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Pichet Binette A, Gaiteri C, Wennström M, Kumar A, Hristovska I, Spotorno N, Salvadó G, Strandberg O, Mathys H, Tsai LH, Palmqvist S, Mattsson-Carlgren N, Janelidze S, Stomrud E, Vogel JW, Hansson O. Proteomic changes in Alzheimer's disease associated with progressive Aβ plaque and tau tangle pathologies. Nat Neurosci 2024:10.1038/s41593-024-01737-w. [PMID: 39187705 DOI: 10.1038/s41593-024-01737-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2023] [Accepted: 07/23/2024] [Indexed: 08/28/2024]
Abstract
Proteomics can shed light on the dynamic and multifaceted alterations in neurodegenerative disorders like Alzheimer's disease (AD). Combining radioligands measuring β-amyloid (Aβ) plaques and tau tangles with cerebrospinal fluid proteomics, we uncover molecular events mirroring different stages of AD pathology in living humans. We found 127 differentially abundant proteins (DAPs) across the AD spectrum. The strongest Aβ-related proteins were mainly expressed in glial cells and included SMOC1 and ITGAM. A dozen proteins linked to ATP metabolism and preferentially expressed in neurons were independently associated with tau tangle load and tau accumulation. Only 20% of the DAPs were also altered in other neurodegenerative diseases, underscoring AD's distinct proteome. Two co-expression modules related, respectively, to protein metabolism and microglial immune response encompassed most DAPs, with opposing, staggered trajectories along the AD continuum. We unveil protein signatures associated with Aβ and tau proteinopathy in vivo, offering insights into complex neural responses and potential biomarkers and therapeutics targeting different disease stages.
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Affiliation(s)
- Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
| | - Chris Gaiteri
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Rush University Alzheimer's Disease Center, Rush University, Chicago, IL, USA
| | - Malin Wennström
- Cognitive Disorder Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Atul Kumar
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Ines Hristovska
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Nicola Spotorno
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Gemma Salvadó
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Olof Strandberg
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Hansruedi Mathys
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
- University of Pittsburgh Brain Institute and Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Li-Huei Tsai
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Rush University Alzheimer's Disease Center, Rush University, Chicago, IL, USA
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Jacob W Vogel
- Department of Clinical Sciences Malmö, SciLifeLab, Lund University, Lund, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Lund, Sweden.
- Memory Clinic, Skåne University Hospital, Malmö, Sweden.
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Shantaraman A, Dammer EB, Ugochukwu O, Duong DM, Yin L, Carter EK, Gearing M, Chen-Plotkin A, Lee EB, Trojanowski JQ, Bennett DA, Lah JJ, Levey AI, Seyfried NT, Higginbotham L. Network proteomics of the Lewy body dementia brain reveals presynaptic signatures distinct from Alzheimer's disease. Mol Neurodegener 2024; 19:60. [PMID: 39107789 PMCID: PMC11302177 DOI: 10.1186/s13024-024-00749-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 07/24/2024] [Indexed: 08/10/2024] Open
Abstract
Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.
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Affiliation(s)
- Anantharaman Shantaraman
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric B Dammer
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Obiadada Ugochukwu
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Duc M Duong
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Luming Yin
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - E Kathleen Carter
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Marla Gearing
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - James J Lah
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Allan I Levey
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Nicholas T Seyfried
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA.
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA.
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
| | - Lenora Higginbotham
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA.
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
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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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Xu B, Ling Y, Liu L, Liu Y, Lin Y, Lyu J, Zhang Y. Potential prognostic value of CSF-targeted proteomics across the Alzheimer's disease continuum. BMC Geriatr 2024; 24:501. [PMID: 38844858 PMCID: PMC11157758 DOI: 10.1186/s12877-024-05104-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 05/23/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Core biomarkers for Alzheimer's disease (AD), such as Aβ42 and tau, have demonstrated high prognostic accuracy but do not fully capture the complex pathophysiology of AD. In this study, our objective was to identify novel cerebrospinal fluid (CSF) biomarkers using proteomics across the entire AD continuum to predict conversion to AD and explore their involvement in AD pathogenesis. METHODS A cohort of 186 cognitively normal (CN), 127 subjective memory complaint (SMC), 79 early mild cognitive impairment (EMCI), 249 late MCI (LMCI), and 132 AD individuals was analyzed, with a follow-up period of over 3 years for non-AD participants. CSF 65 peptides, as well as hippocampal and entorhinal volumes were analyzed, and cognitive function was evaluated using the 13-item cognitive subscale of the Alzheimer's Disease Assessment Scale (ADAS-Cog 13). Cox proportional hazards models and mediation analysis were performed to investigate associations and causal relationships. RESULTS During the follow-up, approximately one-fourth (146/580) of the non-AD participants progressed to AD. After adjusting for baseline diagnosis (CN to LMCI) and other variables, multivariable Cox regression analysis identified three peptides (VAELEDEK, VSFELFADK, and VVSSIEQK) as significant predictors of conversion to AD. Incorporating these three peptides into the initial model significantly improved the C-statistic from 0.82 to 0.85 for predicting AD conversion, surpassing the predictive ability of Aβ42 and P-tau. Moreover, hippocampal and entorhinal volumes mediated 30.3-53.8% of the association between the three peptides and ADAS-Cog 13 scores. CONCLUSIONS These findings underscore the potential of these three peptides as robust prognostic biomarker candidates for AD conversion across the entire AD continuum, with a mechanism involving the mediation of hippocampal and entorhinal volumes.
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Affiliation(s)
- Bingdong Xu
- Department of Neurology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Avenue West, Guangzhou, Guangdong, 510632, P.R. China
| | - Yitong Ling
- Department of Neurology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Avenue West, Guangzhou, Guangdong, 510632, P.R. China
| | - Leiyuan Liu
- Department of Neurology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Avenue West, Guangzhou, Guangdong, 510632, P.R. China
| | - Yujun Liu
- Department of Neurology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Avenue West, Guangzhou, Guangdong, 510632, P.R. China
| | - Yingze Lin
- Department of Neurology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Avenue West, Guangzhou, Guangdong, 510632, P.R. China
| | - Jun Lyu
- Department of Clinical Research, The First Affiliated Hospital of Jinan University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Traditional Chinese Medicine Informatization, Guangzhou, China
| | - Yusheng Zhang
- Department of Neurology, The First Affiliated Hospital of Jinan University, No. 613, Huangpu Avenue West, Guangzhou, Guangdong, 510632, P.R. China.
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5
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Mukherjee A, Biswas S, Roy I. Immunotherapy: An emerging treatment option for neurodegenerative diseases. Drug Discov Today 2024; 29:103974. [PMID: 38555032 DOI: 10.1016/j.drudis.2024.103974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 03/18/2024] [Accepted: 03/26/2024] [Indexed: 04/02/2024]
Abstract
Accumulation of misfolded proteins and protein aggregates leading to degeneration of neurons is a hallmark of several neurodegenerative diseases. Therapy mostly relies on symptomatic relief. Immunotherapy offers a promising approach for the development of disease-modifying routes. Such strategies have shown remarkable results in oncology, and this promise is increasingly being realized for neurodegenerative diseases in advanced preclinical and clinical studies. This review highlights cases of passive and active immunotherapies in Parkinson's and Alzheimer's diseases. The reasons for success and failure, wherever available, and strategies to cross the blood-brain barrier, are discussed. The need for conditional modulation of the immune response is also reflected on.
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Affiliation(s)
- Abhiyanta Mukherjee
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India
| | - Soumojit Biswas
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India
| | - Ipsita Roy
- Department of Biotechnology, National Institute of Pharmaceutical Education and Research, Sector 67, S.A.S. Nagar, Punjab 160062, India.
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Sheng J, Zhang Q, Zhang Q, Wang L, Yang Z, Xin Y, Wang B. A hybrid multimodal machine learning model for Detecting Alzheimer's disease. Comput Biol Med 2024; 170:108035. [PMID: 38325214 DOI: 10.1016/j.compbiomed.2024.108035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/03/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.
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Affiliation(s)
- Jinhua Sheng
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China.
| | - Qian Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China; School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, Zhejiang, 325035, China
| | - Qiao Zhang
- Beijing Hospital, Beijing, 100730, China; National Center of Gerontology, Beijing, 100730, China; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luyun Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Ze Yang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Yu Xin
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
| | - Binbing Wang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang, 310018, China
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7
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Guo Q, Ping L, Dammer EB, Yin L, Xu K, Shantaraman A, Fox EJ, Golde TE, Johnson ECB, Roberts BR, Lah JJ, Levey AI, Seyfried NT. Heparin-enriched plasma proteome is significantly altered in Alzheimer's Disease. RESEARCH SQUARE 2024:rs.3.rs-3933136. [PMID: 38464223 PMCID: PMC10925398 DOI: 10.21203/rs.3.rs-3933136/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: 03/12/2024]
Abstract
Introduction Heparin binding proteins (HBPs) with roles in extracellular matrix assembly are strongly correlated to β-amyloid (Aβ) and tau pathology in Alzheimer's disease (AD) brain and cerebrospinal fluid (CSF). However, it remains challenging to detect these proteins in plasma using standard mass spectrometry-based proteomic approaches. Methods We employed heparin affinity chromatography, followed by off-line fractionation and tandem mass tag mass spectrometry (TMT-MS), to capture and enrich HBPs in plasma obtained from AD (n=62) and control (n=47) samples. These profiles were then correlated to a consensus AD brain proteome, as well as with Aβ, tau and phosphorylated tau (pTau) CSF biomarkers from the same individuals. We then leveraged published human postmortem brain proteome datasets to assess the overlap with the heparin-enriched plasma proteome. Results Heparin-enrichment from plasma was highly reproducible, enriched well-known HBPs like APOE and thrombin, and depleted high-abundance proteins such as albumin. A total of 2865 proteins, spanning 10 orders of magnitude were detectable. Utilizing a consensus AD brain protein co-expression network, we observed that specific plasma HBPs exhibited consistent direction of change in both brain and plasma, whereas others displayed divergent changes highlighting the complex interplay between the two compartments. Elevated HBPs in AD plasma, when compared to controls, included members of the matrisome module in brain that accumulate within Aβ deposits, such as SMOC1, SMOC2, SPON1, MDK, OLFML3, FRZB, GPNMB, and APOE. Additionally, heparin enriched plasma proteins demonstrated significant correlations with conventional AD CSF biomarkers, including Aβ, total tau, pTau, and plasma pTau from the same individuals. Conclusion These findings support the utility of a heparin-affinity approach for enriching amyloid-associated proteins, as well as a wide spectrum of plasma biomarkers that reflect pathological changes in the AD brain.
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Affiliation(s)
- Qi Guo
- Emory University School of Medicine
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8
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Blood protein markers predict 15-year risk of dementia. NATURE AGING 2024; 4:173-174. [PMID: 38347191 DOI: 10.1038/s43587-023-00566-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
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9
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Shantaraman A, Dammer EB, Ugochukwu O, Duong DM, Yin L, Carter EK, Gearing M, Chen-Plotkin A, Lee EB, Trojanowski JQ, Bennett DA, Lah JJ, Levey AI, Seyfried NT, Higginbotham L. Network Proteomics of the Lewy Body Dementia Brain Reveals Presynaptic Signatures Distinct from Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.23.576728. [PMID: 38328211 PMCID: PMC10849701 DOI: 10.1101/2024.01.23.576728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Lewy body dementia (LBD), a class of disorders comprising Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB), features substantial clinical and pathological overlap with Alzheimer's disease (AD). The identification of biomarkers unique to LBD pathophysiology could meaningfully advance its diagnosis, monitoring, and treatment. Using quantitative mass spectrometry (MS), we measured over 9,000 proteins across 138 dorsolateral prefrontal cortex (DLPFC) tissues from a University of Pennsylvania autopsy collection comprising control, Parkinson's disease (PD), PDD, and DLB diagnoses. We then analyzed co-expression network protein alterations in those with LBD, validated these disease signatures in two independent LBD datasets, and compared these findings to those observed in network analyses of AD cases. The LBD network revealed numerous groups or "modules" of co-expressed proteins significantly altered in PDD and DLB, representing synaptic, metabolic, and inflammatory pathophysiology. A comparison of validated LBD signatures to those of AD identified distinct differences between the two diseases. Notably, synuclein-associated presynaptic modules were elevated in LBD but decreased in AD relative to controls. We also found that glial-associated matrisome signatures consistently elevated in AD were more variably altered in LBD, ultimately stratifying those LBD cases with low versus high burdens of concurrent beta-amyloid deposition. In conclusion, unbiased network proteomic analysis revealed diverse pathophysiological changes in the LBD frontal cortex distinct from alterations in AD. These results highlight the LBD brain network proteome as a promising source of biomarkers that could enhance clinical recognition and management.
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Affiliation(s)
- Anantharaman Shantaraman
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric B. Dammer
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Obiadada Ugochukwu
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
| | - Duc M. Duong
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Luming Yin
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - E. Kathleen Carter
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Marla Gearing
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
- Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA
| | - Alice Chen-Plotkin
- Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Edward B. Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - John Q. Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Bennett
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - James J. Lah
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Allan I. Levey
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Nicholas T. Seyfried
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Lenora Higginbotham
- Center for Neurodegenerative Disease, Emory University School of Medicine, Atlanta, GA, USA
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
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10
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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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11
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Vervuurt M, Schrader JM, de Kort AM, Kersten I, Wessels HJCT, Klijn CJM, Schreuder FHBM, Kuiperij HB, Gloerich J, Van Nostrand WE, Verbeek MM. Cerebrospinal fluid shotgun proteomics identifies distinct proteomic patterns in cerebral amyloid angiopathy rodent models and human patients. Acta Neuropathol Commun 2024; 12:6. [PMID: 38191511 PMCID: PMC10775534 DOI: 10.1186/s40478-023-01698-4] [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: 10/13/2023] [Accepted: 11/28/2023] [Indexed: 01/10/2024] Open
Abstract
Cerebral amyloid angiopathy (CAA) is a form of small vessel disease characterised by the progressive deposition of amyloid β protein in the cerebral vasculature, inducing symptoms including cognitive impairment and cerebral haemorrhages. Due to their accessibility and homogeneous disease phenotypes, animal models are advantageous platforms to study diseases like CAA. Untargeted proteomics studies of CAA rat models (e.g. rTg-DI) and CAA patients provide opportunities for the identification of novel biomarkers of CAA. We performed untargeted, data-independent acquisition proteomic shotgun analyses on the cerebrospinal fluid of rTg-DI rats and wild-type (WT) littermates. Rodents were analysed at 3 months (n = 6/10), 6 months (n = 8/8), and 12 months (n = 10/10) for rTg-DI and WT respectively. For humans, proteomic analyses were performed on CSF of sporadic CAA patients (sCAA) and control participants (n = 39/28). We show recurring patterns of differentially expressed (mostly increased) proteins in the rTg-DI rats compared to wild type rats, especially of proteases of the cathepsin protein family (CTSB, CTSD, CTSS), and their main inhibitor (CST3). In sCAA patients, decreased levels of synaptic proteins (e.g. including VGF, NPTX1, NRXN2) and several members of the granin family (SCG1, SCG2, SCG3, SCG5) compared to controls were discovered. Additionally, several serine protease inhibitors of the SERPIN protein family (including SERPINA3, SERPINC1 and SERPING1) were differentially expressed compared to controls. Fifteen proteins were significantly altered in both rTg-DI rats and sCAA patients, including (amongst others) SCG5 and SERPING1. These results identify specific groups of proteins likely involved in, or affected by, pathophysiological processes involved in CAA pathology such as protease and synapse function of rTg-DI rat models and sCAA patients, and may serve as candidate biomarkers for sCAA.
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Affiliation(s)
- Marc Vervuurt
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Joseph M Schrader
- Department of Biomedical and Pharmaceutical Sciences, George & Anne Institute for Neuroscience, University of Rhode Island, Kingston, RI, USA
| | - Anna M de Kort
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Iris Kersten
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Hans J C T Wessels
- Department of Human Genetics, Translational Metabolic Laboratory, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Catharina J M Klijn
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Floris H B M Schreuder
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - H Bea Kuiperij
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands
| | - Jolein Gloerich
- Department of Human Genetics, Translational Metabolic Laboratory, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands
| | - William E Van Nostrand
- Department of Biomedical and Pharmaceutical Sciences, George & Anne Institute for Neuroscience, University of Rhode Island, Kingston, RI, USA
| | - Marcel M Verbeek
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, 830 TML, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
- Department of Human Genetics, Translational Metabolic Laboratory, Radboud Institute for Molecular Life Sciences, Radboud University Medical Center, Nijmegen, The Netherlands.
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