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      CSF tau microtubule-binding region identifies pathological changes in primary tauopathies


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          Despite recent advances in fluid biomarker research in Alzheimer’s disease (AD), there are no fluid biomarkers or imaging tracers with utility for diagnosis and/or theragnosis available for other tauopathies. Using immunoprecipitation and mass spectrometry, we show that 4 repeat (4R) isoform-specific tau species from microtubule-binding region (MTBR-tau 275 and MTBR-tau 282) increase in the brains of corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), frontotemporal lobar degeneration (FTLD)- MAPT and AD but decrease inversely in the cerebrospinal fluid (CSF) of CBD, FTLD- MAPT and AD compared to control and other FTLD-tau (for example, Pick’s disease). CSF MTBR-tau measures are reproducible in repeated lumbar punctures and can be used to distinguish CBD from control (receiver operating characteristic area under the curve (AUC) = 0.889) and other FTLD-tau, such as PSP (AUC = 0.886). CSF MTBR-tau 275 and MTBR-tau 282 may represent the first affirmative biomarkers to aid in the diagnosis of primary tauopathies and facilitate clinical trial designs.


          Cerebrospinal fluid measures of isoform-specific tau species from the microtubule-binding region serve as the first fluid biomarkers of primary tauopathy.

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          Most cited references38

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            Cryo-EM structures of Tau filaments from Alzheimer’s disease brain

            Alzheimer’s disease (AD) is the most common neurodegenerative disease, and there are no mechanism-based therapies. AD is defined by the presence of abundant neurofibrillary lesions and neuritic plaques in cerebral cortex. Neurofibrillary lesions are made of paired helical and straight Tau filaments (PHFs and SFs), whereas Tau filaments with different morphologies characterize other neurodegenerative diseases. No high-resolution structures of Tau filaments are available. Here we present cryo-electron microscopy (cryo-EM) maps at 3.4–3.5 Å resolution and corresponding atomic models of PHFs and SFs from AD brain. Filament cores are made of two identical protofilaments comprising residues 306–378 of Tau, which adopt a combined cross-β/β-helix structure and define the seed for Tau aggregation. PHFs and SFs differ in their inter-protofilament packing, showing that they are ultrastructural polymorphs. These findings demonstrate that cryo-EM allows atomic characterization of amyloid filaments from patient-derived material, and pave the way to study a range of neurodegenerative diseases.
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              High performance plasma amyloid-β biomarkers for Alzheimer’s disease

              To facilitate clinical trials of disease-modifying therapies for Alzheimer's disease, which are expected to be most efficacious at the earliest and mildest stages of the disease, supportive biomarker information is necessary. The only validated methods for identifying amyloid-β deposition in the brain-the earliest pathological signature of Alzheimer's disease-are amyloid-β positron-emission tomography (PET) imaging or measurement of amyloid-β in cerebrospinal fluid. Therefore, a minimally invasive, cost-effective blood-based biomarker is desirable. Despite much effort, to our knowledge, no study has validated the clinical utility of blood-based amyloid-β markers. Here we demonstrate the measurement of high-performance plasma amyloid-β biomarkers by immunoprecipitation coupled with mass spectrometry. The ability of amyloid-β precursor protein (APP)669-711/amyloid-β (Aβ)1-42 and Aβ1-40/Aβ1-42 ratios, and their composites, to predict individual brain amyloid-β-positive or -negative status was determined by amyloid-β-PET imaging and tested using two independent data sets: a discovery data set (Japan, n = 121) and a validation data set (Australia, n = 252 including 111 individuals diagnosed using 11C-labelled Pittsburgh compound-B (PIB)-PET and 141 using other ligands). Both data sets included cognitively normal individuals, individuals with mild cognitive impairment and individuals with Alzheimer's disease. All test biomarkers showed high performance when predicting brain amyloid-β burden. In particular, the composite biomarker showed very high areas under the receiver operating characteristic curves (AUCs) in both data sets (discovery, 96.7%, n = 121 and validation, 94.1%, n = 111) with an accuracy approximately equal to 90% when using PIB-PET as a standard of truth. Furthermore, test biomarkers were correlated with amyloid-β-PET burden and levels of Aβ1-42 in cerebrospinal fluid. These results demonstrate the potential clinical utility of plasma biomarkers in predicting brain amyloid-β burden at an individual level. These plasma biomarkers also have cost-benefit and scalability advantages over current techniques, potentially enabling broader clinical access and efficient population screening.

                Author and article information

                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                24 November 2022
                24 November 2022
                : 28
                : 12
                : 2547-2554
                [1 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Neurology, , Washington University School of Medicine, ; St. Louis, MO USA
                [2 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, The Tracy Family Stable Isotope Labeling Quantitation Center, , Washington University School of Medicine, ; St. Louis, MO USA
                [3 ]GRID grid.266102.1, ISNI 0000 0001 2297 6811, Department of Neurology, , University of California San Francisco, ; San Francisco, CA USA
                [4 ]GRID grid.83440.3b, ISNI 0000000121901201, Department of Neurology, University College London Queen Square Institute of Neurology, , University College London, ; London, UK
                [5 ]GRID grid.266100.3, ISNI 0000 0001 2107 4242, Department of Neurosciences, University of California San Diego School of Medicine, ; La Jolla, CA USA
                [6 ]GRID grid.417467.7, ISNI 0000 0004 0443 9942, Department of Neurology, , Mayo Clinic Florida, ; Jacksonville, FL USA
                [7 ]GRID grid.512651.4, Hope Center for Neurological Disorders, ; St. Louis, MO USA
                [8 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Psychiatry, , Washington University School of Medicine, ; St. Louis, MO USA
                [9 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Charles F. and Joanne Knight Alzheimer Disease Research Center, , Washington University School of Medicine, ; St. Louis, MO USA
                [10 ]GRID grid.4367.6, ISNI 0000 0001 2355 7002, Department of Pathology and Immunology, , Washington University School of Medicine, ; St. Louis, MO USA
                [11 ]GRID grid.121334.6, ISNI 0000 0001 2097 0141, Memory Research and Resources Center, Department of Neurology, , University Hospital of Montpellier, Neurosciences Institute of Montpellier, University of Montpellier, ; Montpellier, France
                Author information
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                : 2 May 2022
                : 5 October 2022
                Funded by: FundRef https://doi.org/10.13039/100000049, U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging);
                Award ID: K01AG062796
                Award ID: K23 AG064029
                Award ID: P50 AG023501
                Award ID: P01 AG019724
                Award ID: U19AG063911
                Award ID: R01AG038791
                Award ID: R01 AG073482
                Award ID: U24AG057437
                Award Recipient :
                Funded by: Rainwater Charitable Foundation
                Funded by: Rainwater Charitable Foundation, The Association for Frontotemporal Degeneration, BIRCWH K12 HD001459
                Funded by: Rainwater Charitable Foundation, Barnes Jewish Hospital Foundation (#3945), The Association for Frontotemporal Degeneration
                Funded by: Rainwater Charitable Foundation, The Association for Frontotemporal Degeneration
                Funded by: FundRef https://doi.org/10.13039/100000065, U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS);
                Award ID: K08 NS101118
                Award ID: NS110890
                Award ID: R01NS065667
                Award ID: R01NS095773
                Award Recipient :
                Funded by: Rainwater Charitable Foundation, ADRC grant / P30 AG066444
                Funded by: Tau Foundation plan Alzheimer
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                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                neurodegenerative diseases,diagnostic markers
                neurodegenerative diseases, diagnostic markers


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