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Distinguishing features of long COVID identified through immune profiling
Post-acute infection syndromes may develop after acute viral disease(1). Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognit...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620090/ https://www.ncbi.nlm.nih.gov/pubmed/37748514 http://dx.doi.org/10.1038/s41586-023-06651-y |
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author | Klein, Jon Wood, Jamie Jaycox, Jillian R. Dhodapkar, Rahul M. Lu, Peiwen Gehlhausen, Jeff R. Tabachnikova, Alexandra Greene, Kerrie Tabacof, Laura Malik, Amyn A. Silva Monteiro, Valter Silva, Julio Kamath, Kathy Zhang, Minlu Dhal, Abhilash Ott, Isabel M. Valle, Gabrielee Peña-Hernández, Mario Mao, Tianyang Bhattacharjee, Bornali Takahashi, Takehiro Lucas, Carolina Song, Eric McCarthy, Dayna Breyman, Erica Tosto-Mancuso, Jenna Dai, Yile Perotti, Emily Akduman, Koray Tzeng, Tiffany J. Xu, Lan Geraghty, Anna C. Monje, Michelle Yildirim, Inci Shon, John Medzhitov, Ruslan Lutchmansingh, Denyse Possick, Jennifer D. Kaminski, Naftali Omer, Saad B. Krumholz, Harlan M. Guan, Leying Dela Cruz, Charles S. van Dijk, David Ring, Aaron M. Putrino, David Iwasaki, Akiko |
author_facet | Klein, Jon Wood, Jamie Jaycox, Jillian R. Dhodapkar, Rahul M. Lu, Peiwen Gehlhausen, Jeff R. Tabachnikova, Alexandra Greene, Kerrie Tabacof, Laura Malik, Amyn A. Silva Monteiro, Valter Silva, Julio Kamath, Kathy Zhang, Minlu Dhal, Abhilash Ott, Isabel M. Valle, Gabrielee Peña-Hernández, Mario Mao, Tianyang Bhattacharjee, Bornali Takahashi, Takehiro Lucas, Carolina Song, Eric McCarthy, Dayna Breyman, Erica Tosto-Mancuso, Jenna Dai, Yile Perotti, Emily Akduman, Koray Tzeng, Tiffany J. Xu, Lan Geraghty, Anna C. Monje, Michelle Yildirim, Inci Shon, John Medzhitov, Ruslan Lutchmansingh, Denyse Possick, Jennifer D. Kaminski, Naftali Omer, Saad B. Krumholz, Harlan M. Guan, Leying Dela Cruz, Charles S. van Dijk, David Ring, Aaron M. Putrino, David Iwasaki, Akiko |
author_sort | Klein, Jon |
collection | PubMed |
description | Post-acute infection syndromes may develop after acute viral disease(1). Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions(2–4). However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers. |
format | Online Article Text |
id | pubmed-10620090 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-106200902023-11-03 Distinguishing features of long COVID identified through immune profiling Klein, Jon Wood, Jamie Jaycox, Jillian R. Dhodapkar, Rahul M. Lu, Peiwen Gehlhausen, Jeff R. Tabachnikova, Alexandra Greene, Kerrie Tabacof, Laura Malik, Amyn A. Silva Monteiro, Valter Silva, Julio Kamath, Kathy Zhang, Minlu Dhal, Abhilash Ott, Isabel M. Valle, Gabrielee Peña-Hernández, Mario Mao, Tianyang Bhattacharjee, Bornali Takahashi, Takehiro Lucas, Carolina Song, Eric McCarthy, Dayna Breyman, Erica Tosto-Mancuso, Jenna Dai, Yile Perotti, Emily Akduman, Koray Tzeng, Tiffany J. Xu, Lan Geraghty, Anna C. Monje, Michelle Yildirim, Inci Shon, John Medzhitov, Ruslan Lutchmansingh, Denyse Possick, Jennifer D. Kaminski, Naftali Omer, Saad B. Krumholz, Harlan M. Guan, Leying Dela Cruz, Charles S. van Dijk, David Ring, Aaron M. Putrino, David Iwasaki, Akiko Nature Article Post-acute infection syndromes may develop after acute viral disease(1). Infection with SARS-CoV-2 can result in the development of a post-acute infection syndrome known as long COVID. Individuals with long COVID frequently report unremitting fatigue, post-exertional malaise, and a variety of cognitive and autonomic dysfunctions(2–4). However, the biological processes that are associated with the development and persistence of these symptoms are unclear. Here 275 individuals with or without long COVID were enrolled in a cross-sectional study that included multidimensional immune phenotyping and unbiased machine learning methods to identify biological features associated with long COVID. Marked differences were noted in circulating myeloid and lymphocyte populations relative to the matched controls, as well as evidence of exaggerated humoral responses directed against SARS-CoV-2 among participants with long COVID. Furthermore, higher antibody responses directed against non-SARS-CoV-2 viral pathogens were observed among individuals with long COVID, particularly Epstein–Barr virus. Levels of soluble immune mediators and hormones varied among groups, with cortisol levels being lower among participants with long COVID. Integration of immune phenotyping data into unbiased machine learning models identified the key features that are most strongly associated with long COVID status. Collectively, these findings may help to guide future studies into the pathobiology of long COVID and help with developing relevant biomarkers. Nature Publishing Group UK 2023-09-25 2023 /pmc/articles/PMC10620090/ /pubmed/37748514 http://dx.doi.org/10.1038/s41586-023-06651-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/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 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/) . |
spellingShingle | Article Klein, Jon Wood, Jamie Jaycox, Jillian R. Dhodapkar, Rahul M. Lu, Peiwen Gehlhausen, Jeff R. Tabachnikova, Alexandra Greene, Kerrie Tabacof, Laura Malik, Amyn A. Silva Monteiro, Valter Silva, Julio Kamath, Kathy Zhang, Minlu Dhal, Abhilash Ott, Isabel M. Valle, Gabrielee Peña-Hernández, Mario Mao, Tianyang Bhattacharjee, Bornali Takahashi, Takehiro Lucas, Carolina Song, Eric McCarthy, Dayna Breyman, Erica Tosto-Mancuso, Jenna Dai, Yile Perotti, Emily Akduman, Koray Tzeng, Tiffany J. Xu, Lan Geraghty, Anna C. Monje, Michelle Yildirim, Inci Shon, John Medzhitov, Ruslan Lutchmansingh, Denyse Possick, Jennifer D. Kaminski, Naftali Omer, Saad B. Krumholz, Harlan M. Guan, Leying Dela Cruz, Charles S. van Dijk, David Ring, Aaron M. Putrino, David Iwasaki, Akiko Distinguishing features of long COVID identified through immune profiling |
title | Distinguishing features of long COVID identified through immune profiling |
title_full | Distinguishing features of long COVID identified through immune profiling |
title_fullStr | Distinguishing features of long COVID identified through immune profiling |
title_full_unstemmed | Distinguishing features of long COVID identified through immune profiling |
title_short | Distinguishing features of long COVID identified through immune profiling |
title_sort | distinguishing features of long covid identified through immune profiling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10620090/ https://www.ncbi.nlm.nih.gov/pubmed/37748514 http://dx.doi.org/10.1038/s41586-023-06651-y |
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