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Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19
BACKGROUND: SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiat...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040313/ https://www.ncbi.nlm.nih.gov/pubmed/36967377 http://dx.doi.org/10.1186/s12014-023-09403-2 |
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author | Hirdman, Gabriel Bodén, Embla Kjellström, Sven Fraenkel, Carl-Johan Olm, Franziska Hallgren, Oskar Lindstedt, Sandra |
author_facet | Hirdman, Gabriel Bodén, Embla Kjellström, Sven Fraenkel, Carl-Johan Olm, Franziska Hallgren, Oskar Lindstedt, Sandra |
author_sort | Hirdman, Gabriel |
collection | PubMed |
description | BACKGROUND: SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool. METHODS: Patients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a “particles in exhaled air” (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92. RESULTS: Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs. CONCLUSION: Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-023-09403-2. |
format | Online Article Text |
id | pubmed-10040313 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-100403132023-03-27 Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 Hirdman, Gabriel Bodén, Embla Kjellström, Sven Fraenkel, Carl-Johan Olm, Franziska Hallgren, Oskar Lindstedt, Sandra Clin Proteomics Research BACKGROUND: SARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool. METHODS: Patients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a “particles in exhaled air” (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92. RESULTS: Significant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs. CONCLUSION: Our results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12014-023-09403-2. BioMed Central 2023-03-27 /pmc/articles/PMC10040313/ /pubmed/36967377 http://dx.doi.org/10.1186/s12014-023-09403-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hirdman, Gabriel Bodén, Embla Kjellström, Sven Fraenkel, Carl-Johan Olm, Franziska Hallgren, Oskar Lindstedt, Sandra Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 |
title | Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 |
title_full | Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 |
title_fullStr | Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 |
title_full_unstemmed | Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 |
title_short | Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19 |
title_sort | proteomic characteristics and diagnostic potential of exhaled breath particles in patients with covid-19 |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10040313/ https://www.ncbi.nlm.nih.gov/pubmed/36967377 http://dx.doi.org/10.1186/s12014-023-09403-2 |
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