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Wu M, Tao H, Xu T, Zheng X, Wen C, Wang G, Peng Y, Dai Y. Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases. Proteome Sci 2024; 22:7. [PMID: 39304896 DOI: 10.1186/s12953-024-00231-2] [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: 04/29/2024] [Accepted: 09/01/2024] [Indexed: 09/22/2024] Open
Abstract
Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research.
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Affiliation(s)
- Mengyao Wu
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Huihui Tao
- School of Medicine, Anhui University of Science & Technology, Huainan, China.
- Key Laboratory of Industrial Dust Deep Reduction and Occupational Health and Safety of Anhui Higher Education Institutes, Huainan, China.
- Anhui Province Engineering Laboratory of Occupational Health and Safety, Huainan, China.
| | - Tiantian Xu
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Xuejia Zheng
- The First Hospital of Anhui University of Science and Technology, Huainan, China
| | - Chunmei Wen
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Guoying Wang
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Yali Peng
- School of Medicine, Anhui University of Science & Technology, Huainan, China
| | - Yong Dai
- School of Medicine, Anhui University of Science & Technology, Huainan, China
- The First Hospital of Anhui University of Science and Technology, Huainan, China
- Joint Research Center for Occupational Medicine and Health of IHM, Anhui University of Science and Technology, Huainan, China
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2
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Kitani A, Matsui Y. Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.25.609610. [PMID: 39253457 PMCID: PMC11383294 DOI: 10.1101/2024.08.25.609610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Alzheimer's disease (AD) is an important research topic. While amyloid plaques and neurofibrillary tangles are hallmark pathological features of AD, cognitive resilience (CR) is a phenomenon where cognitive function remains preserved despite the presence of these pathological features. This study aimed to construct and compare predictive machine learning models for CR scores using RNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) and Mount Sinai Brain Bank (MSBB) cohorts. We evaluated support vector regression (SVR), random forest, XGBoost, linear, and transformer-based models. The SVR model exhibited the best performance, with contributing genes identified using Shapley additive explanations (SHAP) scores, providing insights into biological pathways associated with CR. Finally, we developed a tool called the resilience gene analyzer (REGA), which visualizes SHAP scores to interpret the contributions of individual genes to CR. REGA is available at https://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/.
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Affiliation(s)
- Akihiro Kitani
- Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yusuke Matsui
- Biomedical and Health Informatics Unit, Department of Integrated Health Science, Nagoya University Graduate School of Medicine, Nagoya, Japan
- Institute for Glyco-core Research (iGCORE), Nagoya University, 461-8673 Nagoya, Aichi, Japan
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3
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Tariq U, Saeed F. Predicting peptide properties from mass spectrometry data using deep attention-based multitask network and uncertainty quantification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.21.609035. [PMID: 39229185 PMCID: PMC11370541 DOI: 10.1101/2024.08.21.609035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Database search algorithms reduce the number of potential candidate peptides against which scoring needs to be performed using a single (i.e. mass) property for filtering. While useful, filtering based on one property may lead to exclusion of non-abundant spectra and uncharacterized peptides - potentially exacerbating the streetlight effect. Here we present ProteoRift, a novel attention and multitask deep-network, which can predict multiple peptide properties (length, missed cleavages, and modification status) directly from spectra. We demonstrate that ProteoRift can predict these properties with up to 97% accuracy resulting in search-space reduction by more than 90%. As a result, our end-to-end pipeline is shown to exhibit 8x to 12x speedups with peptide deduction accuracy comparable to algorithmic techniques. We also formulate two uncertainty estimation metrics, which can distinguish between in-distribution and out-of-distribution data (ROC-AUC 0.99) and predict high-scoring mass spectra against correct peptide (ROC-AUC 0.94). These models and metrics are integrated in an end-to-end ML pipeline available at https://github.com/pcdslab/ProteoRift.
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Affiliation(s)
- Usman Tariq
- Knight Foundation School of Computing, and Information Sciences, Florida International University (FIU), Miami, FL USA
| | - Fahad Saeed
- Knight Foundation School of Computing, and Information Sciences, Florida International University (FIU), Miami, FL USA
- Biomolecular Sciences Institute (BSI), Florida International University, Miami, FL, USA
- Department of Human and Molecular Genetics, Herbert Wertheim School of Medicine, Florida International University, Miami, FL, USA
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4
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Jury-Garfe N, Redding-Ochoa J, You Y, Martínez P, Karahan H, Chimal-Juárez E, Johnson TS, Zhang J, Resnick S, Kim J, Troncoso JC, Lasagna-Reeves CA. Enhanced microglial dynamics and a paucity of tau seeding in the amyloid plaque microenvironment contribute to cognitive resilience in Alzheimer's disease. Acta Neuropathol 2024; 148:15. [PMID: 39102080 DOI: 10.1007/s00401-024-02775-1] [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: 05/21/2024] [Revised: 07/09/2024] [Accepted: 07/26/2024] [Indexed: 08/06/2024]
Abstract
Asymptomatic Alzheimer's disease (AsymAD) describes the status of individuals with preserved cognition but identifiable Alzheimer's disease (AD) brain pathology (i.e., beta-amyloid (Aβ) deposits, neuritic plaques, and neurofibrillary tangles) at autopsy. In this study, we investigated the postmortem brains of a cohort of AsymAD subjects to gain insight into the mechanisms underlying resilience to AD pathology and cognitive decline. Our results showed that AsymAD cases exhibit enrichment in core plaques, decreased filamentous plaque accumulation, and increased plaque-surrounding microglia. Less pathological tau aggregation in dystrophic neurites was found in AsymAD brains than in AD brains, and tau seeding activity was comparable to that in healthy brains. We used spatial transcriptomics to characterize the plaque niche further and revealed autophagy, endocytosis, and phagocytosis as the pathways associated with the genes upregulated in the AsymAD plaque niche. Furthermore, the levels of ARP2 and CAP1, which are actin-based motility proteins that participate in the dynamics of actin filaments to allow cell motility, were increased in the microglia surrounding amyloid plaques in AsymAD cases. Our findings suggest that the amyloid-plaque microenvironment in AsymAD cases is characterized by the presence of microglia with highly efficient actin-based cell motility mechanisms and decreased tau seeding compared with that in AD brains. These two mechanisms can potentially protect against the toxic cascade initiated by Aβ, preserving brain health, and slowing AD pathology progression.
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Affiliation(s)
- Nur Jury-Garfe
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Javier Redding-Ochoa
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Yanwen You
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Pablo Martínez
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hande Karahan
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
| | - Enrique Chimal-Juárez
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Travis S Johnson
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, USA
| | - Jie Zhang
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Susan Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging and National Institute of Health, Baltimore, MD, USA
| | - Jungsu Kim
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, USA
| | - Juan C Troncoso
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, USA
| | - Cristian A Lasagna-Reeves
- Stark Neurosciences Research Institute, Indiana University School of Medicine, Neurosciences Research Building 214G, 320 West 15th Street, Indianapolis, IN, 46202, USA.
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA.
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA.
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Chu LX, Wang WJ, Gu XP, Wu P, Gao C, Zhang Q, Wu J, Jiang DW, Huang JQ, Ying XW, Shen JM, Jiang Y, Luo LH, Xu JP, Ying YB, Chen HM, Fang A, Feng ZY, An SH, Li XK, Wang ZG. Spatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine. Mil Med Res 2024; 11:31. [PMID: 38797843 PMCID: PMC11129507 DOI: 10.1186/s40779-024-00537-4] [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: 12/15/2023] [Accepted: 05/07/2024] [Indexed: 05/29/2024] Open
Abstract
Aging and regeneration represent complex biological phenomena that have long captivated the scientific community. To fully comprehend these processes, it is essential to investigate molecular dynamics through a lens that encompasses both spatial and temporal dimensions. Conventional omics methodologies, such as genomics and transcriptomics, have been instrumental in identifying critical molecular facets of aging and regeneration. However, these methods are somewhat limited, constrained by their spatial resolution and their lack of capacity to dynamically represent tissue alterations. The advent of emerging spatiotemporal multi-omics approaches, encompassing transcriptomics, proteomics, metabolomics, and epigenomics, furnishes comprehensive insights into these intricate molecular dynamics. These sophisticated techniques facilitate accurate delineation of molecular patterns across an array of cells, tissues, and organs, thereby offering an in-depth understanding of the fundamental mechanisms at play. This review meticulously examines the significance of spatiotemporal multi-omics in the realms of aging and regeneration research. It underscores how these methodologies augment our comprehension of molecular dynamics, cellular interactions, and signaling pathways. Initially, the review delineates the foundational principles underpinning these methods, followed by an evaluation of their recent applications within the field. The review ultimately concludes by addressing the prevailing challenges and projecting future advancements in the field. Indubitably, spatiotemporal multi-omics are instrumental in deciphering the complexities inherent in aging and regeneration, thus charting a course toward potential therapeutic innovations.
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Affiliation(s)
- Liu-Xi Chu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Wen-Jia Wang
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xin-Pei Gu
- School of Pharmaceutical Sciences, Guangdong Provincial Key Laboratory of New Drug Screening, Southern Medical University, Guangzhou, 510515, China
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China
| | - Ping Wu
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Chen Gao
- State Key Laboratory of Bioelectronics, School of Biological Science & Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Quan Zhang
- Integrative Muscle Biology Laboratory, Division of Regenerative and Rehabilitative Sciences, University of Tennessee Health Science Center, Memphis, TN, 38163, United States
| | - Jia Wu
- Key Laboratory for Laboratory Medicine, Ministry of Education, Zhejiang Provincial Key Laboratory of Medical Genetics, School of Laboratory Medicine and Life Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Da-Wei Jiang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jun-Qing Huang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China
| | - Xin-Wang Ying
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Jia-Men Shen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi Jiang
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Li-Hua Luo
- School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 324025, Zhejiang, China
| | - Jun-Peng Xu
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Yi-Bo Ying
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Hao-Man Chen
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Ao Fang
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China
| | - Zun-Yong Feng
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Departments of Diagnostic Radiology, Surgery, Chemical and Biomolecular Engineering, and Biomedical Engineering, Yong Loo Lin School of Medicine and College of Design and Engineering, National University of Singapore, Singapore, 119074, Singapore.
- Clinical Imaging Research Centre, Centre for Translational Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Nanomedicine Translational Research Program, NUS Center for Nanomedicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117597, Singapore.
- Institute of Molecular and Cell Biology, Agency for Science, Technology, and Research (A*STAR), Singapore, 138673, Singapore.
| | - Shu-Hong An
- Department of Human Anatomy, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271000, Shandong, China.
| | - Xiao-Kun Li
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Zhou-Guang Wang
- Affiliated Cixi Hospital, Wenzhou Medical University, Ningbo, 315300, Zhejiang, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- National Key Laboratory of Macromolecular Drug Development and Manufacturing, School of Pharmaceutical Science, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Institute of Imaging Diagnosis and Minimally Invasive Intervention Research, the Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Hospital of Zhejiang University, Lishui, 323000, Zhejiang, China.
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6
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Gouveia Roque C, Phatnani H, Hengst U. The broken Alzheimer's disease genome. CELL GENOMICS 2024; 4:100555. [PMID: 38697121 PMCID: PMC11099344 DOI: 10.1016/j.xgen.2024.100555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/25/2024] [Accepted: 04/07/2024] [Indexed: 05/04/2024]
Abstract
The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.
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Affiliation(s)
- Cláudio Gouveia Roque
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY 10013, USA; Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University, New York, NY 10032, USA
| | - Ulrich Hengst
- The Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA; Department of Pathology & Cell Biology, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY 10032, USA.
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7
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de Vries LE, Jongejan A, Monteiro Fortes J, Balesar R, Rozemuller AJM, Moerland PD, Huitinga I, Swaab DF, Verhaagen J. Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response. Acta Neuropathol Commun 2024; 12:68. [PMID: 38664739 PMCID: PMC11046840 DOI: 10.1186/s40478-024-01760-9] [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: 01/10/2024] [Accepted: 03/10/2024] [Indexed: 04/28/2024] Open
Abstract
Some individuals show a discrepancy between cognition and the amount of neuropathological changes characteristic for Alzheimer's disease (AD). This phenomenon has been referred to as 'resilience'. The molecular and cellular underpinnings of resilience remain poorly understood. To obtain an unbiased understanding of the molecular changes underlying resilience, we investigated global changes in gene expression in the superior frontal gyrus of a cohort of cognitively and pathologically well-defined AD patients, resilient individuals and age-matched controls (n = 11-12 per group). 897 genes were significantly altered between AD and control, 1121 between resilient and control and 6 between resilient and AD. Gene set enrichment analysis (GSEA) revealed that the expression of metallothionein (MT) and of genes related to mitochondrial processes was higher in the resilient donors. Weighted gene co-expression network analysis (WGCNA) identified gene modules related to the unfolded protein response, mitochondrial processes and synaptic signaling to be differentially associated with resilience or dementia. As changes in MT, mitochondria, heat shock proteins and the unfolded protein response (UPR) were the most pronounced changes in the GSEA and/or WGCNA, immunohistochemistry was used to further validate these processes. MT was significantly increased in astrocytes in resilient individuals. A higher proportion of the mitochondrial gene MT-CO1 was detected outside the cell body versus inside the cell body in the resilient compared to the control group and there were higher levels of heat shock protein 70 (HSP70) and X-box-binding protein 1 spliced (XBP1s), two proteins related to heat shock proteins and the UPR, in the AD donors. Finally, we show evidence for putative sex-specific alterations in resilience, including gene expression differences related to autophagy in females compared to males. Taken together, these results show possible mechanisms involving MTs, mitochondrial processes and the UPR by which individuals might maintain cognition despite the presence of AD pathology.
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Affiliation(s)
- Luuk E de Vries
- Department of Neuroregeneration, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands.
| | - Aldo Jongejan
- Amsterdam UMC Location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Jennifer Monteiro Fortes
- Department of Neuropsychiatric Disorders, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
| | - Rawien Balesar
- Department of Neuropsychiatric Disorders, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
| | - Annemieke J M Rozemuller
- Department of Pathology, Amsterdam Neuroscience, Amsterdam UMC - Location VUmc, Amsterdam, The Netherlands
| | - Perry D Moerland
- Amsterdam UMC Location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Inge Huitinga
- Department of Neuroimmunology, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
- Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Dick F Swaab
- Department of Neuropsychiatric Disorders, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
| | - Joost Verhaagen
- Department of Neuroregeneration, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands.
- Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.
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8
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Morgan GR, Carlyle BC. Interrogation of the human cortical peptidome uncovers cell-type specific signatures of cognitive resilience against Alzheimer's disease. Sci Rep 2024; 14:7161. [PMID: 38531951 DOI: 10.1038/s41598-024-57104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Alzheimer's disease (AD) is characterised by age-related cognitive decline. Brain accumulation of amyloid-β plaques and tau tangles is required for a neuropathological AD diagnosis, yet up to one-third of AD-pathology positive community-dwelling elderly adults experience no symptoms of cognitive decline during life. Conversely, some exhibit chronic cognitive impairment in absence of measurable neuropathology, prompting interest into cognitive resilience-retained cognition despite significant neuropathology-and cognitive frailty-impaired cognition despite low neuropathology. Synapse loss is widespread within the AD-dementia, but not AD-resilient, brain. Recent evidence points towards critical roles for synaptic proteins, such as neurosecretory VGF, in cognitive resilience. However, VGF and related proteins often signal as peptide derivatives. Here, nontryptic peptidomic mass spectrometry was performed on 102 post-mortem cortical samples from individuals across cognitive and neuropathological spectra. Neuropeptide signalling proteoforms derived from VGF, somatostatin (SST) and protachykinin-1 (TAC1) showed higher abundance in AD-resilient than AD-dementia brain, whereas signalling proteoforms of cholecystokinin (CCK) and chromogranin (CHG) A/B and multiple cytoskeletal molecules were enriched in frail vs control brain. Integrating our data with publicly available single nuclear RNA sequencing (snRNA-seq) showed enrichment of cognition-related genes in defined cell-types with established links to cognitive resilience, including SST interneurons and excitatory intratelencephalic cells.
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Affiliation(s)
- G R Morgan
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, OX1 3QU, UK
| | - B C Carlyle
- Department of Physiology, Anatomy & Genetics, University of Oxford, Oxford, OX1 3QU, UK.
- Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford, OX1 3QU, UK.
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9
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Iacono D, Hatch K, Murphy EK, Post J, Cole RN, Perl DP, Day RM. Proteomic changes in the hippocampus of large mammals after total-body low dose radiation. PLoS One 2024; 19:e0296903. [PMID: 38427613 PMCID: PMC10906861 DOI: 10.1371/journal.pone.0296903] [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: 08/09/2023] [Accepted: 12/19/2023] [Indexed: 03/03/2024] Open
Abstract
There is a growing interest in low dose radiation (LDR) to counteract neurodegeneration. However, LDR effects on normal brain have not been completely explored yet. Recent analyses showed that LDR exposure to normal brain tissue causes expression level changes of different proteins including neurodegeneration-associated proteins. We assessed the proteomic changes occurring in radiated vs. sham normal swine brains. Due to its involvement in various neurodegenerative processes, including those associated with cognitive changes after high dose radiation exposure, we focused on the hippocampus first. We observed significant proteomic changes in the hippocampus of radiated vs. sham swine after LDR (1.79Gy). Mass spectrometry results showed 190 up-regulated and 120 down-regulated proteins after LDR. Western blotting analyses confirmed increased levels of TPM1, TPM4, PCP4 and NPY (all proteins decreased in various neurodegenerative processes, with NPY and PCP4 known to be neuroprotective) in radiated vs. sham swine. These data support the use of LDR as a potential beneficial tool to interfere with neurodegenerative processes and perhaps other brain-related disorders, including behavioral disorders.
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Affiliation(s)
- Diego Iacono
- DoD/USU Brain Tissue Repository & Neuropathology Program, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- Department of Neurology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- Neuroscience Program, Department of Anatomy, Physiology and Genetics (APG), F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland, United States of America
- Neurodegeneration Disorders Clinic, National Institute of Neurological Disorders and Stroke, NINDS, NIH, Bethesda, Maryland, United States of America
| | - Kathleen Hatch
- DoD/USU Brain Tissue Repository & Neuropathology Program, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland, United States of America
| | - Erin K. Murphy
- DoD/USU Brain Tissue Repository & Neuropathology Program, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc. (HJF), Bethesda, Maryland, United States of America
| | - Jeremy Post
- Mass Spectrometry and Proteomics, Department of Biological Chemistry, Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States of America
| | - Robert N. Cole
- Mass Spectrometry and Proteomics, Department of Biological Chemistry, Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States of America
| | - Daniel P. Perl
- DoD/USU Brain Tissue Repository & Neuropathology Program, Uniformed Services University (USU), Bethesda, Maryland, United States of America
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University (USU), Bethesda, Maryland, United States of America
| | - Regina M. Day
- Department of Pharmacology and Molecular Therapeutics, Uniformed Services University (USU), Bethesda, Maryland, United States of America
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Mahzarnia A, Lutz MW, Badea A. A Continuous Extension of Gene Set Enrichment Analysis Using the Likelihood Ratio Test Statistics Identifies Vascular Endothelial Growth Factor as a Candidate Pathway for Alzheimer's Disease via ITGA5. J Alzheimers Dis 2024; 97:635-648. [PMID: 38160360 PMCID: PMC10836573 DOI: 10.3233/jad-230934] [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] [Accepted: 11/01/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) involves brain neuropathologies such as amyloid plaque and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand disease etiology and devise therapies. OBJECTIVE Our objective was to identify molecular pathways associated with hallmark AD biomarkers and cognitive status, accounting for variables such as age, sex, education, and APOE genotype. METHODS We introduce a pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (amyloid-β, tau, cognition) using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved 634 subjects with data available for all three phenotypes, allowing for the identification of common pathways. RESULTS We identified 14 pathways significantly associated with amyloid-β; 5 associated with tau; and 174 associated with cognition, which showed a larger number of pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited associations with all three phenotypes. Mediation analysis showed that among the VEGF-RB family genes, ITGA5 mediates the relationship between cognitive scores and pathological biomarkers. CONCLUSIONS We presented a new statistical approach linking continuous phenotypes, gene expression across pathways, and covariates like sex, age, and education. Our results reinforced VEGF RB2's role in AD cognition and demonstrated ITGA5's significant role in mediating the AD pathology-cognition connection.
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Affiliation(s)
- Ali Mahzarnia
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Michael W. Lutz
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Alexandra Badea
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
- Biomedical Engineering, Duke University, Durham, NC, USA
- Brain Imaging and Analysis Center, Duke University School of Medicine, Durham, NC, USA
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Montine KS, Berson E, Phongpreecha T, Huang Z, Aghaeepour N, Zou JY, MacCoss MJ, Montine TJ. Understanding the molecular basis of resilience to Alzheimer's disease. Front Neurosci 2023; 17:1311157. [PMID: 38192507 PMCID: PMC10773681 DOI: 10.3389/fnins.2023.1311157] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024] Open
Abstract
The cellular and molecular distinction between brain aging and neurodegenerative disease begins to blur in the oldest old. Approximately 15-25% of observations in humans do not fit predicted clinical manifestations, likely the result of suppressed damage despite usually adequate stressors and of resilience, the suppression of neurological dysfunction despite usually adequate degeneration. Factors during life may predict the clinico-pathologic state of resilience: cardiovascular health and mental health, more so than educational attainment, are predictive of a continuous measure of resilience to Alzheimer's disease (AD) and AD-related dementias (ADRDs). In resilience to AD alone (RAD), core features include synaptic and axonal processes, especially in the hippocampus. Future focus on larger and more diverse cohorts and additional regions offer emerging opportunities to understand this counterforce to neurodegeneration. The focus of this review is the molecular basis of resilience to AD.
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Affiliation(s)
| | - Eloïse Berson
- Department of Pathology, Stanford University, Stanford, CA, United States
- Department of Anesthesiology, Stanford University, Stanford, CA, United States
| | - Thanaphong Phongpreecha
- Department of Pathology, Stanford University, Stanford, CA, United States
- Department of Anesthesiology, Stanford University, Stanford, CA, United States
| | - Zhi Huang
- Department of Pathology, Stanford University, Stanford, CA, United States
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - Nima Aghaeepour
- Department of Anesthesiology, Stanford University, Stanford, CA, United States
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
| | - James Y. Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, United States
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, United States
| | - Thomas J. Montine
- Department of Pathology, Stanford University, Stanford, CA, United States
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Mahzarnia A, Lutz MW, Badea A. A Continuous Extension of Gene Set Enrichment Analysis using the Likelihood Ratio Test Statistics Identifies VEGF as a Candidate Pathway for Alzheimer's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.22.554319. [PMID: 37662249 PMCID: PMC10473614 DOI: 10.1101/2023.08.22.554319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Background Alzheimer's disease involves brain pathologies such as amyloid plaque depositions and hyperphosphorylated tau tangles and is accompanied by cognitive decline. Identifying the biological mechanisms underlying disease onset and progression based on quantifiable phenotypes will help understand the disease etiology and devise therapies. Objective Our objective was to identify molecular pathways associated with AD biomarkers (Amyloid-β and tau) and cognitive status (MMSE) accounting for variables such as age, sex, education, and APOE genotype. Methods We introduce a novel pathway-based statistical approach, extending the gene set likelihood ratio test to continuous phenotypes. We first analyzed independently each of the three phenotypes (Amyloid-β, tau, cognition), using continuous gene set likelihood ratio tests to account for covariates, including age, sex, education, and APOE genotype. The analysis involved a large sample size with data available for all three phenotypes, allowing for the identification of common pathways. Results We identified 14 pathways significantly associated with Amyloid-β, 5 associated with tau, and 174 associated with MMSE. Surprisingly, the MMSE outcome showed a larger number of significant pathways compared to biomarkers. A single pathway, vascular endothelial growth factor receptor binding (VEGF-RB), exhibited significant associations with all three phenotypes. Conclusions The study's findings highlight the importance of the VEGF signaling pathway in aging in AD. The complex interactions within the VEGF signaling family offer valuable insights for future therapeutic interventions.
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Stephen TL, Korobkova L, Breningstall B, Nguyen K, Mehta S, Pachicano M, Jones KT, Hawes D, Cabeen RP, Bienkowski MS. Machine Learning Classification of Alzheimer's Disease Pathology Reveals Diffuse Amyloid as a Major Predictor of Cognitive Impairment in Human Hippocampal Subregions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.31.543117. [PMID: 37333119 PMCID: PMC10274752 DOI: 10.1101/2023.05.31.543117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Analyzing Alzheimer's disease (AD) pathology within anatomical subregions is a significant challenge, often carried out by pathologists using a standardized, semi-quantitative approach. To augment traditional methods, a high-throughput, high-resolution pipeline was created to classify the distribution of AD pathology within hippocampal subregions. USC ADRC post-mortem tissue sections from 51 patients were stained with 4G8 for amyloid, Gallyas for neurofibrillary tangles (NFTs) and Iba1 for microglia. Machine learning (ML) techniques were utilized to identify and classify amyloid pathology (dense, diffuse and APP (amyloid precursor protein)), NFTs, neuritic plaques and microglia. These classifications were overlaid within manually segmented regions (aligned with the Allen Human Brain Atlas) to create detailed pathology maps. Cases were separated into low, intermediate, or high AD stages. Further data extraction enabled quantification of plaque size and pathology density alongside ApoE genotype, sex, and cognitive status. Our findings revealed that the increase in pathology burden across AD stages was driven mainly by diffuse amyloid. The pre and para-subiculum had the highest levels of diffuse amyloid while NFTs were highest in the A36 region in high AD cases. Moreover, different pathology types had distinct trajectories across disease stages. In a subset of AD cases, microglia were elevated in intermediate and high compared to low AD. Microglia also correlated with amyloid pathology in the Dentate Gyrus. The size of dense plaques, which may represent microglial function, was lower in ApoE4 carriers. In addition, individuals with memory impairment had higher levels of both dense and diffuse amyloid. Taken together, our findings integrating ML classification approaches with anatomical segmentation maps provide new insights on the complexity of disease pathology in AD progression. Specifically, we identified diffuse amyloid pathology as being a major driver of AD in our cohort, regions of interest and microglial responses that might advance AD diagnosis and treatment.
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