Biomarkers and Immune Repertoire Metrics Identified by Peripheral Blood Transcriptomic Sequencing Reveal the Pathogenesis of COVID-19.
Adult
Aged
Antibodies, Viral
/ blood
B-Lymphocytes
/ immunology
Biomarkers
/ blood
COVID-19
/ blood
China
Cohort Studies
Female
Humans
Leukocytes, Mononuclear
/ chemistry
Machine Learning
Male
Middle Aged
SARS-CoV-2
/ physiology
Sequence Analysis, RNA
T-Lymphocytes
/ immunology
Transcriptome
Ubiquitin Thiolesterase
/ genetics
IgG
SARS-CoV-2
biomarker
machine learning
transcriptomic characteristics
Journal
Frontiers in immunology
ISSN: 1664-3224
Titre abrégé: Front Immunol
Pays: Switzerland
ID NLM: 101560960
Informations de publication
Date de publication:
2021
2021
Historique:
received:
07
03
2021
accepted:
30
07
2021
entrez:
10
9
2021
pubmed:
11
9
2021
medline:
18
9
2021
Statut:
epublish
Résumé
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global crisis; however, our current understanding of the host immune response to SARS-CoV-2 infection remains limited. Herein, we performed RNA sequencing using peripheral blood from acute and convalescent patients and interrogated the dynamic changes of adaptive immune response to SARS-CoV-2 infection over time. Our results revealed numerous alterations in these cohorts in terms of gene expression profiles and the features of immune repertoire. Moreover, a machine learning method was developed and resulted in the identification of five independent biomarkers and a collection of biomarkers that could accurately differentiate and predict the development of COVID-19. Interestingly, the increased expression of one of these biomarkers,
Identifiants
pubmed: 34504487
doi: 10.3389/fimmu.2021.677025
pmc: PMC8421539
doi:
Substances chimiques
Antibodies, Viral
0
Biomarkers
0
UCHL1 protein, human
0
Ubiquitin Thiolesterase
EC 3.4.19.12
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
677025Informations de copyright
Copyright © 2021 Liu, Wu, Liu, Zhang, San, Chen, Zhou, Yu, Zeng, Zhou, Zhou, Yang, Yin and Huang.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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