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Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease
The traditional approach to biomarker discovery for any pathology has been through hypothesis-based research one candidate at a time. The objective of this study was to develop an agnostic approach for the simultaneous screening of plasma for consistent molecular differences between a group of indiv...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781383/ https://www.ncbi.nlm.nih.gov/pubmed/33395442 http://dx.doi.org/10.1371/journal.pone.0243902 |
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author | Penner, Gregory Lecocq, Soizic Chopin, Anaëlle Vedoya, Ximena Lista, Simone Vergallo, Andrea Cavedo, Enrica Lejeune, Francois-Xavier Dubois, Bruno Hampel, Harald |
author_facet | Penner, Gregory Lecocq, Soizic Chopin, Anaëlle Vedoya, Ximena Lista, Simone Vergallo, Andrea Cavedo, Enrica Lejeune, Francois-Xavier Dubois, Bruno Hampel, Harald |
author_sort | Penner, Gregory |
collection | PubMed |
description | The traditional approach to biomarker discovery for any pathology has been through hypothesis-based research one candidate at a time. The objective of this study was to develop an agnostic approach for the simultaneous screening of plasma for consistent molecular differences between a group of individuals exhibiting a pathology and a group of healthy individuals. To achieve this, we focused on developing a predictive tool based on plasma for the amount of brain amyloid-β deposition as observed in PET scans. The accumulation of brain amyloid-β (Aβ) plaques is a key risk factor for the development of Alzheimer’s disease. A contrast was established between cognitively normal individuals above the age of 70 that differed for the amount of brain amyloid-β observed in PET scans (INSIGHT study group). Positive selection was performed against a pool of plasma from individuals with high brain amyloid and negative selection against a pool of plasma from individuals with low brain amyloid This enriched, selected library was then applied to plasma samples from 11 individuals with high levels of brain amyloid and 11 individuals with low levels of brain Aβ accumulation. Each of these individually selected libraries was then characterized by next generation sequencing, and the relative frequency of 10,000 aptamer sequences that were observed in each selection was screened for ability to explain variation in brain amyloid using sparse partial least squares discriminant analysis. From this analysis a subset of 44 aptamers was defined, and the individual aptamers were synthesized. This subset was applied to plasma samples from 70 cognitively normal individuals all above the age of 70 that differed for brain amyloid deposition. 54 individuals were used as a training set, and 15 as a test set. Three of the 15 individuals in the test set were mis-classified resulting in an overall accuracy of 80% with 86% sensitivity and 75% specificity. The aptamers included in the subset serve directly as biomarkers, thus we have named them Aptamarkers. There are two potential applications of these results: extending the predictive capacity of these aptamers across a broader range of individuals, and/or using the individual aptamers to identify targets through covariance analysis and reverse omics approaches. We are currently expanding applications of the Aptamarker platform to other diseases and target matrices. |
format | Online Article Text |
id | pubmed-7781383 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77813832021-01-07 Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease Penner, Gregory Lecocq, Soizic Chopin, Anaëlle Vedoya, Ximena Lista, Simone Vergallo, Andrea Cavedo, Enrica Lejeune, Francois-Xavier Dubois, Bruno Hampel, Harald PLoS One Research Article The traditional approach to biomarker discovery for any pathology has been through hypothesis-based research one candidate at a time. The objective of this study was to develop an agnostic approach for the simultaneous screening of plasma for consistent molecular differences between a group of individuals exhibiting a pathology and a group of healthy individuals. To achieve this, we focused on developing a predictive tool based on plasma for the amount of brain amyloid-β deposition as observed in PET scans. The accumulation of brain amyloid-β (Aβ) plaques is a key risk factor for the development of Alzheimer’s disease. A contrast was established between cognitively normal individuals above the age of 70 that differed for the amount of brain amyloid-β observed in PET scans (INSIGHT study group). Positive selection was performed against a pool of plasma from individuals with high brain amyloid and negative selection against a pool of plasma from individuals with low brain amyloid This enriched, selected library was then applied to plasma samples from 11 individuals with high levels of brain amyloid and 11 individuals with low levels of brain Aβ accumulation. Each of these individually selected libraries was then characterized by next generation sequencing, and the relative frequency of 10,000 aptamer sequences that were observed in each selection was screened for ability to explain variation in brain amyloid using sparse partial least squares discriminant analysis. From this analysis a subset of 44 aptamers was defined, and the individual aptamers were synthesized. This subset was applied to plasma samples from 70 cognitively normal individuals all above the age of 70 that differed for brain amyloid deposition. 54 individuals were used as a training set, and 15 as a test set. Three of the 15 individuals in the test set were mis-classified resulting in an overall accuracy of 80% with 86% sensitivity and 75% specificity. The aptamers included in the subset serve directly as biomarkers, thus we have named them Aptamarkers. There are two potential applications of these results: extending the predictive capacity of these aptamers across a broader range of individuals, and/or using the individual aptamers to identify targets through covariance analysis and reverse omics approaches. We are currently expanding applications of the Aptamarker platform to other diseases and target matrices. Public Library of Science 2021-01-04 /pmc/articles/PMC7781383/ /pubmed/33395442 http://dx.doi.org/10.1371/journal.pone.0243902 Text en © 2021 Penner et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Penner, Gregory Lecocq, Soizic Chopin, Anaëlle Vedoya, Ximena Lista, Simone Vergallo, Andrea Cavedo, Enrica Lejeune, Francois-Xavier Dubois, Bruno Hampel, Harald Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease |
title | Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease |
title_full | Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease |
title_fullStr | Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease |
title_full_unstemmed | Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease |
title_short | Aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for Alzheimer’s disease |
title_sort | aptamarker prediction of brain amyloid-β status in cognitively normal individuals at risk for alzheimer’s disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7781383/ https://www.ncbi.nlm.nih.gov/pubmed/33395442 http://dx.doi.org/10.1371/journal.pone.0243902 |
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