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Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fi...

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Autores principales: Palstrøm, Nicolai Bjødstrup, Rojek, Aleksandra M., Møller, Hanne E. H., Hansen, Charlotte Toftmann, Matthiesen, Rune, Rasmussen, Lars Melholt, Abildgaard, Niels, Beck, Hans Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745254/
https://www.ncbi.nlm.nih.gov/pubmed/35008745
http://dx.doi.org/10.3390/ijms23010319
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author Palstrøm, Nicolai Bjødstrup
Rojek, Aleksandra M.
Møller, Hanne E. H.
Hansen, Charlotte Toftmann
Matthiesen, Rune
Rasmussen, Lars Melholt
Abildgaard, Niels
Beck, Hans Christian
author_facet Palstrøm, Nicolai Bjødstrup
Rojek, Aleksandra M.
Møller, Hanne E. H.
Hansen, Charlotte Toftmann
Matthiesen, Rune
Rasmussen, Lars Melholt
Abildgaard, Niels
Beck, Hans Christian
author_sort Palstrøm, Nicolai Bjødstrup
collection PubMed
description Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.
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spelling pubmed-87452542022-01-11 Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics Palstrøm, Nicolai Bjødstrup Rojek, Aleksandra M. Møller, Hanne E. H. Hansen, Charlotte Toftmann Matthiesen, Rune Rasmussen, Lars Melholt Abildgaard, Niels Beck, Hans Christian Int J Mol Sci Article Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel “amyloid signature” proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype. MDPI 2021-12-28 /pmc/articles/PMC8745254/ /pubmed/35008745 http://dx.doi.org/10.3390/ijms23010319 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Palstrøm, Nicolai Bjødstrup
Rojek, Aleksandra M.
Møller, Hanne E. H.
Hansen, Charlotte Toftmann
Matthiesen, Rune
Rasmussen, Lars Melholt
Abildgaard, Niels
Beck, Hans Christian
Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics
title Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics
title_full Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics
title_fullStr Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics
title_full_unstemmed Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics
title_short Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics
title_sort classification of amyloidosis by model-assisted mass spectrometry-based proteomics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8745254/
https://www.ncbi.nlm.nih.gov/pubmed/35008745
http://dx.doi.org/10.3390/ijms23010319
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