A methylation clock model of mild SARS-CoV-2 infection provides insight into immune dysregulation.
DNA methylation
SARS-CoV-2
machine learning model
temporal dynamics
trained immunity
Journal
Molecular systems biology
ISSN: 1744-4292
Titre abrégé: Mol Syst Biol
Pays: England
ID NLM: 101235389
Informations de publication
Date de publication:
09 05 2023
09 05 2023
Historique:
revised:
16
02
2023
received:
23
09
2022
accepted:
20
02
2023
medline:
10
5
2023
pubmed:
16
3
2023
entrez:
15
3
2023
Statut:
ppublish
Résumé
DNA methylation comprises a cumulative record of lifetime exposures superimposed on genetically determined markers. Little is known about methylation dynamics in humans following an acute perturbation, such as infection. We characterized the temporal trajectory of blood epigenetic remodeling in 133 participants in a prospective study of young adults before, during, and after asymptomatic and mildly symptomatic SARS-CoV-2 infection. The differential methylation caused by asymptomatic or mildly symptomatic infections was indistinguishable. While differential gene expression largely returned to baseline levels after the virus became undetectable, some differentially methylated sites persisted for months of follow-up, with a pattern resembling autoimmune or inflammatory disease. We leveraged these responses to construct methylation-based machine learning models that distinguished samples from pre-, during-, and postinfection time periods, and quantitatively predicted the time since infection. The clinical trajectory in the young adults and in a diverse cohort with more severe outcomes was predicted by the similarity of methylation before or early after SARS-CoV-2 infection to the model-defined postinfection state. Unlike the phenomenon of trained immunity, the postacute SARS-CoV-2 epigenetic landscape we identify is antiprotective.
Identifiants
pubmed: 36919946
doi: 10.15252/msb.202211361
pmc: PMC10167476
doi:
Banques de données
GEO
['GSE198449', 'GSE219037']
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
e11361Subventions
Organisme : NCI NIH HHS
ID : U24 CA224319
Pays : United States
Organisme : NCI NIH HHS
ID : R33 CA263705
Pays : United States
Organisme : NIH HHS
ID : S10 OD028483
Pays : United States
Organisme : NCI NIH HHS
ID : 75N91020C00055
Pays : United States
Organisme : NHGRI NIH HHS
ID : R01 HG009299
Pays : United States
Organisme : NCI NIH HHS
ID : P30 CA196521
Pays : United States
Investigateurs
Vanessa Barcessat
(V)
Kevin Tuballes
(K)
Diane Marie Del Valle
(DM)
Kai Nie
(K)
Hui Xie
(H)
Grace Chung
(G)
Manishkumar Patel
(M)
Jocelyn Harris
(J)
Kimberly Argueta
(K)
Jacques Fehr
(J)
Barr Gruberg
(B)
Nicholas Zaki
(N)
Informations de copyright
© 2023 The Authors. Published under the terms of the CC BY 4.0 license.
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