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Seifar F, Fox EJ, Shantaraman A, Liu Y, Dammer EB, Modeste E, Duong DM, Yin L, Trautwig AN, Guo Q, Xu K, Ping L, Reddy JS, Allen M, Quicksall Z, Heath L, Scanlan J, Wang E, Wang M, Linden AV, Poehlman W, Chen X, Baheti S, Ho C, Nguyen T, Yepez G, Mitchell AO, Oatman SR, Wang X, Carrasquillo MM, Runnels A, Beach T, Serrano GE, Dickson DW, Lee EB, Golde TE, Prokop S, Barnes LL, Zhang B, Haroutunian V, Gearing M, Lah JJ, Jager PD, Bennett DA, Greenwood A, Ertekin-Taner N, Levey AI, Wingo A, Wingo T, Seyfried NT. Large-scale Deep Proteomic Analysis in Alzheimer's Disease Brain Regions Across Race and Ethnicity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590547. [PMID: 38712030 PMCID: PMC11071432 DOI: 10.1101/2024.04.22.590547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Introduction Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet our comprehension predominantly relies on studies within the non-Hispanic White (NHW) population. Here we aimed to provide comprehensive insights into the proteomic landscape of AD across diverse racial and ethnic groups. Methods Dorsolateral prefrontal cortex (DLPFC) and superior temporal gyrus (STG) brain tissues were donated from multiple centers (Mayo Clinic, Emory University, Rush University, Mt. Sinai School of Medicine) and were harmonized through neuropathological evaluation, specifically adhering to the Braak staging and CERAD criteria. Among 1105 DLPFC tissue samples (998 unique individuals), 333 were from African American donors, 223 from Latino Americans, 529 from NHW donors, and the rest were from a mixed or unknown racial background. Among 280 STG tissue samples (244 unique individuals), 86 were African American, 76 Latino American, 116 NHW and the rest were mixed or unknown ethnicity. All tissues were uniformly homogenized and analyzed by tandem mass tag mass spectrometry (TMT-MS). Results As a Quality control (QC) measure, proteins with more than 50% missing values were removed and iterative principal component analysis was conducted to remove outliers within brain regions. After QC, 9,180 and 9,734 proteins remained in the DLPC and STG proteome, respectively, of which approximately 9,000 proteins were shared between regions. Protein levels of microtubule-associated protein tau (MAPT) and amyloid-precursor protein (APP) demonstrated AD-related elevations in DLPFC tissues with a strong association with CERAD and Braak across racial groups. APOE4 protein levels in brain were highly concordant with APOE genotype of the individuals. Discussion This comprehensive region resolved large-scale proteomic dataset provides a resource for the understanding of ethnoracial-specific protein differences in AD brain.
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Affiliation(s)
| | - Edward J Fox
- Emory University School of Medicine, Atlanta, GA USA
| | | | - Yue Liu
- Emory University School of Medicine, Atlanta, GA USA
| | - Eric B Dammer
- Emory University School of Medicine, Atlanta, GA USA
| | - Erica Modeste
- Emory University School of Medicine, Atlanta, GA USA
| | - Duc M Duong
- Emory University School of Medicine, Atlanta, GA USA
| | - Luming Yin
- Emory University School of Medicine, Atlanta, GA USA
| | | | - Qi Guo
- Emory University School of Medicine, Atlanta, GA USA
| | - Kaiming Xu
- Emory University School of Medicine, Atlanta, GA USA
| | - Lingyan Ping
- Emory University School of Medicine, Atlanta, GA USA
| | - Joseph S Reddy
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Mariet Allen
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Zachary Quicksall
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | | | | | - Erming Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | | | - Xianfeng Chen
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Saurabh Baheti
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Charlotte Ho
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Thuy Nguyen
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Geovanna Yepez
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | | | | | - Xue Wang
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | | | | | - Thomas Beach
- Banner Sun Health Research Institute, Sun City, AR USA
| | | | - Dennis W Dickson
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
| | - Edward B Lee
- Center for Neurodegenerative Disease Research, University of Pennsylvania, Philadelpha, PA, USA
| | - Todd E Golde
- Emory University School of Medicine, Atlanta, GA USA
| | | | - Lisa L Barnes
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Varham Haroutunian
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Marla Gearing
- Emory University School of Medicine, Atlanta, GA USA
| | - James J Lah
- Emory University School of Medicine, Atlanta, GA USA
| | | | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL USA
| | | | - Nilüfer Ertekin-Taner
- Mayo Clinic Florida, Department of Neuroscience, Jacksonville, FL USA
- Mayo Clinic Florida, Department of Neurology, Jacksonville, FL USA
| | - Allan I Levey
- Emory University School of Medicine, Atlanta, GA USA
| | - Aliza Wingo
- Emory University School of Medicine, Atlanta, GA USA
| | - Thomas Wingo
- Emory University School of Medicine, Atlanta, GA USA
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Wisch JK, Butt OH, Gordon BA, Schindler SE, Fagan AM, Henson RL, Yang C, Boerwinkle AH, Benzinger TLS, Holtzman DM, Morris JC, Cruchaga C, Ances BM. Proteomic clusters underlie heterogeneity in preclinical Alzheimer's disease progression. Brain 2023; 146:2944-2956. [PMID: 36542469 PMCID: PMC10316757 DOI: 10.1093/brain/awac484] [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: 04/05/2022] [Revised: 11/21/2022] [Accepted: 11/30/2022] [Indexed: 12/24/2022] Open
Abstract
Heterogeneity in progression to Alzheimer's disease (AD) poses challenges for both clinical prognosis and clinical trial implementation. Multiple AD-related subtypes have previously been identified, suggesting differences in receptivity to drug interventions. We identified early differences in preclinical AD biomarkers, assessed patterns for developing preclinical AD across the amyloid-tau-(neurodegeneration) [AT(N)] framework, and considered potential sources of difference by analysing the CSF proteome. Participants (n = 10) enrolled in longitudinal studies at the Knight Alzheimer Disease Research Center completed four or more lumbar punctures. These individuals were cognitively normal at baseline. Cerebrospinal fluid measures of amyloid-β (Aβ)42, phosphorylated tau (pTau181), and neurofilament light chain (NfL) as well as proteomics values were evaluated. Imaging biomarkers, including PET amyloid and tau, and structural MRI, were repeatedly obtained when available. Individuals were staged according to the amyloid-tau-(neurodegeneration) framework. Growth mixture modelling, an unsupervised clustering technique, identified three patterns of biomarker progression as measured by CSF pTau181 and Aβ42. Two groups (AD Biomarker Positive and Intermediate AD Biomarker) showed distinct progression from normal biomarker status to having biomarkers consistent with preclinical AD. A third group (AD Biomarker Negative) did not develop abnormal AD biomarkers over time. Participants grouped by CSF trajectories were re-classified using only proteomic profiles (AUCAD Biomarker Positive versus AD Biomarker Negative = 0.857, AUCAD Biomarker Positive versus Intermediate AD Biomarkers = 0.525, AUCIntermediate AD Biomarkers versus AD Biomarker Negative = 0.952). We highlight heterogeneity in the development of AD biomarkers in cognitively normal individuals. We identified some individuals who became amyloid positive before the age of 50 years. A second group, Intermediate AD Biomarkers, developed elevated CSF ptau181 significantly before becoming amyloid positive. A third group were AD Biomarker Negative over repeated testing. Our results could influence the selection of participants for specific treatments (e.g. amyloid-reducing versus other agents) in clinical trials. CSF proteome analysis highlighted additional non-AT(N) biomarkers for potential therapies, including blood-brain barrier-, vascular-, immune-, and neuroinflammatory-related targets.
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Affiliation(s)
- Julie K Wisch
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Omar H Butt
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Brian A Gordon
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Suzanne E Schindler
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anne M Fagan
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Rachel L Henson
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Chengran Yang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, USA
- NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Anna H Boerwinkle
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
| | - Tammie L S Benzinger
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David M Holtzman
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C Morris
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Carlos Cruchaga
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Beau M Ances
- Department of Neurology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, MO 63110, USA
- Hope Center, Washington University in Saint Louis, St. Louis, MO 63110, USA
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
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Therapeutic Approach to Alzheimer’s Disease: Current Treatments and New Perspectives. Pharmaceutics 2022; 14:pharmaceutics14061117. [PMID: 35745693 PMCID: PMC9228613 DOI: 10.3390/pharmaceutics14061117] [Citation(s) in RCA: 62] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 05/16/2022] [Accepted: 05/20/2022] [Indexed: 12/11/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common cause of dementia. The pathophysiology of this disease is characterized by the accumulation of amyloid-β, leading to the formation of senile plaques, and by the intracellular presence of neurofibrillary tangles based on hyperphosphorylated tau protein. In the therapeutic approach to AD, we can identify three important fronts: the approved drugs currently available for the treatment of the disease, which include aducanumab, donepezil, galantamine, rivastigmine, memantine, and a combination of memantine and donepezil; therapies under investigation that work mainly on Aβ pathology and tau pathology, and which include γ-secretase inhibitors, β-secretase inhibitors, α-secretase modulators, aggregation inhibitors, metal interfering drugs, drugs that enhance Aβ clearance, inhibitors of tau protein hyperphosphorylation, tau protein aggregation inhibitors, and drugs that promote the clearance of tau, and finally, other alternative therapies designed to improve lifestyle, thus contributing to the prevention of the disease. Therefore, the aim of this review was to analyze and describe current treatments and possible future alternatives in the therapeutic approach to AD.
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