Li
- Department of Neurology, People’s Hospital of Xinjiang Uygur Autonomous Region, Ürümqi, China
- Xinjiang Clinical Research Center for Stroke and Neurological Rare Disease, Ürümqi, China
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19
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Zeng Y, Guo R, Cao S, Liu C, Yang H. CSF N-acylethanolamine acid amidase level and Parkinson's disease risk: A mendelian randomization study. Parkinsonism Relat Disord 2024; 123:106953. [PMID: 38579440 DOI: 10.1016/j.parkreldis.2024.106953] [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: 11/06/2023] [Revised: 03/11/2024] [Accepted: 03/30/2024] [Indexed: 04/07/2024]
Abstract
BACKGROUND Neuroinflammation is involved in the progression of Parkinson's disease (PD), and N-acylethanolamine acid amidase (NAAA) is involved in regulating inflammation by hydrolyzing bioactive lipid mediators called N-acylethanolamines (NAEs). However, the causal relationship between cerebrospinal fluid (CSF) NAAA protein levels and the risk of PD remains unclear. This study aimed to explore the causal effect of CSF NAAA levels on PD risk through Mendelian randomization (MR) analysis. METHOD Genome-wide association study (GWAS) summary statistics for CSF NAAA protein quantitative trait loci (pQTL) and GWAS summary statistics for PD were obtained from publicly available databases. Inverse-variance weighted (IVW) was the main causal estimation method for MR analysis. In addition, the maximum likelihood, MR Egger regression, and weighted median were used to supplement the IVW results. Finally, various sensitivity tests were performed to verify the reliability of the MR findings. RESULTS In the initial MR analysis, the IVW showed that CSF NAAA protein levels significantly increased PD risk (odds ratio [OR] = 1.17, 95% confidence interval [CI]: 1.01-1.35, P = 0.031). This finding was further validated in a replicate MR analysis (OR = 1.20, 95% CI: 1.02-1.41, P = 0.027). Sensitivity analysis showed that MR results were stable and not affected by heterogeneity and horizontal pleiotropy. CONCLUSION The present MR study supports a causal relationship between elevated CSF NAAA protein levels and increased PD risk.
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Affiliation(s)
- Youjie Zeng
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Ren Guo
- Department of Pharmacy, Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Si Cao
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, 410205, Hunan, China
| | - Chunxia Liu
- Department of Anesthesiology, Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China
| | - Heng Yang
- Department of Neurology, Third Xiangya Hospital, Central South University, Changsha, Hunan, 410013, China.
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20
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Hansen N, Wiltfang J. Fluid biomarkers unveil signatures of pathological aging. Seizure 2024:S1059-1311(24)00158-4. [PMID: 38871529 DOI: 10.1016/j.seizure.2024.05.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/29/2024] [Accepted: 05/30/2024] [Indexed: 06/15/2024] Open
Abstract
Aging is a multifaceted and highly varied process in the brain. Identifying aging biomarkers is one means of distinguishing pathological from physiological aging. The aim of this narrative review is to focus on two new developments in the field of fluid biomarkers and draw attention to this excellent tool for the early detection of potential brain pathologies that delay, alter, or enable physiological aging to become pathological. Pathological aging can lower the threshold for the development of specific diseases such as late-onset epilepsy. Fluid biomarkers can reveal pathological levels at an early stage and thus indicate disease processes in the brain that begin before symptoms develop; they thus differ from physiological aging.
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Affiliation(s)
- Niels Hansen
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Von-Siebold-Str. 5, 37075 Göttingen, Germany.
| | - Jens Wiltfang
- Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, Von-Siebold-Str. 5, 37075 Göttingen, Germany; German Center for Neurodegenerative Diseases (DZNE), Von-Siebold-Str. 3a, 37075, Göttingen, Germany; Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), Department of Medical Sciences, University of Aveiro, Aveiro, Portugal
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21
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Walker RM, Chong M, Perrot N, Pigeyre M, Gadd DA, Stolicyn A, Shi L, Campbell A, Shen X, Whalley HC, Nevado-Holgado A, McIntosh AM, Heitmeier S, Rangarajan S, O'Donnell M, Smith EE, Yusuf S, Whiteley WN, Paré G. The circulating proteome and brain health: Mendelian randomisation and cross-sectional analyses. Transl Psychiatry 2024; 14:204. [PMID: 38762535 PMCID: PMC11102511 DOI: 10.1038/s41398-024-02915-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 04/17/2024] [Accepted: 04/23/2024] [Indexed: 05/20/2024] Open
Abstract
Decline in cognitive function is the most feared aspect of ageing. Poorer midlife cognitive function is associated with increased dementia and stroke risk. The mechanisms underlying variation in cognitive function are uncertain. Here, we assessed associations between 1160 proteins' plasma levels and two measures of cognitive function, the digit symbol substitution test (DSST) and the Montreal Cognitive Assessment in 1198 PURE-MIND participants. We identified five DSST performance-associated proteins (NCAN, BCAN, CA14, MOG, CDCP1), with NCAN and CDCP1 showing replicated association in an independent cohort, GS (N = 1053). MRI-assessed structural brain phenotypes partially mediated (8-19%) associations between NCAN, BCAN, and MOG, and DSST performance. Mendelian randomisation analyses suggested higher CA14 levels might cause larger hippocampal volume and increased stroke risk, whilst higher CDCP1 levels might increase intracranial aneurysm risk. Our findings highlight candidates for further study and the potential for drug repurposing to reduce the risk of stroke and cognitive decline.
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Affiliation(s)
- Rosie M Walker
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
- School of Psychology, University of Exeter, Perry Road, Exeter, UK.
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK.
| | - Michael Chong
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Nicolas Perrot
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Marie Pigeyre
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Michael G DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - Danni A Gadd
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Aleks Stolicyn
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Liu Shi
- Department of Psychiatry, University of Oxford, Oxford, UK
- Nxera Pharma UK Limited, Cambridge, UK
| | - Archie Campbell
- Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Xueyi Shen
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Heather C Whalley
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- Generation Scotland, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | | | - Andrew M McIntosh
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | | | - Sumathy Rangarajan
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
| | - Martin O'Donnell
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Health Research Board Clinical Research Facility, University of Galway, Galway, Ireland
| | - Eric E Smith
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Clinical Neurosciences and Hotchkiss Brain Institute, Cumming School of Medicine, Calgary, AB, Canada
- University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
| | - Salim Yusuf
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Department of Medicine, Michael G DeGroote School of Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada
| | - William N Whiteley
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada
- Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
- MRC Centre for Population Health, University of Oxford, Oxford, UK
| | - Guillaume Paré
- Population Health Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
- Department of Pathology and Molecular Medicine, Faculty of Health Sciences, McMaster University, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, Hamilton Health Sciences and McMaster University, Hamilton, ON, Canada.
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22
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Zhou Z, Leng H. Deciphering the causal relationship between plasma and cerebrospinal fluid metabolites and glioblastoma multiforme: a Mendelian Randomization study. Aging (Albany NY) 2024; 16:8306-8319. [PMID: 38742944 PMCID: PMC11131984 DOI: 10.18632/aging.205818] [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: 12/26/2023] [Accepted: 04/10/2024] [Indexed: 05/16/2024]
Abstract
BACKGROUND Glioblastoma Multiforme (GBM) is one of the most aggressive and fatal brain cancers. The study of metabolites could be crucial for understanding GBM's biology and reveal new treatment strategies. METHODS The GWAS data for GBM were sourced from the FinnGen database. A total of 1400 plasma metabolites were collected from the GWAS Catalog dataset. The cerebrospinal fluid (CSF) metabolites data were collected from subsets of participants in the WADRC and WRAP studies. We utilized the inverse variance weighting (IVW) method as the primary tool to explore the causal relationship between metabolites in plasma and CSF and glioblastoma, ensuring the exclusion of instances with horizontal pleiotropy. Additionally, four supplementary analytical methods were applied to reinforce our findings. Aberrant results were identified and omitted based on the outcomes of the leave-one-out sensitivity analysis. Conclusively, a reverse Mendelian Randomization analysis was also conducted to further substantiate our results. RESULTS The study identified 69 plasma metabolites associated with GBM. Of these, 40 metabolites demonstrated a significant positive causal relationship with GBM, while 29 exhibited a significant negative causal association. Notably, Trimethylamine N-oxide (TMAO) levels in plasma, not CSF, were found to be a significant exposure factor for GBM (OR = 3.1627, 95% CI = (1.6347, 6.1189), P = 0.0006). The study did not find a reverse causal relationship between GBM and plasma TMAO levels. CONCLUSIONS This research has identified 69 plasma metabolites potentially associated with the incidence of GBM, among which TMAO stands out as a promising candidate for an early detectable biomarker for GBM.
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Affiliation(s)
- Zhiwei Zhou
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
| | - Haibin Leng
- Department of Neurosurgery, Changde Hospital, Xiangya School of Medicine, Central South University (The First People’s Hospital of Changde City), Changde, Hunan 415003, People’s Republic of China
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23
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Sun J, Li X. Response to Yarmolinsky, Tzoulaki, Gunter, et al. J Natl Cancer Inst 2024; 116:766-767. [PMID: 38486363 DOI: 10.1093/jnci/djae065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 03/06/2024] [Indexed: 05/09/2024] Open
Affiliation(s)
- Jing Sun
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xue Li
- Department of Big Data in Health Science School of Public Health, and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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24
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Ramakrishnan A, Piehl N, Simonton B, Parikh M, Zhang Z, Teregulova V, van Olst L, Gate D. Epigenetic dysregulation in Alzheimer's disease peripheral immunity. Neuron 2024; 112:1235-1248.e5. [PMID: 38340719 PMCID: PMC11031321 DOI: 10.1016/j.neuron.2024.01.013] [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: 03/14/2023] [Revised: 11/10/2023] [Accepted: 01/10/2024] [Indexed: 02/12/2024]
Abstract
The peripheral immune system in Alzheimer's disease (AD) has not been thoroughly studied with modern sequencing methods. To investigate epigenetic and transcriptional alterations to the AD peripheral immune system, we used single-cell sequencing strategies, including assay for transposase-accessible chromatin and RNA sequencing. We reveal a striking amount of open chromatin in peripheral immune cells in AD. In CD8 T cells, we uncover a cis-regulatory DNA element co-accessible with the CXC motif chemokine receptor 3 gene promoter. In monocytes, we identify a novel AD-specific RELA transcription factor binding site adjacent to an open chromatin region in the nuclear factor kappa B subunit 2 gene. We also demonstrate apolipoprotein E genotype-dependent epigenetic changes in monocytes. Surprisingly, we also identify differentially accessible chromatin regions in genes associated with sporadic AD risk. Our findings provide novel insights into the complex relationship between epigenetics and genetic risk factors in AD peripheral immunity.
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Affiliation(s)
- Abhirami Ramakrishnan
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Natalie Piehl
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Brooke Simonton
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Milan Parikh
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Ziyang Zhang
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Victoria Teregulova
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Lynn van Olst
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - David Gate
- The Ken & Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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25
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Yi H, Yang Q, Repaci C, Lee CM, Heo G, Timsina J, Gorijala P, Yang C, Budde J, Wang L, Cruchaga C, Sung YJ. TOPMed imputed genomics enhances genomic atlas of the human proteome in brain, cerebrospinal fluid, and plasma. Sci Data 2024; 11:387. [PMID: 38627416 PMCID: PMC11021418 DOI: 10.1038/s41597-024-03140-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Accepted: 03/14/2024] [Indexed: 04/19/2024] Open
Abstract
Comprehensive expression quantitative trait loci studies have been instrumental for understanding tissue-specific gene regulation and pinpointing functional genes for disease-associated loci in a tissue-specific manner. Compared to gene expressions, proteins more directly affect various biological processes, often dysregulated in disease, and are important drug targets. We previously performed and identified tissue-specific protein quantitative trait loci in brain, cerebrospinal fluid, and plasma. We now enhance this work by analyzing more proteins (1,300 versus 1,079) and an almost twofold increase in high quality imputed genetic variants (8.4 million versus 4.4 million) by using TOPMed reference panel. We identified 38 genomic regions associated with 43 proteins in brain, 150 regions associated with 247 proteins in cerebrospinal fluid, and 95 regions associated with 145 proteins in plasma. Compared to our previous study, this study newly identified 12 loci in brain, 30 loci in cerebrospinal fluid, and 22 loci in plasma. Our improved genomic atlas uncovers the genetic control of protein regulation across multiple tissues. These resources are accessible through the Online Neurodegenerative Trait Integrative Multi-Omics Explorer for use by the scientific community.
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Affiliation(s)
- Heng Yi
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Qijun Yang
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Charlie Repaci
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
| | - Cheolmin Matthew Lee
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Institute for Informatics, Washington University School of Medicine, St. Louis, MO, USA
| | - Gyujin Heo
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Priyanka Gorijala
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - John Budde
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Carlos Cruchaga
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurologic Diseases, Washington University, St. Louis, MO, USA
| | - Yun Ju Sung
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA.
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA.
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26
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Boumali R, Urli L, Naim M, Soualmia F, Kinugawa K, Petropoulos I, El Amri C. Kallikrein-related peptidase's significance in Alzheimer's disease pathogenesis: A comprehensive survey. Biochimie 2024:S0300-9084(24)00076-2. [PMID: 38608749 DOI: 10.1016/j.biochi.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 03/19/2024] [Accepted: 04/04/2024] [Indexed: 04/14/2024]
Abstract
Alzheimer's disease (AD) and related dementias constitute an important global health challenge. Detailed understanding of the multiple molecular mechanisms underlying their pathogenesis constitutes a clue for the management of the disease. Kallikrein-related peptidases (KLKs), a lead family of serine proteases, have emerged as potential biomarkers and therapeutic targets in the context of AD and associated cognitive decline. Hence, KLKs were proposed to display multifaceted impacts influencing various aspects of neurodegeneration, including amyloid-beta aggregation, tau pathology, neuroinflammation, and synaptic dysfunction. We propose here a comprehensive survey to summarize recent findings, providing an overview of the main kallikreins implicated in AD pathophysiology namely KLK8, KLK6 and KLK7. We explore the interplay between KLKs and key AD molecular pathways, shedding light on their significance as potential biomarkers for early disease detection. We also discuss their pertinence as therapeutic targets for disease-modifying interventions to develop innovative therapeutic strategies aimed at halting or ameliorating the progression of AD and associated dementias.
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Affiliation(s)
- Rilès Boumali
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France
| | - Laureline Urli
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France
| | - Meriem Naim
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France
| | - Feryel Soualmia
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France
| | - Kiyoka Kinugawa
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France; AP-HP, Paris, France; Charles-Foix Hospital, Functional Exploration Unit for Older Patients, 94200 Ivry-sur-Seine, France
| | - Isabelle Petropoulos
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France.
| | - Chahrazade El Amri
- Sorbonne Université, Faculty of Sciences and Engineering, IBPS, UMR 8256, CNRS-SU, ERL INSERM U1164, Biological Adaptation and Ageing, F-75252, Paris, France. Paris, France.
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27
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Toikumo S, Vickers-Smith R, Jinwala Z, Xu H, Saini D, Hartwell EE, Pavicic M, Sullivan KA, Xu K, Jacobson DA, Gelernter J, Rentsch CT, Stahl E, Cheatle M, Zhou H, Waxman SG, Justice AC, Kember RL, Kranzler HR. A multi-ancestry genetic study of pain intensity in 598,339 veterans. Nat Med 2024; 30:1075-1084. [PMID: 38429522 DOI: 10.1038/s41591-024-02839-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Accepted: 01/27/2024] [Indexed: 03/03/2024]
Abstract
Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, β-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.
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Affiliation(s)
- Sylvanus Toikumo
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Rachel Vickers-Smith
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Epidemiology and Environmental Health, University of Kentucky College of Public Health, Lexington, KY, USA
| | - Zeal Jinwala
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Heng Xu
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Divya Saini
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Emily E Hartwell
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Mirko Pavicic
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Kyle A Sullivan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Ke Xu
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Daniel A Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA
| | - Joel Gelernter
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Christopher T Rentsch
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
- London School of Hygiene & Tropical Medicine, London, UK
| | - Eli Stahl
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Martin Cheatle
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Hang Zhou
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Section of Biomedical Informatics and Data Science, Yale University School of Medicine, New Haven, CT, USA
| | - Stephen G Waxman
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Amy C Justice
- Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Department of Medicine, Yale University School of Medicine, New Haven, CT, USA
- Yale University School of Public Health, New Haven, CT, USA
| | - Rachel L Kember
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Henry R Kranzler
- Mental Illness Research, Education and Clinical Center, Crescenz VAMC, Philadelphia, PA, USA.
- Department of Psychiatry, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
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28
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Li J, Ma J, Liu M, Li M, Zhang M, Yin W, Wu M, Li X, Zhang Q, Zhang H, Zheng H, Mao C, Sun J, Wang W, Lyu W, Yue X, Weng W, Li J, Chen F, Zhu Y, Leng L. Large-Scale Proteome Profiling Identifies Biomarkers Associated with Suspected Neurosyphilis Diagnosis. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307744. [PMID: 38380496 DOI: 10.1002/advs.202307744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 02/01/2024] [Indexed: 02/22/2024]
Abstract
Neurosyphilis (NS) is a central nervous system (CNS) infection caused by Treponema pallidum (T. pallidum). NS can occur at any stage of syphilis and manifests as a broad spectrum of clinical symptoms. Often referred to as "the great imitator," NS can be easily overlooked or misdiagnosed due to the absence of standard diagnostic tests, potentially leading to severe and irreversible organ dysfunction. In this study, proteomic and machine learning model techniques are used to characterize 223 cerebrospinal fluid (CSF) samples to identify diagnostic markers of NS and provide insights into the underlying mechanisms of the associated inflammatory responses. Three biomarkers (SEMA7A, SERPINA3, and ITIH4) are validated as contributors to NS diagnosis through multicenter verification of an additional 115 CSF samples. We anticipate that the identified biomarkers will become effective tools for assisting in diagnosis of NS. Our insights into NS pathogenesis in brain tissue may inform therapeutic strategies and drug discoveries for NS patients.
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Affiliation(s)
- Jun Li
- Department of Dermatology, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, 100730, China
- Stem cell and Regenerative Medicine Lab, Department of Medical Science Research Center, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Translational Medicine Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Jie Ma
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - MingJuan Liu
- Department of Dermatology, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, 100730, China
| | - Mansheng Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Ming Zhang
- Department of Dermatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Wenhao Yin
- The First Hospital of Jiaxing, The Affiliated Hospital of Jiaxing University, Zhejiang, 314001, China
| | - Mengyin Wu
- Department of Dermatology, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, 100730, China
| | - Xiao Li
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
| | - Qiyu Zhang
- Department of Dermatology, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, 100730, China
- Stem cell and Regenerative Medicine Lab, Department of Medical Science Research Center, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Translational Medicine Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Hanlin Zhang
- Department of Dermatology, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, 100730, China
| | - Heyi Zheng
- Department of Dermatology, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases, Beijing, 100730, China
| | - Chenhui Mao
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Science, Beijing, 100730, China
| | - Jian Sun
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wenze Wang
- Department of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Wei Lyu
- Department of Infectious Disease, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China
| | - Xueping Yue
- Department of Dermatology and Venereology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100050, China
| | - Wenjia Weng
- Department of Dermatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Juan Li
- Department of Dermatology, Beijing Youan Hospital, Capital Medical University, Beijing, 100069, China
| | - Fengxin Chen
- Infections Disease Center, Beijing Ditan Hospital, Capital Medical University, Beijing, 100102, China
| | - Yunping Zhu
- State Key Laboratory of Medical Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, 102206, China
- Basic Medical School, Anhui Medical University, Anhui, 230032, China
| | - Ling Leng
- Stem cell and Regenerative Medicine Lab, Department of Medical Science Research Center, Institute of Clinical Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Translational Medicine Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China
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29
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Eteleeb AM, Novotny BC, Tarraga CS, Sohn C, Dhungel E, Brase L, Nallapu A, Buss J, Farias F, Bergmann K, Bradley J, Norton J, Gentsch J, Wang F, Davis AA, Morris JC, Karch CM, Perrin RJ, Benitez BA, Harari O. Brain high-throughput multi-omics data reveal molecular heterogeneity in Alzheimer's disease. PLoS Biol 2024; 22:e3002607. [PMID: 38687811 PMCID: PMC11086901 DOI: 10.1371/journal.pbio.3002607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 05/10/2024] [Accepted: 03/28/2024] [Indexed: 05/02/2024] Open
Abstract
Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.
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Affiliation(s)
- Abdallah M. Eteleeb
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
| | - Brenna C. Novotny
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Carolina Soriano Tarraga
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Christopher Sohn
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Eliza Dhungel
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, United States of America
| | - Logan Brase
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Aasritha Nallapu
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Jared Buss
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
| | - Fabiana Farias
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Kristy Bergmann
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Joseph Bradley
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Joanne Norton
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Jen Gentsch
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Fengxian Wang
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
| | - Albert A. Davis
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - John C. Morris
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - Celeste M. Karch
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- NeuroGenomics and Informatics Center, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
| | - Richard J. Perrin
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Department of Neurology, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
- Department of Pathology and Immunology, Washington University, St. Louis, Missouri, United States of America
| | - Bruno A. Benitez
- Department of Neurology and Neuroscience, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Oscar Harari
- Department of Psychiatry, Washington University, Saint Louis, St. Louis, Missouri, United States of America
- The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University, St. Louis, Missouri, United States of America
- Hope Center for Neurological Disorders, Washington University, St. Louis, Missouri, United States of America
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Hu T, Dai Q, Epstein MP, Yang J. Proteome-wide association studies using summary proteomic data identified 23 risk genes of Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.28.24305044. [PMID: 38585769 PMCID: PMC10996749 DOI: 10.1101/2024.03.28.24305044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Characterizing the genetic mechanisms underlying Alzheimer's disease (AD) dementia is crucial for developing new therapeutics. Proteome-wide association study (PWAS) integrating proteomics data with genome-wide association study (GWAS) summary data was shown as a powerful tool for detecting risk genes. The identified PWAS risk genes can be interpretated as having genetic effects mediated through the genetically regulated protein abundances. Existing PWAS analyses of AD often rely on the availability of individual-level proteomics and genetics data of a reference cohort. Leveraging summary-level protein quantitative trait loci (pQTL) reference data of multiple relevant tissues is expected to improve PWAS findings for studying AD. Here, we applied our recently developed OTTERS tool to conduct PWAS of AD dementia, by leveraging summary-level pQTL data of brain, cerebrospinal fluid (CSF), and plasma tissues, and multiple statistical methods. For each target protein, imputation models of the protein abundance with genetic predictors were trained from summary-level pQTL data, estimating a set of pQTL weights for considered genetic predictors. PWAS p-values were obtained by integrating GWAS summary data of AD dementia with estimated pQTL weights. PWAS p-values from multiple statistical methods were combined by the aggregated Cauchy association test to yield one omnibus PWAS p-value for the target protein. We identified significant PWAS risk genes through omnibus PWAS p-values and analyzed their protein-protein interactions using STRING. Their potential causal effects were assessed by the probabilistic Mendelian randomization (PMR-Egger). As a result, we identified a total of 23 significant PWAS risk genes for AD dementia in brain, CSF, and plasma tissues, including 7 novel findings. We showed that 15 of these risk genes were interconnected within a protein-protein interaction network involving the well-known AD risk gene of APOE and 5 novel findings, and enriched in immune functions and lipids pathways including positive regulation of immune system process, positive regulation of macrophage proliferation, humoral immune response, and high-density lipoprotein particle clearance. Existing biological evidence was found to relate our novel findings with AD. We validated the mediated causal effects of 14 risk genes (60.8%). In conclusion, we identified both known and novel PWAS risk genes, providing novel insights into the genetic mechanisms in brain, CSF, and plasma tissues, and targeted therapeutics development of AD dementia. Our study also demonstrated the effectiveness of integrating public available summary-level pQTL data with GWAS summary data for mapping risk genes of complex human diseases.
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Affiliation(s)
- Tingyang Hu
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Division of Biostatistics and Bioinformatics, Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qile Dai
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
- Department of Biostatistics and Bioinformatics, Emory University School of Public Health, Atlanta, GA, 30322, USA
| | - Michael P. Epstein
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Jingjing Yang
- Center for Computational and Quantitative Genetics, Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, 30322, USA
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31
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Saloner R, Staffaroni A, Dammer E, Johnson ECB, Paolillo E, Wise A, Heuer H, Forsberg L, Lago AL, Webb J, Vogel J, Santillo A, Hansson O, Kramer J, Miller B, Li J, Loureiro J, Sivasankaran R, Worringer K, Seyfried N, Yokoyama J, Seeley W, Spina S, Grinberg L, VandeVrede L, Ljubenkov P, Bayram E, Bozoki A, Brushaber D, Considine C, Day G, Dickerson B, Domoto-Reilly K, Faber K, Galasko D, Geschwind D, Ghoshal N, Graff-Radford N, Hales C, Honig L, Hsiung GY, Huey E, Kornak J, Kremers W, Lapid M, Lee S, Litvan I, McMillan C, Mendez M, Miyagawa T, Pantelyat A, Pascual B, Paulson H, Petrucelli L, Pressman P, Ramos E, Rascovsky K, Roberson E, Savica R, Snyder A, Sullivan AC, Tartaglia C, Vandebergh M, Boeve B, Rosen H, Rojas J, Boxer A, Casaletto K. Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration. RESEARCH SQUARE 2024:rs.3.rs-4103685. [PMID: 38585969 PMCID: PMC10996789 DOI: 10.21203/rs.3.rs-4103685/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
The pathophysiological mechanisms driving disease progression of frontotemporal lobar degeneration (FTLD) and corresponding biomarkers are not fully understood. We leveraged aptamer-based proteomics (> 4,000 proteins) to identify dysregulated communities of co-expressed cerebrospinal fluid proteins in 116 adults carrying autosomal dominant FTLD mutations (C9orf72, GRN, MAPT) compared to 39 noncarrier controls. Network analysis identified 31 protein co-expression modules. Proteomic signatures of genetic FTLD clinical severity included increased abundance of RNA splicing (particularly in C9orf72 and GRN) and extracellular matrix (particularly in MAPT) modules, as well as decreased abundance of synaptic/neuronal and autophagy modules. The generalizability of genetic FTLD proteomic signatures was tested and confirmed in independent cohorts of 1) sporadic progressive supranuclear palsy-Richardson syndrome and 2) frontotemporal dementia spectrum syndromes. Network-based proteomics hold promise for identifying replicable molecular pathways in adults living with FTLD. 'Hub' proteins driving co-expression of affected modules warrant further attention as candidate biomarkers and therapeutic targets.
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Affiliation(s)
| | | | | | | | | | - Amy Wise
- University of California, San Francisco
| | | | | | | | | | | | | | | | | | | | - Jingyao Li
- Novartis Institutes for Biomedical Research, Inc
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Suzee Lee
- University of California, San Francisco
| | | | - Corey McMillan
- Department of Neurology, University of Pennsylvania, Philadelphia, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Adam Boxer
- Memory and Aging Center, Department of Neurology, University of California, San Francisco
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32
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Do AN, Ali M, Timsina J, Wang L, Western D, Liu M, Sanford J, Rosende-Roca M, Boada M, Puerta R, Wilson T, Ruiz A, Pastor P, Wyss-Coray T, Cruchaga C, Sung YJ. CSF proteomic profiling with amyloid/tau positivity identifies distinctive sex-different alteration of multiple proteins involved in Alzheimer's disease. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.15.24304164. [PMID: 38559166 PMCID: PMC10980123 DOI: 10.1101/2024.03.15.24304164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
In Alzheimer's disease (AD), the most common cause of dementia, females have higher prevalence and faster progression, but sex-specific molecular findings in AD are limited. Here, we comprehensively examined and validated 7,006 aptamers targeting 6,162 proteins in cerebral spinal fluid (CSF) from 2,077 amyloid/tau positive cases and controls to identify sex-specific proteomic signatures of AD. In discovery (N=1,766), we identified 330 male-specific and 121 female-specific proteomic alternations in CSF (FDR <0.05). These sex-specific proteins strongly predicted amyloid/tau positivity (AUC=0.98 in males; 0.99 in females), significantly higher than those with age, sex, and APOE-ε4 (AUC=0.85). The identified sex-specific proteins were well validated (r≥0.5) in the Stanford study (N=108) and Emory study (N=148). Biological follow-up of these proteins led to sex differences in cell-type specificity, pathways, interaction networks, and drug targets. Male-specific proteins, enriched in astrocytes and oligodendrocytes, were involved in postsynaptic and axon-genesis. The male network exhibited direct connections among 152 proteins and highlighted PTEN, NOTCH1, FYN, and MAPK8 as hubs. Drug target suggested melatonin (used for sleep-wake cycle regulation), nabumetone (used for pain), daunorubicin, and verteporfin for treating AD males. In contrast, female-specific proteins, enriched in neurons, were involved in phosphoserine residue binding including cytokine activities. The female network exhibits strong connections among 51 proteins and highlighted JUN and 14-3-3 proteins (YWHAG and YWHAZ) as hubs. Drug target suggested biperiden (for muscle control of Parkinson's disease), nimodipine (for cerebral vasospasm), quinostatin and ethaverine for treating AD females. Together, our findings provide mechanistic understanding of sex differences for AD risk and insights into clinically translatable interventions.
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Affiliation(s)
- Anh N Do
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
| | - Muhammad Ali
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Jigyasha Timsina
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Lihua Wang
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel Western
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Menghan Liu
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Jessie Sanford
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Matitee Rosende-Roca
- Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Merce Boada
- Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Raquel Puerta
- Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Ted Wilson
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Agustin Ruiz
- Research Center and Memory clinic Fundació ACE. Institut Català de Neurociències Aplicades, Universitat Internacional de Catalunya, Barcelona, Spain
| | - Pau Pastor
- Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain
| | - Tony Wyss-Coray
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
| | - Carlos Cruchaga
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
- Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA
| | - Yun Ju Sung
- Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
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33
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Chen S, Huang W, Wan Q, Tang Z, Li X, Zeng F, Zheng S, Li Z, Liu X. Investigation of the acute pathogenesis of spondyloarthritis/HLA-B27-associated anterior uveitis based on genome-wide association analysis and single-cell transcriptomics. J Transl Med 2024; 22:271. [PMID: 38475831 PMCID: PMC10936029 DOI: 10.1186/s12967-024-05077-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 03/07/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Patients with spondyloarthritis (SpA)/HLA-B27-associated acute anterior uveitis (AAU) experience recurring acute flares, which pose significant visual and financial challenges. Despite established links between SpA and HLA-B27-associated AAU, the exact mechanism involved remains unclear, and further understanding is needed for effective prevention and treatment. METHODS To investigate the acute pathogenesis of SpA/HLA-B27-associated AAU, Mendelian randomization (MR) and single-cell transcriptomic analyses were employed. The MR incorporated publicly available protein quantitative trait locus data from previous studies, along with genome-wide association study data from public databases. Causal relationships between plasma proteins and anterior uveitis were assessed using two-sample MR. Additionally, colocalization analysis was performed using Bayesian colocalization. Single-cell transcriptome analysis utilized the anterior uveitis dataset from the Gene Expression Omnibus (GEO) database. Dimensionality reduction, clustering, transcription factor analysis, pseudotime analysis, and cell communication analysis were subsequently conducted to explore the underlying mechanisms involved. RESULTS Mendelian randomization analysis revealed that circulating levels of AIF1 and VARS were significantly associated with a reduced risk of developing SpA/HLA-B27-associated AAU, with AIF1 showing a robust correlation with anterior uveitis onset. Colocalization analysis supported these findings. Single-cell transcriptome analysis showed predominant AIF1 expression in myeloid cells, which was notably lower in the HLA-B27-positive group. Pseudotime analysis revealed dendritic cell terminal positions in differentiation branches, accompanied by gradual decreases in AIF1 expression. Based on cell communication analysis, CD141+CLEC9A+ classic dendritic cells (cDCs) and the APP pathway play crucial roles in cellular communication in the Spa/HLA-B27 group. CONCLUSIONS AIF1 is essential for the pathogenesis of SpA/HLA-B27-associated AAU. Myeloid cell differentiation into DCs and decreased AIF1 levels are also pivotal in this process.
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Affiliation(s)
- Shuming Chen
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China
| | - Weidi Huang
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China
| | - Qiaoqian Wan
- Department of Anaesthesiology, the Second Xiangya Hospital, Central South University, Changsha, 410011, China
| | - Zichun Tang
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China
| | - Xie Li
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China
| | - Fang Zeng
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China
| | - Shuyan Zheng
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China
| | - Zhuo Li
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China.
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China.
- Hunan Provincial Key Laboratory of Critical Quality Attribute of Cell Therapy Products, Changsha, 410011, Hunan, China.
| | - Xiao Liu
- Department of Ophthalmology, The Second Xiangya Hospital, Central South University, Changsha, 410011, China.
- Hunan Clinical Research Center of Ophthalmic Disease, Changsha, 410011, Hunan, China.
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34
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Wittich H, Ardlie K, Taylor KD, Durda P, Liu Y, Mikhaylova A, Gignoux CR, Cho MH, Rich SS, Rotter JI, Manichaikul A, Im HK, Wheeler HE. Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits. Am J Hum Genet 2024; 111:445-455. [PMID: 38320554 PMCID: PMC10940016 DOI: 10.1016/j.ajhg.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/12/2024] [Accepted: 01/12/2024] [Indexed: 02/08/2024] Open
Abstract
Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR < 0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R = 0.17) than observed expression (R = 0.10, p = 7.50 × 10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics + environment) and is useful in uncovering the function of SNPs associated with complex traits.
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Affiliation(s)
- Henry Wittich
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA
| | - Kristin Ardlie
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kent D Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Peter Durda
- Laboratory for Clinical Biochemistry Research, University of Vermont, Colchester, VT 05446, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC 27710, USA
| | - Anna Mikhaylova
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Chris R Gignoux
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Ani Manichaikul
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA
| | - Hae Kyung Im
- Section of Genetic Medicine, The University of Chicago, Chicago, IL 60637, USA
| | - Heather E Wheeler
- Program in Bioinformatics, Loyola University Chicago, Chicago, IL 60660, USA; Department of Biology, Loyola University Chicago, Chicago, IL 60660, USA.
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35
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Gagnon E, Daghlas I, Zagkos L, Sargurupremraj M, Georgakis MK, Anderson CD, Cronje HT, Burgess S, Arsenault BJ, Gill D. Mendelian Randomization Applied to Neurology: Promises and Challenges. Neurology 2024; 102:e209128. [PMID: 38261980 PMCID: PMC7615637 DOI: 10.1212/wnl.0000000000209128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 11/16/2023] [Indexed: 01/25/2024] Open
Abstract
The Mendelian randomization (MR) paradigm allows for causal inferences to be drawn using genetic data. In recent years, the expansion of well-powered publicly available genetic association data related to phenotypes such as brain tissue gene expression, brain imaging, and neurologic diseases offers exciting opportunities for the application of MR in the field of neurology. In this review, we discuss the basic principles of MR, its myriad applications to research in neurology, and potential pitfalls of injudicious applications. Throughout, we provide examples where MR-informed findings have shed light on long-standing epidemiologic controversies, provided insights into the pathophysiology of neurologic conditions, prioritized drug targets, and informed drug repurposing opportunities. With the ever-expanding availability of genome-wide association data, we project MR to become a key driver of progress in the field of neurology. It is therefore paramount that academics and clinicians within the field are familiar with the approach.
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Affiliation(s)
- Eloi Gagnon
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Iyas Daghlas
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Loukas Zagkos
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Muralidharan Sargurupremraj
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Marios K Georgakis
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Christopher D Anderson
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Helene T Cronje
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Stephen Burgess
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Benoit J Arsenault
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
| | - Dipender Gill
- From the Quebec Heart and Lung Institute (E.G., B.J.A.), Laval University, Quebec, Canada; Department of Neurology (I.D.), University of California San Francisco; Department of Epidemiology and Biostatistics (L.Z., D.G.), School of Public Health, Imperial College London, United Kingdom; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases (M.S.), University of Texas Health Sciences Center, San Antonio; Broad Institute of MIT and Harvard (M.K.G., C.D.A.), Cambridge, MA; Institute for Stroke and Dementia Research (ISD) (M.K.G.), University Hospital, LMU Munich, Germany; Center for Genomic Medicine (C.D.A.), Massachusetts General Hospital; Department of Neurology (C.D.A.), Brigham and Women's Hospital, Boston, MA; Department of Public Health (H.T.C.), Section of Epidemiology, University of Copenhagen, Denmark; MRC Biostatistics Unit (S.B.), and Cardiovascular Epidemiology Unit (S.B.), Department of Public Health and Primary Care, University of Cambridge, United Kingdom; and Department of Medicine (B.J.A.), Faculty of Medicine, Université Laval, Québec, Canada
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Cruchaga C, Ali M, Shen Y, Do A, Wang L, Western D, Liu M, Beric A, Budde J, Gentsch J, Schindler S, Morris J, Holtzman D, Fernández M, Ruiz A, Alvarez I, Aguilar M, Pastor P, Rutledge J, Oh H, Wilson E, Le Guen Y, Khalid R, Robins C, Pulford D, Ibanez L, Wyss-Coray T, Ju Sung Y. Multi-cohort cerebrospinal fluid proteomics identifies robust molecular signatures for asymptomatic and symptomatic Alzheimer's disease. RESEARCH SQUARE 2024:rs.3.rs-3631708. [PMID: 38410465 PMCID: PMC10896368 DOI: 10.21203/rs.3.rs-3631708/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Changes in Amyloid-β (A), hyperphosphorylated Tau (T) in brain and cerebrospinal fluid (CSF) precedes AD symptoms, making CSF proteome a potential avenue to understand the pathophysiology and facilitate reliable diagnostics and therapies. Using the AT framework and a three-stage study design (discovery, replication, and meta-analysis), we identified 2,173 proteins dysregulated in AD, that were further validated in a third totally independent cohort. Machine learning was implemented to create and validate highly accurate and replicable (AUC>0.90) models that predict AD biomarker positivity and clinical status. These models can also identify people that will convert to AD and those AD cases with faster progression. The associated proteins cluster in four different protein pseudo-trajectories groups spanning the AD continuum and were enrichment in specific pathways including neuronal death, apoptosis and tau phosphorylation (early stages), microglia dysregulation and endolysosomal dysfuncton(mid-stages), brain plasticity and longevity (mid-stages) and late microglia-neuron crosstalk (late stages).
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Affiliation(s)
| | | | | | - Anh Do
- Washington University School of Medicine
| | - Lihua Wang
- Washington University School of Medicine
| | - Daniel Western
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | | | | | | | | | | | | | | | | | | | - Ignacio Alvarez
- Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona, Spain
| | | | - Pau Pastor
- University Hospital Germans Trias i Pujol
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37
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Zou X, Wang L, Wang S, Zhang Y, Ma J, Chen L, Li Y, Yao TX, Zhou H, Wu L, Tang Q, Ma S, Zhang X, Tang R, Yi Y, Liu R, Zeng Y, Zhang L. Promising therapeutic targets for ischemic stroke identified from plasma and cerebrospinal fluid proteomes: a multicenter Mendelian randomization study. Int J Surg 2024; 110:766-776. [PMID: 38016292 PMCID: PMC10871597 DOI: 10.1097/js9.0000000000000922] [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: 08/21/2023] [Accepted: 11/09/2023] [Indexed: 11/30/2023]
Abstract
BACKGROUND Ischemic stroke (IS) is more common every year, the condition is serious, and have a poor prognosis. New, efficient, and safe therapeutic targets are desperately needed as early treatment especially prevention and reperfusion is the key to lowering the occurrence of poorer prognosis. Generally circulating proteins are attractive therapeutic targets, this study aims to identify potential pharmacological targets among plasma and cerebrospinal fluid (CSF) proteins for the prevention and treatment of IS using a multicenter Mendelian randomization (MR) approach. METHODS First, the genetic instruments of 734 plasma and 151 CSF proteins were assessed for causative connections with IS from MEGASTROKE consortium by MR to identify prospective therapeutic targets. Then, for additional validation, plasma proteins from the deCODE consortium and the Fenland consortium, as well as IS GWAS data from the FinnGen cohort, the ISGC consortium and UK biobank, were employed. A thorough evaluation of the aforementioned possible pharmacological targets was carried out using meta-analysis. The robustness of MR results was then confirmed through sensitivity analysis using several techniques, such as bidirectional MR analysis, Steiger filtering, and Bayesian colocalization. Finally, methods like Protein-Protein Interaction (PPI) Networking were utilized to investigate the relationship between putative drug targets and therapeutic agents. RESULTS The authors discovered three proteins that may function as promising therapeutic targets for IS and meet the Bonferroni correction ( P <0.05/885=5.65×10 -5 ). Prekallikrein (OR=0.41, 95% CI: 0.27-0.63, P =3.61×10 -5 ), a protein found in CSF, has a 10-fold protective impact in IS, while the plasma proteins SWAP70 (OR=0.85, 95% CI: 0.80-0.91, P =1.64×10 -6 ) and MMP-12 (OR=0.92, 95% CI: 0.89-0.95, P =4.49×10 -6 ) of each SD play a protective role in IS. Prekallikrein, MMP-12, SWAP70 was replicated in the FinnGen cohort and ISGC database. MMP-12 (OR=0.93, 95% CI: 0.91-0.94, P <0.001), SWAP70 (OR=0.92, 95% CI: 0.90-0.94, P <0.001), and prekallikrein (OR=0.53, 95% CI: 0.33-0.72, P <0.001) may all be viable targets for IS, according to the combined meta-analysis results. Additionally, no evidence of reverse causality was identified, and Bayesian colocalization revealed MMP-12 (PPH 4 =0.995), SWAP70 (PPH 4 =0.987), and prekallikrein (PPH 4 =0.894) shared the same variant with IS, supporting the robustness of the aforementioned causation. Prekallikrein and MMP-12 were associated with the target protein of the current treatment of IS. Among them, Lanadelumab, a new drug whose target protein is a prekallikrein, may be a promising new drug for the treatment of IS. CONCLUSION The prekallikrein, MMP-12, and SWAP70 are causally associated with the risk of IS. Moreover, MMP-12 and prekallikrein may be treated as promising therapeutic targets for medical intervention of IS.
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Affiliation(s)
- Xuelun Zou
- Department of Neurology, Xiangya Hospital, Central South University
| | - Leiyun Wang
- Department of Pharmacy, Wuhan No.1 Hospital, Wuhan, Hubei, People’s Republic of China
| | - Sai Wang
- Department of Neurology, Xiangya Hospital, Central South University
| | - Yupeng Zhang
- Department of Neurology, Xiangya Hospital, Central South University
| | - Junyi Ma
- Department of Neurology, Xiangya Hospital, Central South University
| | - Lei Chen
- Department of Neurology, Xiangya Hospital, Central South University
| | - Ye Li
- Department of Neurology, Xiangya Hospital, Central South University
| | - Tian-Xing Yao
- Department of Neurology, Xiangya Hospital, Central South University
| | - Huifang Zhou
- Department of Neurology, Xiangya Hospital, Central South University
| | - Lianxu Wu
- Department of Neurology, Xiangya Hospital, Central South University
| | - Qiaoling Tang
- Department of Neurology, Xiangya Hospital, Central South University
| | - Siyuan Ma
- Department of Neurology, Xiangya Hospital, Central South University
| | - Xiangbin Zhang
- Department of Neurology, Xiangya Hospital, Central South University
| | - Rongmei Tang
- Department of Neurology, Xiangya Hospital, Central South University
| | - Yexiang Yi
- Department of Neurology, Xiangya Hospital, Central South University
| | - Ran Liu
- Department of Neurology, Xiangya Hospital, Central South University
| | - Yi Zeng
- Department of Geriatrics, Second Xiangya Hospital
| | - Le Zhang
- Department of Neurology, Xiangya Hospital, Central South University
- Human Brain Disease Biological Resources Platform of Hunan Province, Xiangya Hospital
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital
- Multi-Modal Monitoring Technology for Severe Cerebrovascular Disease of Human Engineering Research Center, Xiangya Hospital
- Brain Health Center of Hunan Province, Xiangya Hospital, Central South University, Changsha, Hunan
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38
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de La Harpe R, Zagkos L, Gill D, Cronjé HT, Karhunen V. Cerebrospinal and Brain Proteins Implicated in Neuropsychiatric and Risk Factor Traits: Evidence from Mendelian Randomization. Biomedicines 2024; 12:327. [PMID: 38397929 PMCID: PMC10886978 DOI: 10.3390/biomedicines12020327] [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: 01/06/2024] [Revised: 01/25/2024] [Accepted: 01/27/2024] [Indexed: 02/25/2024] Open
Abstract
Neuropsychiatric disorders present a global health challenge, necessitating an understanding of their molecular mechanisms for therapeutic development. Using Mendelian randomization (MR) analysis, this study explored associations between genetically predicted levels of 173 proteins in cerebrospinal fluid (CSF) and 25 in the brain with 14 neuropsychiatric disorders and risk factors. Follow-up analyses assessed consistency across plasma protein levels and gene expression in various brain regions. Proteins were instrumented using tissue-specific genetic variants, and colocalization analysis confirmed unbiased gene variants. Consistent MR and colocalization evidence revealed that lower cortical expression of low-density lipoprotein receptor-related protein 8, coupled higher abundance in the CSF and plasma, associated with lower fluid intelligence scores and decreased bipolar disorder risk. Additionally, elevated apolipoprotein-E2 and hepatocyte growth factor-like protein in the CSF and brain were related to reduced leisure screen time and lower odds of physical activity, respectively. Furthermore, elevated CSF soluble tyrosine-protein kinase receptor 1 level increased liability to attention deficit hyperactivity disorder and schizophrenia alongside lower fluid intelligence scores. This research provides genetic evidence supporting novel tissue-specific proteomic targets for neuropsychiatric disorders and their risk factors. Further exploration is necessary to understand the underlying biological mechanisms and assess their potential for therapeutic intervention.
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Affiliation(s)
- Roxane de La Harpe
- Unit of Internal Medicine, Department of Medicine, University Hospital of Lausanne, 1011 Lausanne, Switzerland
| | - Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (L.Z.); (D.G.)
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (L.Z.); (D.G.)
| | - Héléne T. Cronjé
- Department of Public Health, Section of Epidemiology, University of Copenhagen, 1165 Copenhagen, Denmark;
| | - Ville Karhunen
- Research Unit of Mathematical Sciences, Faculty of Science, University of Oulu, Fi-900014 Oulu, Finland;
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Fi-900014 Oulu, Finland
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39
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Shi JJ, Mao CY, Guo YZ, Fan Y, Hao XY, Li SJ, Tian J, Hu ZW, Li MJ, Li JD, Ma DR, Guo MN, Zuo CY, Liang YY, Xu YM, Yang J, Shi CH. Joint analysis of proteome, transcriptome, and multi-trait analysis to identify novel Parkinson's disease risk genes. Aging (Albany NY) 2024; 16:1555-1580. [PMID: 38240717 PMCID: PMC10866412 DOI: 10.18632/aging.205444] [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: 02/20/2023] [Accepted: 12/04/2023] [Indexed: 02/06/2024]
Abstract
Genome-wide association studies (GWAS) have identified multiple risk variants for Parkinson's disease (PD). Nevertheless, how the risk variants confer the risk of PD remains largely unknown. We conducted a proteome-wide association study (PWAS) and summary-data-based mendelian randomization (SMR) analysis by integrating PD GWAS with proteome and protein quantitative trait loci (pQTL) data from human brain, plasma and CSF. We also performed a large transcriptome-wide association study (TWAS) and Fine-mapping of causal gene sets (FOCUS), leveraging joint-tissue imputation (JTI) prediction models of 22 tissues to identify and prioritize putatively causal genes. We further conducted PWAS, SMR, TWAS, and FOCUS using a multi-trait analysis of GWAS (MTAG) to identify additional PD risk genes to boost statistical power. In this large-scale study, we identified 16 genes whose genetically regulated protein abundance levels were associated with Parkinson's disease risk. We undertook a large-scale analysis of PD and correlated traits, through TWAS and FOCUS studies, and discovered 26 casual genes related to PD that had not been reported in previous TWAS. 5 genes (CD38, GPNMB, RAB29, TMEM175, TTC19) showed significant associations with PD at both the proteome-wide and transcriptome-wide levels. Our study provides new insights into the etiology and underlying genetic architecture of PD.
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Affiliation(s)
- Jing-Jing Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Cheng-Yuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Ya-Zhou Guo
- School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China
| | - Yu Fan
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Xiao-Yan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Shuang-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Jie Tian
- Zhengzhou Railway Vocational and Technical College, Zhengzhou 450000, Henan, China
| | - Zheng-Wei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Meng-Jie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Jia-Di Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Dong-Rui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Meng-Nan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Chun-Yan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Yuan-Yuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Yu-Ming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou 450000, Henan, China
| | - Jian Yang
- School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang, China
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, Zhejiang, China
| | - Chang-He Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou 450000, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou 450000, Henan, China
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40
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Shen Y, Ali M, Timsina J, Wang C, Do A, Western D, Liu M, Gorijala P, Budde J, Liu H, Gordon B, McDade E, Morris JC, Llibre-Guerra JJ, Bateman RJ, Joseph-Mathurin N, Perrin RJ, Maschi D, Wyss-Coray T, Pastor P, Goate A, Renton AE, Surace EI, Johnson ECB, Levey AI, Alvarez I, Levin J, Ringman JM, Allegri RF, Seyfried N, Day GS, Wu Q, Fernández MV, Ibanez L, Sung YJ, Cruchaga C. Systematic proteomics in Autosomal dominant Alzheimer's disease reveals decades-early changes of CSF proteins in neuronal death, and immune pathways. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301242. [PMID: 38260583 PMCID: PMC10802763 DOI: 10.1101/2024.01.12.24301242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background To date, there is no high throughput proteomic study in the context of Autosomal Dominant Alzheimer's disease (ADAD). Here, we aimed to characterize early CSF proteome changes in ADAD and leverage them as potential biomarkers for disease monitoring and therapeutic strategies. Methods We utilized Somascan® 7K assay to quantify protein levels in the CSF from 291 mutation carriers (MCs) and 185 non-carriers (NCs). We employed a multi-layer regression model to identify proteins with different pseudo-trajectories between MCs and NCs. We replicated the results using publicly available ADAD datasets as well as proteomic data from sporadic Alzheimer's disease (sAD). To biologically contextualize the results, we performed network and pathway enrichment analyses. Machine learning was applied to create and validate predictive models. Findings We identified 125 proteins with significantly different pseudo-trajectories between MCs and NCs. Twelve proteins showed changes even before the traditional AD biomarkers (Aβ42, tau, ptau). These 125 proteins belong to three different modules that are associated with age at onset: 1) early stage module associated with stress response, glutamate metabolism, and mitochondria damage; 2) the middle stage module, enriched in neuronal death and apoptosis; and 3) the presymptomatic stage module was characterized by changes in microglia, and cell-to-cell communication processes, indicating an attempt of rebuilding and establishing new connections to maintain functionality. Machine learning identified a subset of nine proteins that can differentiate MCs from NCs better than traditional AD biomarkers (AUC>0.89). Interpretation Our findings comprehensively described early proteomic changes associated with ADAD and captured specific biological processes that happen in the early phases of the disease, fifteen to five years before clinical onset. We identified a small subset of proteins with the potentials to become therapy-monitoring biomarkers of ADAD MCs. Funding Proteomic data generation was supported by NIH: RF1AG044546.
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41
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Zhu J, Liu S, Walker KA, Zhong H, Ghoneim DH, Zhang Z, Surendran P, Fahle S, Butterworth A, Alam MA, Deng HW, Wu C, Wu L. Associations between genetically predicted plasma protein levels and Alzheimer's disease risk: a study using genetic prediction models. Alzheimers Res Ther 2024; 16:8. [PMID: 38212844 PMCID: PMC10782590 DOI: 10.1186/s13195-023-01378-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 12/30/2023] [Indexed: 01/13/2024]
Abstract
BACKGROUND Specific peripheral proteins have been implicated to play an important role in the development of Alzheimer's disease (AD). However, the roles of additional novel protein biomarkers in AD etiology remains elusive. The availability of large-scale AD GWAS and plasma proteomic data provide the resources needed for the identification of causally relevant circulating proteins that may serve as risk factors for AD and potential therapeutic targets. METHODS We established and validated genetic prediction models for protein levels in plasma as instruments to investigate the associations between genetically predicted protein levels and AD risk. We studied 71,880 (proxy) cases and 383,378 (proxy) controls of European descent. RESULTS We identified 69 proteins with genetically predicted concentrations showing associations with AD risk. The drugs almitrine and ciclopirox targeting ATP1A1 were suggested to have a potential for being repositioned for AD treatment. CONCLUSIONS Our study provides additional insights into the underlying mechanisms of AD and potential therapeutic strategies.
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Affiliation(s)
- Jingjing Zhu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Shuai Liu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Keenan A Walker
- Laboratory of Behavioral Neuroscience, National Institute On Aging, Intramural Research Program, Baltimore, MD, USA
| | - Hua Zhong
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Dalia H Ghoneim
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA
| | - Zichen Zhang
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Praveen Surendran
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Sarah Fahle
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Adam Butterworth
- MRC/BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
- NIHR Blood and Transplant Research Unit in Donor Health and Genomics, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Md Ashad Alam
- Tulane Center for Biomedical Informatics and Genomics., Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, 1440 Canal Street, New Orleans, LA, 70112, USA
- Center for Outcomes Research, Ochsner Clinic Foundation, New Orleans, LA, 70121, USA
| | - Hong-Wen Deng
- Tulane Center for Biomedical Informatics and Genomics., Division of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University, 1440 Canal Street, New Orleans, LA, 70112, USA
| | - Chong Wu
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Lang Wu
- Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, 96813, USA.
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Gudmundsdottir V, Frick E, Emilsson V, Jonmundsson T, Steindorsdottir A, Johnson ECB, Puerta R, Dammer E, Shantaraman A, Cano A, Boada M, Valero S, Garcia-Gonzalez P, Gudmundsson E, Gudjonsson A, Pitts R, Qiu X, Finkel N, Loureiro J, Orth A, Seyfried N, Levey A, Ruiz A, Aspelund T, Jennings L, Launer L, Gudnason V. Serum proteomics reveals APOE dependent and independent protein signatures in Alzheimer's disease. RESEARCH SQUARE 2024:rs.3.rs-3706206. [PMID: 38260284 PMCID: PMC10802738 DOI: 10.21203/rs.3.rs-3706206/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The current demand for early intervention, prevention, and treatment of late onset Alzheimer's disease (LOAD) warrants deeper understanding of the underlying molecular processes which could contribute to biomarker and drug target discovery. Utilizing high-throughput proteomic measurements in serum from a prospective population-based cohort of older adults (n = 5,294), we identified 303 unique proteins associated with incident LOAD (median follow-up 12.8 years). Over 40% of these proteins were associated with LOAD independently of APOE-ε4 carrier status. These proteins were implicated in neuronal processes and overlapped with protein signatures of LOAD in brain and cerebrospinal fluid. We found 17 proteins which LOAD-association was strongly dependent on APOE-ε4 carrier status. Most of them showed consistent associations with LOAD in cerebrospinal fluid and a third had brain-specific gene expression. Remarkably, four proteins in this group (TBCA, ARL2, S100A13 and IRF6) were downregulated by APOE-ε4 yet upregulated as a consequence of LOAD as determined in a bi-directional Mendelian randomization analysis, reflecting a potential response to the disease onset. Accordingly, the direct association of these proteins to LOAD was reversed upon APOE-ε4 genotype adjustment, a finding which we replicate in an external cohort (n = 719). Our findings provide an insight into the dysregulated pathways that may lead to the development and early detection of LOAD, including those both independent and dependent on APOE-ε4. Importantly, many of the LOAD-associated proteins we find in the circulation have been found to be expressed - and have a direct link with AD - in brain tissue. Thus, the proteins identified here, and their upstream modulating pathways, provide a new source of circulating biomarker and therapeutic target candidates for LOAD.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Merce Boada
- Research Center and Memory Clinic of Fundació ACE, Institut Català de Neurociències Aplicades-UIC, Barcelona
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Lenore Launer
- National Institute on Aging, National Institutes of Health
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Zagkos L, Dib MJ, Cronjé HT, Elliott P, Dehghan A, Tzoulaki I, Gill D, Daghlas I. Cerebrospinal Fluid C1-Esterase Inhibitor and Tie-1 Levels Affect Cognitive Performance: Evidence from Proteome-Wide Mendelian Randomization. Genes (Basel) 2024; 15:71. [PMID: 38254961 PMCID: PMC10815381 DOI: 10.3390/genes15010071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/29/2023] [Accepted: 01/01/2024] [Indexed: 01/24/2024] Open
Abstract
OBJECTIVE The association of cerebrospinal fluid (CSF) protein levels with cognitive function in the general population remains largely unexplored. We performed Mendelian randomization (MR) analyses to query which CSF proteins may have potential causal effects on cognitive performance. METHODS AND ANALYSIS Genetic associations with CSF proteins were obtained from a genome-wide association study conducted in up to 835 European-ancestry individuals and for cognitive performance from a meta-analysis of GWAS including 257,841 European-ancestry individuals. We performed Mendelian randomization (MR) analyses to test the effect of randomly allocated variation in 154 genetically predicted CSF protein levels on cognitive performance. Findings were validated by performing colocalization analyses and considering cognition-related phenotypes. RESULTS Genetically predicted C1-esterase inhibitor levels in the CSF were associated with a better cognitive performance (SD units of cognitive performance per 1 log-relative fluorescence unit (RFU): 0.23, 95% confidence interval: 0.12 to 0.35, p = 7.91 × 10-5), while tyrosine-protein kinase receptor Tie-1 (sTie-1) levels were associated with a worse cognitive performance (-0.43, -0.62 to -0.23, p = 2.08 × 10-5). These findings were supported by colocalization analyses and by concordant effects on distinct cognition-related and brain-volume measures. CONCLUSIONS Human genetics supports a role for the C1-esterase inhibitor and sTie-1 in cognitive performance.
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Affiliation(s)
- Loukas Zagkos
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
| | - Marie-Joe Dib
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Héléne T. Cronjé
- Department of Public Health, Section of Epidemiology, University of Copenhagen, 1165 Copenhagen, Denmark;
| | - Paul Elliott
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
- UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, London W1T 7NF, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Abbas Dehghan
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
- UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, London W1T 7NF, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - Ioanna Tzoulaki
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
- UK Dementia Research Institute at Imperial College London, Hammersmith Hospital, London W1T 7NF, UK
- Medical Research Council Centre for Environment and Health, School of Public Health, Imperial College London, London SW7 2AZ, UK
- Centre for Systems Biology, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
| | - Dipender Gill
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London SW7 2BX, UK; (P.E.); (A.D.); (I.T.); (D.G.)
| | - Iyas Daghlas
- Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA;
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Alvarado CX, Makarious MB, Weller CA, Vitale D, Koretsky MJ, Bandres-Ciga S, Iwaki H, Levine K, Singleton A, Faghri F, Nalls MA, Leonard HL. omicSynth: An open multi-omic community resource for identifying druggable targets across neurodegenerative diseases. Am J Hum Genet 2024; 111:150-164. [PMID: 38181731 PMCID: PMC10806756 DOI: 10.1016/j.ajhg.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/04/2023] [Accepted: 12/04/2023] [Indexed: 01/07/2024] Open
Abstract
Treatments for neurodegenerative disorders remain rare, but recent FDA approvals, such as lecanemab and aducanumab for Alzheimer disease (MIM: 607822), highlight the importance of the underlying biological mechanisms in driving discovery and creating disease modifying therapies. The global population is aging, driving an urgent need for therapeutics that stop disease progression and eliminate symptoms. In this study, we create an open framework and resource for evidence-based identification of therapeutic targets for neurodegenerative disease. We use summary-data-based Mendelian randomization to identify genetic targets for drug discovery and repurposing. In parallel, we provide mechanistic insights into disease processes and potential network-level consequences of gene-based therapeutics. We identify 116 Alzheimer disease, 3 amyotrophic lateral sclerosis (MIM: 105400), 5 Lewy body dementia (MIM: 127750), 46 Parkinson disease (MIM: 605909), and 9 progressive supranuclear palsy (MIM: 601104) target genes passing multiple test corrections (pSMR_multi < 2.95 × 10-6 and pHEIDI > 0.01). We created a therapeutic scheme to classify our identified target genes into strata based on druggability and approved therapeutics, classifying 41 novel targets, 3 known targets, and 115 difficult targets (of these, 69.8% are expressed in the disease-relevant cell type from single-nucleus experiments). Our novel class of genes provides a springboard for new opportunities in drug discovery, development, and repurposing in the pre-competitive space. In addition, looking at drug-gene interaction networks, we identify previous trials that may require further follow-up such as riluzole in Alzheimer disease. We also provide a user-friendly web platform to help users explore potential therapeutic targets for neurodegenerative diseases, decreasing activation energy for the community.
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Affiliation(s)
- Chelsea X Alvarado
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Mary B Makarious
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA; Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, London WC1N 3BG, UK; UCL Movement Disorders Centre, University College London, London WC1N 3BG, UK
| | - Cory A Weller
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Dan Vitale
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Mathew J Koretsky
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA
| | - Hirotaka Iwaki
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Kristin Levine
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA
| | - Andrew Singleton
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Faraz Faghri
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Mike A Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA
| | - Hampton L Leonard
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20814, USA; Data Tecnica LLC, Washington, DC 20037, USA; Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD 20814, USA; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany.
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45
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He Q, Wang W, Xiong Y, Tao C, Ma L, You C. Potential Biomarkers in Cerebrospinal Fluid and Plasma for Dementia. J Alzheimers Dis 2024; 100:603-611. [PMID: 38875042 DOI: 10.3233/jad-240260] [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] [Indexed: 06/16/2024]
Abstract
Background The identification of biomarkers for different dementias in plasma and cerebrospinal fluid (CSF) has made substantial progress. However, they are observational studies, and there remains a lack of research on dementias with low incidence rates. Objective We performed a comprehensive Mendelian randomization to identify potential biomarkers for different dementia type. Methods The summary-level datasets encompassed 734 plasma and 154 cerebrospinal fluid proteins sourced from recently published genome-wide association studies (GWAS). Summary statistics for different dementias, including any dementia (refering to any type of dementia symptoms, 218,792 samples), Alzheimer's disease (AD, 63,926 samples), vascular dementia (212,389 samples), frontotemporal dementia (3,024 samples), dementia with Lewy bodies (DLB, 6,618 samples), and dementia in Parkinson's disease (216,895 samples), were collected from large GWAS. The primary method is inverse variance weighting, with additional sensitivity analyses conducted to ensure the robustness of the findings. Results The molecules released into CSF, namely APOE2 for any dementia, APOE2 and Siglec-3 for AD, APOE2 for vascular dementia, and APOE2 for DLB, might be potential biomarkers. CD33 for AD and SNCA for DLB in plasma could be promising biomarkers. Conclusions This is the first study to integrate plasma and CSF proteins to identify potential biomarkers for different dementias.
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Affiliation(s)
- Qiang He
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Wenjing Wang
- Department of Pharmacy, Institute of Metabolic Diseases and Pharmacotherapy, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Xiong
- Department of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Chuanyuan Tao
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Lu Ma
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Chao You
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China
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46
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Jain PR, Yates M, de Celis CR, Drineas P, Jahanshad N, Thompson P, Paschou P. Multiomic approach and Mendelian randomization analysis identify causal associations between blood biomarkers and subcortical brain structure volumes. Neuroimage 2023; 284:120466. [PMID: 37995919 DOI: 10.1016/j.neuroimage.2023.120466] [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: 07/17/2023] [Revised: 10/17/2023] [Accepted: 11/20/2023] [Indexed: 11/25/2023] Open
Abstract
Alterations in subcortical brain structure volumes have been found to be associated with several neurodegenerative and psychiatric disorders. At the same time, genome-wide association studies (GWAS) have identified numerous common variants associated with brain structure. In this study, we integrate these findings, aiming to identify proteins, metabolites, or microbes that have a putative causal association with subcortical brain structure volumes via a two-sample Mendelian randomization approach. This method uses genetic variants as instrument variables to identify potentially causal associations between an exposure and an outcome. The exposure data that we analyzed comprised genetic associations for 2994 plasma proteins, 237 metabolites, and 103 microbial genera. The outcome data included GWAS data for seven subcortical brain structure volumes including accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Eleven proteins and six metabolites were found to have a significant association with subcortical structure volumes, with nine proteins and five metabolites replicated using independent exposure data. We found causal associations between accumbens volume and plasma protease c1 inhibitor as well as strong association between putamen volume and Agouti signaling protein. Among metabolites, urate had the strongest association with thalamic volume. No significant associations were detected between the microbial genera and subcortical brain structure volumes. We also observed significant enrichment for biological processes such as proteolysis, regulation of the endoplasmic reticulum apoptotic signaling pathway, and negative regulation of DNA binding. Our findings provide insights to the mechanisms through which brain volumes may be affected in the pathogenesis of neurodevelopmental and psychiatric disorders and point to potential treatment targets for disorders that are associated with subcortical brain structure volumes.
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Affiliation(s)
- Pritesh R Jain
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Madison Yates
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Carlos Rubin de Celis
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States
| | - Petros Drineas
- Department of Computer Science, Purdue University, United States
| | - Neda Jahanshad
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Paul Thompson
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of South California, United States
| | - Peristera Paschou
- Department of Biological Sciences, Purdue University, West Lafayette, IN 47907, United States.
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47
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Borbye-Lorenzen N, Zhu Z, Agerbo E, Albiñana C, Benros ME, Bian B, Børglum AD, Bulik CM, Debost JCPG, Grove J, Hougaard DM, McRae AF, Mors O, Mortensen PB, Musliner KL, Nordentoft M, Petersen LV, Privé F, Sidorenko J, Skogstrand K, Werge T, Wray NR, Vilhjálmsson BJ, McGrath JJ. The correlates of neonatal complement component 3 and 4 protein concentrations with a focus on psychiatric and autoimmune disorders. CELL GENOMICS 2023; 3:100457. [PMID: 38116117 PMCID: PMC10726496 DOI: 10.1016/j.xgen.2023.100457] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 09/03/2023] [Accepted: 11/08/2023] [Indexed: 12/21/2023]
Abstract
Complement components have been linked to schizophrenia and autoimmune disorders. We examined the association between neonatal circulating C3 and C4 protein concentrations in 68,768 neonates and the risk of six mental disorders. We completed genome-wide association studies (GWASs) for C3 and C4 and applied the summary statistics in Mendelian randomization and phenome-wide association studies related to mental and autoimmune disorders. The GWASs for C3 and C4 protein concentrations identified 15 and 36 independent loci, respectively. We found no associations between neonatal C3 and C4 concentrations and mental disorders in the total sample (both sexes combined); however, post-hoc analyses found that a higher C3 concentration was associated with a reduced risk of schizophrenia in females. Mendelian randomization based on C4 summary statistics found an altered risk of five types of autoimmune disorders. Our study adds to our understanding of the associations between C3 and C4 concentrations and subsequent mental and autoimmune disorders.
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Affiliation(s)
- Nis Borbye-Lorenzen
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Zhihong Zhu
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark.
| | - Esben Agerbo
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Center for Integrated Register-based Research, Aarhus University, CIRRAU, 8210 Aarhus V, Denmark
| | - Clara Albiñana
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark
| | - Michael E Benros
- Copenhagen Research Center for Mental Health, Mental Health Center Copenhagen, Copenhagen University Hospital, Hellerup, Denmark; Department of Immunology and Microbiology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Beilei Bian
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Anders D Børglum
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department of Biomedicine and the iSEQ Center, Aarhus University, Aarhus, Denmark; Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark
| | - Cynthia M Bulik
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden; Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jean-Christophe Philippe Goldtsche Debost
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Department of Psychosis, Aarhus University Hospital Skejby, Aarhus Nord, Denmark
| | - Jakob Grove
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Center for Genomics and Personalized Medicine, CGPM, Aarhus, Denmark; Department of Biomedicine (Human Genetics), Aarhus University, Aarhus, Denmark; Bioinformatics Research Center, Aarhus University, 8000 Aarhus C, Denmark
| | - David M Hougaard
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department for Congenital Disorders, Statens Serum Institut, 2300 Copenhagen S, Denmark
| | - Allan F McRae
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Ole Mors
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Psychosis Research Unit, Aarhus University Hospital - Psychiatry, Aarhus, Denmark
| | - Preben Bo Mortensen
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Center for Integrated Register-based Research, Aarhus University, CIRRAU, 8210 Aarhus V, Denmark
| | - Katherine L Musliner
- Department of Affective Disorders, Aarhus University and Aarhus University Hospital -Psychiatry, Aarhus, Denmark
| | - Merete Nordentoft
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Mental Health Services in the Capital Region of Denmark, Mental Health Center Copenhagen, University of Copenhagen, 2100 Copenhagen, Denmark; Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark
| | - Liselotte V Petersen
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Florian Privé
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark
| | - Julia Sidorenko
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia
| | - Kristin Skogstrand
- Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Thomas Werge
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Department of Clinical Medicine, University of Copenhagen, 2200 Copenhagen N, Denmark; Department of Clinical Medicine, Institute of Biological Psychiatry, Mental Health Services, Copenhagen University Hospital, University of Copenhagen, 2200 Copenhagen N, Denmark; Lundbeck Center for Geogenetics, GLOBE Institute, University of Copenhagen, Copenhagen, Denmark
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD, Australia; Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, UK; Big Data Institute, University of Oxford, Oxford OX3 7LF, UK
| | - Bjarni J Vilhjálmsson
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, 8210 Aarhus V, Denmark; Bioinformatics Research Center, Aarhus University, 8000 Aarhus C, Denmark
| | - John J McGrath
- National Center for Register-Based Research, Aarhus University, 8210 Aarhus V, Denmark; Queensland Brain Institute, The University of Queensland, Brisbane, QLD 4072, Australia; Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD 4076, Australia.
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48
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Bettencourt C, Skene N, Bandres-Ciga S, Anderson E, Winchester LM, Foote IF, Schwartzentruber J, Botia JA, Nalls M, Singleton A, Schilder BM, Humphrey J, Marzi SJ, Toomey CE, Kleifat AA, Harshfield EL, Garfield V, Sandor C, Keat S, Tamburin S, Frigerio CS, Lourida I, Ranson JM, Llewellyn DJ. Artificial intelligence for dementia genetics and omics. Alzheimers Dement 2023; 19:5905-5921. [PMID: 37606627 PMCID: PMC10841325 DOI: 10.1002/alz.13427] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 08/23/2023]
Abstract
Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.
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Affiliation(s)
- Conceicao Bettencourt
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Nathan Skene
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Sara Bandres-Ciga
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
| | - Emma Anderson
- Department of Mental Health of Older People, Division of Psychiatry, University College London, London, UK
| | | | - Isabelle F Foote
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Jeremy Schwartzentruber
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
- Illumina Artificial Intelligence Laboratory, Illumina Inc, Foster City, California, USA
| | - Juan A Botia
- Departamento de Ingeniería de la Información y las Comunicaciones, Universidad de Murcia, Murcia, Spain
| | - Mike Nalls
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
- Data Tecnica International LLC, Washington, DC, USA
| | - Andrew Singleton
- Center for Alzheimer's and Related Dementias (CARD), National Institute on Aging and National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA
- Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, USA
| | - Brian M Schilder
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, USA
| | - Sarah J Marzi
- UK Dementia Research Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Christina E Toomey
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
- Department of Clinical and Movement Neuroscience, UCL Queen Square Institute of Neurology, London, UK
- The Francis Crick Institute, London, UK
| | - Ahmad Al Kleifat
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Eric L Harshfield
- Stroke Research Group, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Victoria Garfield
- MRC Unit for Lifelong Health and Ageing, Institute of Cardiovascular Science, University College London, London, UK
| | - Cynthia Sandor
- UK Dementia Research Institute. School of Medicine, Cardiff University, Cardiff, UK
| | - Samuel Keat
- UK Dementia Research Institute. School of Medicine, Cardiff University, Cardiff, UK
| | - Stefano Tamburin
- Department of Neurosciences, Biomedicine and Movement Sciences, Neurology Section, University of Verona, Verona, Italy
| | - Carlo Sala Frigerio
- UK Dementia Research Institute, Queen Square Institute of Neurology, University College London, London, UK
| | | | | | - David J Llewellyn
- University of Exeter Medical School, Exeter, UK
- The Alan Turing Institute, London, UK
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49
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Cederroth CR, Hong MG, Freydin MB, Edvall NK, Trpchevska N, Jarach C, Schlee W, Schwenk JM, Lopez-Escamez JA, Gallus S, Canlon B, Bulla J, Williams FMK. Screening for Circulating Inflammatory Proteins Does Not Reveal Plasma Biomarkers of Constant Tinnitus. J Assoc Res Otolaryngol 2023; 24:593-606. [PMID: 38079022 PMCID: PMC10752855 DOI: 10.1007/s10162-023-00920-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 11/22/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND AND OBJECTIVE Tinnitus would benefit from an objective biomarker. The goal of this study is to identify plasma biomarkers of constant and chronic tinnitus among selected circulating inflammatory proteins. METHODS A case-control retrospective study on 548 cases with constant tinnitus and 548 matched controls from the Swedish Tinnitus Outreach Project (STOP), whose plasma samples were examined using Olink's Inflammatory panel. Replication and meta-analysis were performed using the same method on samples from the TwinsUK cohort. Participants from LifeGene, whose blood was collected in Stockholm and Umeå, were recruited to STOP for a tinnitus subtyping study. An age and sex matching was performed at the individual level. TwinsUK participants (n = 928) were selected based on self-reported tinnitus status over 2 to 10 years. Primary outcomes include normalized levels for 96 circulating proteins, which were used as an index test. No reference standard was available in this study. RESULTS After adjustment for age, sex, BMI, smoking, hearing loss, and laboratory site, the top proteins identified were FGF-21, MCP4, GDNF, CXCL9, and MCP-1; however, these were no longer statistically significant after correction for multiple testing. Stratification by sex did not yield any significant associations. Similarly, associations with hearing loss or other tinnitus-related comorbidities such as stress, anxiety, depression, hyperacusis, temporomandibular joint disorders, and headache did not yield any significant associations. Analysis in the TwinsUK failed in replicating the top candidates. Meta-analysis of STOP and TwinsUK did not reveal any significant association. Using elastic net regularization, models exhibited poor predictive capacity tinnitus based on inflammatory markers [sensitivity = 0.52 (95% CI 0.47-0.57), specificity = 0.53 (0.48-0.58), positive predictive value = 0.52 (0.47-0.56), negative predictive values = 0.53 (0.49-0.58), and AUC = 0.53 (0.49-0.56)]. DISCUSSION Our results did not identify significant associations of the selected inflammatory proteins with constant tinnitus. Future studies examining longitudinal relations among those with more severe tinnitus and using more recent expanded proteomics platforms and sampling of cerebrospinal fluid could increase the likelihood of identifying relevant molecular biomarkers.
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Affiliation(s)
- Christopher R Cederroth
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden.
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Ropewalk House, Nottingham, UK.
- Department of Otolaryngology, Head and Neck Surgery, Translational Hearing Research, Tübingen Hearing Research Center, University of Tübingen, Tubingen, Germany.
| | - Mun-Gwan Hong
- Affinity Proteomics, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Stockholm, Sweden
| | - Maxim B Freydin
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
| | - Niklas K Edvall
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Natalia Trpchevska
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Carlotta Jarach
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Winfried Schlee
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Jochen M Schwenk
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Stockholm, Sweden
| | - Jose-Antonio Lopez-Escamez
- Faculty of Medicine & Health, School of Medical Sciences, Meniere's Disease Neuroscience Research Program, The Kolling Institute, University of Sydney, Sydney, NSW, Australia
- Otology and Neurotology Group CTS495, Department of Genomic Medicine, GENYO - Centre for Genomics and Oncological Research - Pfizer, University of Granada, PTS, Junta de Andalucía, Granada, Spain
- Division of Otolaryngology, Department of Surgery, Instituto de Investigación Biosanitaria, ibs.GRANADA, Universidad de Granada, GranadaGranada, Spain
| | - Silvano Gallus
- Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy
| | - Barbara Canlon
- Section of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden
| | - Jan Bulla
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
- Department of Mathematics, University of Bergen, Bergen, Norway
| | - Frances M K Williams
- Department of Twin Research and Genetic Epidemiology, School of Life Course Sciences, King's College London, London, UK
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50
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You J, Guo Y, Zhang Y, Kang JJ, Wang LB, Feng JF, Cheng W, Yu JT. Plasma proteomic profiles predict individual future health risk. Nat Commun 2023; 14:7817. [PMID: 38016990 PMCID: PMC10684756 DOI: 10.1038/s41467-023-43575-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 11/13/2023] [Indexed: 11/30/2023] Open
Abstract
Developing a single-domain assay to identify individuals at high risk of future events is a priority for multi-disease and mortality prevention. By training a neural network, we developed a disease/mortality-specific proteomic risk score (ProRS) based on 1461 Olink plasma proteins measured in 52,006 UK Biobank participants. This integrative score markedly stratified the risk for 45 common conditions, including infectious, hematological, endocrine, psychiatric, neurological, sensory, circulatory, respiratory, digestive, cutaneous, musculoskeletal, and genitourinary diseases, cancers, and mortality. The discriminations witnessed high accuracies achieved by ProRS for 10 endpoints (e.g., cancer, dementia, and death), with C-indexes exceeding 0.80. Notably, ProRS produced much better or equivalent predictive performance than established clinical indicators for almost all endpoints. Incorporating clinical predictors with ProRS enhanced predictive power for most endpoints, but this combination only exhibited limited improvement when compared to ProRS alone. Some proteins, e.g., GDF15, exhibited important discriminative values for various diseases. We also showed that the good discriminative performance observed could be largely translated into practical clinical utility. Taken together, proteomic profiles may serve as a replacement for complex laboratory tests or clinical measures to refine the comprehensive risk assessments of multiple diseases and mortalities simultaneously. Our models were internally validated in the UK Biobank; thus, further independent external validations are necessary to confirm our findings before application in clinical settings.
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Affiliation(s)
- Jia You
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yu Guo
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yi Zhang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Ju-Jiao Kang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Lin-Bo Wang
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jian-Feng Feng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- School of Data Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
| | - Wei Cheng
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Zhejiang, China.
- Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, China.
| | - Jin-Tai Yu
- Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
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