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Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity

Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles...

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Autores principales: Iturria-Medina, Yasser, Adewale, Quadri, Khan, Ahmed F., Ducharme, Simon, Rosa-Neto, Pedro, O’Donnell, Kieran, Petyuk, Vladislav A., Gauthier, Serge, De Jager, Philip L., Breitner, John, Bennett, David A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Association for the Advancement of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674284/
https://www.ncbi.nlm.nih.gov/pubmed/36399579
http://dx.doi.org/10.1126/sciadv.abo6764
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author Iturria-Medina, Yasser
Adewale, Quadri
Khan, Ahmed F.
Ducharme, Simon
Rosa-Neto, Pedro
O’Donnell, Kieran
Petyuk, Vladislav A.
Gauthier, Serge
De Jager, Philip L.
Breitner, John
Bennett, David A.
author_facet Iturria-Medina, Yasser
Adewale, Quadri
Khan, Ahmed F.
Ducharme, Simon
Rosa-Neto, Pedro
O’Donnell, Kieran
Petyuk, Vladislav A.
Gauthier, Serge
De Jager, Philip L.
Breitner, John
Bennett, David A.
author_sort Iturria-Medina, Yasser
collection PubMed
description Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions.
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spelling pubmed-96742842022-11-29 Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity Iturria-Medina, Yasser Adewale, Quadri Khan, Ahmed F. Ducharme, Simon Rosa-Neto, Pedro O’Donnell, Kieran Petyuk, Vladislav A. Gauthier, Serge De Jager, Philip L. Breitner, John Bennett, David A. Sci Adv Neuroscience Alzheimer’s disease (AD) is a heterogeneous disorder with abnormalities in multiple biological domains. In an advanced machine learning analysis of postmortem brain and in vivo blood multi-omics molecular data (N = 1863), we integrated epigenomic, transcriptomic, proteomic, and metabolomic profiles into a multilevel biological AD taxonomy. We obtained a personalized multilevel molecular index of AD dementia progression that predicts severity of neuropathologies, and identified three robust molecular-based subtypes that explain much of the pathologic and clinical heterogeneity of AD. These subtypes present distinct patterns of alteration in DNA methylation, RNA, proteins, and metabolites, identifiable in the brain and subsequently in blood. In addition, the genetic variations that predispose to the various AD subtypes in brain predict distinct spatial patterns of alteration in cell types, suggesting a unique influence of each putative AD variant on neuropathological mechanisms. These observations support that an individually tailored multi-omics molecular taxonomy of AD may represent distinct targets for preventive or treatment interventions. American Association for the Advancement of Science 2022-11-18 /pmc/articles/PMC9674284/ /pubmed/36399579 http://dx.doi.org/10.1126/sciadv.abo6764 Text en Copyright © 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.
spellingShingle Neuroscience
Iturria-Medina, Yasser
Adewale, Quadri
Khan, Ahmed F.
Ducharme, Simon
Rosa-Neto, Pedro
O’Donnell, Kieran
Petyuk, Vladislav A.
Gauthier, Serge
De Jager, Philip L.
Breitner, John
Bennett, David A.
Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
title Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
title_full Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
title_fullStr Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
title_full_unstemmed Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
title_short Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity
title_sort unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of alzheimer’s disease progression and heterogeneity
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9674284/
https://www.ncbi.nlm.nih.gov/pubmed/36399579
http://dx.doi.org/10.1126/sciadv.abo6764
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