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Chung S, Jeong JH, Park JC, Han JW, Lee Y, Kim JI, Mook-Jung I. Blockade of STING activation alleviates microglial dysfunction and a broad spectrum of Alzheimer's disease pathologies. Exp Mol Med 2024:10.1038/s12276-024-01295-y. [PMID: 39218977 DOI: 10.1038/s12276-024-01295-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 04/29/2024] [Accepted: 05/22/2024] [Indexed: 09/04/2024] Open
Abstract
Abnormal glial activation promotes neurodegeneration in Alzheimer's disease (AD), the most common cause of dementia. Stimulation of the cGAS-STING pathway induces microglial dysfunction and sterile inflammation, which exacerbates AD. We showed that inhibiting STING activation can control microglia and ameliorate a wide spectrum of AD symptoms. The cGAS-STING pathway is required for the detection of ectopic DNA and the subsequent immune response. Amyloid-β (Aβ) and tau induce mitochondrial stress, which causes DNA to be released into the cytoplasm of microglia. cGAS and STING are highly expressed in Aβ plaque-associated microglia, and neuronal STING is upregulated in the brains of AD model animals. The presence of the APOE ε4 allele, an AD risk factor, also upregulated both proteins. STING activation was necessary for microglial NLRP3 activation, proinflammatory responses, and type-I-interferon responses. Pharmacological STING inhibition reduced a wide range of AD pathogenic features in AppNL-G-F/hTau double-knock-in mice. An unanticipated transcriptome shift in microglia reduced gliosis and cerebral inflammation. Significant reductions in the Aβ load, tau phosphorylation, and microglial synapse engulfment prevented memory loss. To summarize, our study describes the pathogenic mechanism of STING activation as well as its potential as a therapeutic target in AD.
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Affiliation(s)
- Sunwoo Chung
- Convergence Dementia Research Center, College of Medicine, Seoul National University, 03080, Seoul, Korea
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, 03080, Seoul, Korea
| | - June-Hyun Jeong
- Convergence Dementia Research Center, College of Medicine, Seoul National University, 03080, Seoul, Korea
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, 03080, Seoul, Korea
| | - Jong-Chan Park
- Department of Biophysics & Institute of Quantum Biophysics, Sungkyunkwan University, 16419, Gyeonggi-do, Korea
| | - Jong Won Han
- Convergence Dementia Research Center, College of Medicine, Seoul National University, 03080, Seoul, Korea
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, 03080, Seoul, Korea
| | - Yeajina Lee
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, 03080, Seoul, Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University, 03080, Seoul, Korea
| | - Jong-Il Kim
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, 03080, Seoul, Korea
- Genomic Medicine Institute, Medical Research Center, Seoul National University, 03080, Seoul, Korea
| | - Inhee Mook-Jung
- Convergence Dementia Research Center, College of Medicine, Seoul National University, 03080, Seoul, Korea.
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, 03080, Seoul, Korea.
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Yang Y, Pan Z, Sun J, Welch J, Klionsky DJ. Autophagy and machine learning: Unanswered questions. Biochim Biophys Acta Mol Basis Dis 2024; 1870:167263. [PMID: 38801963 DOI: 10.1016/j.bbadis.2024.167263] [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: 02/15/2024] [Revised: 04/27/2024] [Accepted: 05/21/2024] [Indexed: 05/29/2024]
Abstract
Autophagy is a critical conserved cellular process in maintaining cellular homeostasis by clearing and recycling damaged organelles and intracellular components in lysosomes and vacuoles. Autophagy plays a vital role in cell survival, bioenergetic homeostasis, organism development, and cell death regulation. Malfunctions in autophagy are associated with various human diseases and health disorders, such as cancers and neurodegenerative diseases. Significant effort has been devoted to autophagy-related research in the context of genes, proteins, diagnosis, etc. In recent years, there has been a surge of studies utilizing state of the art machine learning (ML) tools to analyze and understand the roles of autophagy in various biological processes. We taxonomize ML techniques that are applicable in an autophagy context, comprehensively review existing efforts being taken in this direction, and outline principles to consider in a biomedical context. In recognition of recent groundbreaking advances in the deep-learning community, we discuss new opportunities in interdisciplinary collaborations and seek to engage autophagy and computer science researchers to promote autophagy research with joint efforts.
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Affiliation(s)
- Ying Yang
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA; Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Zhaoying Pan
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA
| | - Jianhui Sun
- Department of Computer Science, University of Virginia, Charlottesville, VA 22903, USA
| | - Joshua Welch
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel J Klionsky
- Department of Molecular, Cellular, and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA; Life Sciences Institute, University of Michigan, Ann Arbor, MI 48109, USA.
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3
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Park JC, Han JW, Lee W, Kim J, Lee SE, Lee D, Choi H, Han J, Kang YJ, Diep YN, Cho H, Kang R, Yu WJ, Lee J, Choi M, Im SW, Kim JI, Mook-Jung I. Microglia Gravitate toward Amyloid Plaques Surrounded by Externalized Phosphatidylserine via TREM2. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2400064. [PMID: 38981007 DOI: 10.1002/advs.202400064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Revised: 05/08/2024] [Indexed: 07/11/2024]
Abstract
Microglia play a crucial role in synaptic elimination by engulfing dystrophic neurons via triggering receptors expressed on myeloid cells 2 (TREM2). They are also involved in the clearance of beta-amyloid (Aβ) plaques in Alzheimer's disease (AD); nonetheless, the driving force behind TREM2-mediated phagocytosis of beta-amyloid (Aβ) plaques remains unknown. Here, using advanced 2D/3D/4D co-culture systems with loss-of-function mutations in TREM2 (a frameshift mutation engineered in exon 2) brain organoids/microglia/assembloids, it is identified that the clearance of Aβ via TREM2 is accelerated by externalized phosphatidylserine (ePtdSer) generated from dystrophic neurons surrounding the Aβ plaques. Moreover, it is investigated whether microglia from both sporadic (CRISPR-Cas9-based APOE4 lines) and familial (APPNL-G-F/MAPT double knock-in mice) AD models show reduced levels of TREM2 and lack of phagocytic activity toward ePtdSer-positive Aβ plaques. Herein new insight is provided into TREM2-dependent microglial phagocytosis of Aβ plaques in the context of the presence of ePtdSer during AD progression.
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Affiliation(s)
- Jong-Chan Park
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Jong Won Han
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Woochan Lee
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- Genome Medicine Institute, Medical Research Center, Seoul National University, Seoul, 03080, Republic of Korea
| | - Jieun Kim
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Sang-Eun Lee
- Department of Physiology and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- BK21 FOUR Biomedical Science Program, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- UK Dementia Research Institute, Institute of Neurology, University College London, Gower Street, London, WC1E 6BT, UK
- Neuroscience Research Institute, Seoul National University Medical Research Center, Seoul, 03080, Republic of Korea
| | - Dongjoon Lee
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Hayoung Choi
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Jihui Han
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - You Jung Kang
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Yen N Diep
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Hansang Cho
- Department of Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Rian Kang
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Won Jong Yu
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon, 16419, Republic of Korea
| | - Jean Lee
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Murim Choi
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Sun-Wha Im
- Department of Biochemistry and Molecular Biology, Kangwon National University School of Medicine, Gangwon, Seoul, 24341, Republic of Korea
| | - Jong-Il Kim
- Genome Medicine Institute, Medical Research Center, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Inhee Mook-Jung
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
- Convergence Dementia Research Center, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
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Kang R, Park S, Shin S, Bak G, Park JC. Electrophysiological insights with brain organoid models: a brief review. BMB Rep 2024; 57:311-317. [PMID: 38919012 PMCID: PMC11289503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Revised: 06/19/2024] [Accepted: 06/19/2024] [Indexed: 06/27/2024] Open
Abstract
Brain organoid is a three-dimensional (3D) tissue derived from stem cells such as induced pluripotent stem cells (iPSCs) embryonic stem cells (ESCs) that reflect real human brain structure. It replicates the complexity and development of the human brain, enabling studies of the human brain in vitro. With emerging technologies, its application is various, including disease modeling and drug screening. A variety of experimental methods have been used to study structural and molecular characteristics of brain organoids. However, electrophysiological analysis is necessary to understand their functional characteristics and complexity. Although electrophysiological approaches have rapidly advanced for monolayered cells, there are some limitations in studying electrophysiological and neural network characteristics due to the lack of 3D characteristics. Herein, electrophysiological measurement and analytical methods related to neural complexity and 3D characteristics of brain organoids are reviewed. Overall, electrophysiological understanding of brain organoids allows us to overcome limitations of monolayer in vitro cell culture models, providing deep insights into the neural network complex of the real human brain and new ways of disease modeling. [BMB Reports 2024; 57(7): 311-317].
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Affiliation(s)
- Rian Kang
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Korea
| | - Soomin Park
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Korea
- Department of Biophysics, Sungkyunkwan University, Suwon 16419, Korea
| | - Saewoon Shin
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Korea
| | - Gyusoo Bak
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Korea
| | - Jong-Chan Park
- Institute of Quantum Biophysics, Sungkyunkwan University, Suwon 16419, Korea
- Department of Metabiohealth, Sungkyunkwan University, Suwon 16419, Korea
- Department of Biophysics, Sungkyunkwan University, Suwon 16419, Korea
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5
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Goddard TR, Brookes KJ, Sharma R, Moemeni A, Rajkumar AP. Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science. Cells 2024; 13:223. [PMID: 38334615 PMCID: PMC10854541 DOI: 10.3390/cells13030223] [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: 12/14/2023] [Revised: 01/17/2024] [Accepted: 01/23/2024] [Indexed: 02/10/2024] Open
Abstract
Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.
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Affiliation(s)
- Thomas R. Goddard
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
| | - Keeley J. Brookes
- Department of Biosciences, School of Science & Technology, Nottingham Trent University, Nottingham NG11 8NS, UK
| | - Riddhi Sharma
- Biodiscovery Institute, School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK
- UK Health Security Agency, Radiation Effects Department, Radiation Protection Science Division, Harwell Science Campus, Didcot, Oxfordshire OX11 0RQ, UK
| | - Armaghan Moemeni
- School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
| | - Anto P. Rajkumar
- Mental Health and Clinical Neurosciences Academic Unit, Institute of Mental Health, School of Medicine, University of Nottingham, Nottingham NG7 2TU, UK
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Chamoso-Sanchez D, Rabadán Pérez F, Argente J, Barbas C, Martos-Moreno GA, Rupérez FJ. Identifying subgroups of childhood obesity by using multiplatform metabotyping. Front Mol Biosci 2023; 10:1301996. [PMID: 38174068 PMCID: PMC10761426 DOI: 10.3389/fmolb.2023.1301996] [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: 09/25/2023] [Accepted: 11/30/2023] [Indexed: 01/05/2024] Open
Abstract
Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.
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Affiliation(s)
- David Chamoso-Sanchez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
| | | | - Jesús Argente
- Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación Sanitaria La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
- IMDEA Food Institute, Madrid, Spain
| | - Coral Barbas
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
| | - Gabriel A. Martos-Moreno
- Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación Sanitaria La Princesa, Universidad Autónoma de Madrid, Madrid, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain
| | - Francisco J. Rupérez
- Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Boadilla del Monte, Spain
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Zhu J, Jiang X, Chang Y, Wu Y, Sun S, Wang C, Zheng S, Wang M, Yao Y, Li G, Ma R. Clemastine fumarate attenuates tauopathy and meliorates cognition in hTau mice via autophagy enhancement. Int Immunopharmacol 2023; 123:110649. [PMID: 37494840 DOI: 10.1016/j.intimp.2023.110649] [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: 04/24/2023] [Revised: 06/12/2023] [Accepted: 07/11/2023] [Indexed: 07/28/2023]
Abstract
Clemastine fumarate, which has been identified as a promising agent for remyelination and autophagy enhancement, has been shown to mitigate Aβ deposition and improve cognitive function in the APP/PS1 mouse model of Alzheimer's disease. Based on these findings, we investigated the effect of clemastine fumarate in hTau mice, a different Alzheimer's disease model characterized by overexpression of human Tau protein. Surprisingly, clemastine fumarate was effective in reducing pathological deposition of Tau protein, protecting neurons and synapses from damage, inhibiting neuroinflammation, and improving cognitive impairment in hTau mice. Interestingly, chloroquine, an autophagy inhibitor, had a significant impact on total and Sarkosyl fractions of autophagy, demonstrating that it can interrupt autophagy. Notably, after administration of chloroquine, levels of Tau protein were significantly increased. When clemastine fumarate was co-administered with chloroquine, the protective effects were reversed, indicating that clemastine fumarate indeed triggered autophagy and promoted the degradation of Tau protein, while also inhibiting further Tauopathy-related neuroinflammation and synapse loss to improve cognitive function in hTau mice.
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Affiliation(s)
- Jiahui Zhu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; Department of Neurology, Wuhan Fourth Hospital, Wuhan 430033 Hubei, China
| | - Xingjun Jiang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Yanmin Chang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Yanqing Wu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Shangqi Sun
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Cailin Wang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Siyi Zheng
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Min Wang
- Department of Neurology, Wuhan Fourth Hospital, Wuhan 430033 Hubei, China
| | - Yi Yao
- Department of Neurology, Wuhan Fourth Hospital, Wuhan 430033 Hubei, China
| | - Gang Li
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
| | - Rong Ma
- Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
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Clark C, Rabl M, Dayon L, Popp J. The promise of multi-omics approaches to discover biological alterations with clinical relevance in Alzheimer's disease. Front Aging Neurosci 2022; 14:1065904. [PMID: 36570537 PMCID: PMC9768448 DOI: 10.3389/fnagi.2022.1065904] [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: 10/10/2022] [Accepted: 11/21/2022] [Indexed: 12/12/2022] Open
Abstract
Beyond the core features of Alzheimer's disease (AD) pathology, i.e. amyloid pathology, tau-related neurodegeneration and microglia response, multiple other molecular alterations and pathway dysregulations have been observed in AD. Their inter-individual variations, complex interactions and relevance for clinical manifestation and disease progression remain poorly understood, however. Heterogeneity at both pathophysiological and clinical levels complicates diagnosis, prognosis, treatment and drug design and testing. High-throughput "omics" comprise unbiased and untargeted data-driven methods which allow the exploration of a wide spectrum of disease-related changes at different endophenotype levels without focussing a priori on specific molecular pathways or molecules. Crucially, new methodological and statistical advances now allow for the integrative analysis of data resulting from multiple and different omics methods. These multi-omics approaches offer the unique advantage of providing a more comprehensive characterisation of the AD endophenotype and to capture molecular signatures and interactions spanning various biological levels. These new insights can then help decipher disease mechanisms more deeply. In this review, we describe the different multi-omics tools and approaches currently available and how they have been applied in AD research so far. We discuss how multi-omics can be used to explore molecular alterations related to core features of the AD pathologies and how they interact with comorbid pathological alterations. We further discuss whether the identified pathophysiological changes are relevant for the clinical manifestation of AD, in terms of both cognitive impairment and neuropsychiatric symptoms, and for clinical disease progression over time. Finally, we address the opportunities for multi-omics approaches to help discover novel biomarkers for diagnosis and monitoring of relevant pathophysiological processes, along with personalised intervention strategies in AD.
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Affiliation(s)
- Christopher Clark
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,*Correspondence: Christopher Clark,
| | - Miriam Rabl
- Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,University of Lausanne, Lausanne, Switzerland
| | - Loïc Dayon
- Nestlé Institute of Food Safety and Analytical Sciences, Nestlé Research, Lausanne, Switzerland,Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Julius Popp
- Department of Psychiatry, Psychotherapy and Psychosomatics, University of Zürich, Zürich, Switzerland,Geriatric Psychiatry, University Hospital of Psychiatry Zürich, Zürich, Switzerland,Old Age Psychiatry, Department of Psychiatry, Lausanne University Hospital, Lausanne, Switzerland
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9
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Park JC, Noh J, Jang S, Kim KH, Choi H, Lee D, Kim J, Chung J, Lee DY, Lee Y, Lee H, Yoo DK, Lee AC, Byun MS, Yi D, Han SH, Kwon S, Mook-Jung I. Association of B cell profile and receptor repertoire with the progression of Alzheimer's disease. Cell Rep 2022; 40:111391. [PMID: 36130492 DOI: 10.1016/j.celrep.2022.111391] [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: 02/28/2022] [Revised: 07/04/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
Alzheimer's disease (AD) is the most prevalent type of dementia. Reports have revealed that the peripheral immune system is linked to neuropathology; however, little is known about the contribution of B lymphocytes in AD. For this longitudinal study, 133 participants are included at baseline and second-year follow-up. Also, we analyze B cell receptor (BCR) repertoire data generated from a public dataset of three normal and 10 AD samples and perform BCR repertoire profiling and pairwise sharing analysis. As a result, longitudinal increase in B lymphocytes is associated with increased cerebral amyloid deposition and hyperactivates induced pluripotent stem cell-derived microglia with loss-of-function for beta-amyloid clearance. Patients with AD share similar class-switched BCR sequences with identical isotypes, despite the high somatic hypermutation rate. Thus, BCR repertoire profiling can lead to the development of individualized immune-based therapeutics and treatment. We provide evidence of both quantitative and qualitative changes in B lymphocytes during AD pathogenesis.
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Affiliation(s)
- Jong-Chan Park
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Jinsung Noh
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Sukjin Jang
- Department of Medicine, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Ki Hyun Kim
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Hayoung Choi
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Dongjoon Lee
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Jieun Kim
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea
| | - Junho Chung
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul 03080, Republic of Korea; Department of Biomedical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Dong Young Lee
- Institute of Human Behavioral Medicine, Medical Research Center, Seoul National University, Seoul 03080, Republic of Korea; Department of Psychiatry, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Yonghee Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Hyunho Lee
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Duck Kyun Yoo
- Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Biomedical Science, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Amos Chungwon Lee
- Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea
| | - Min Soo Byun
- Department of Psychiatry, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Dahyun Yi
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul 03080, Republic of Korea
| | - Sun-Ho Han
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Bio-MAX Institute, Seoul National University, Seoul 08826, Republic of Korea; BK21+ Creative Research Engineer Development for IT, Seoul National University, Seoul 08826, Republic of Korea; Biomedical Research Institute, Seoul National University Hospital, Seoul 03080, Republic of Korea; Interdisciplinary Program in Bioengineering, Seoul National University, Seoul 08826, Republic of Korea.
| | - Inhee Mook-Jung
- Department of Biochemistry and Biomedical Sciences, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; Neuroscience Research Institute, Medical Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea; SNU Dementia Research Center, College of Medicine, Seoul National University, Seoul 03080, Republic of Korea.
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