Nasopharyngeal fungal subtypes of infant bronchiolitis and disease severity risk.


Journal

EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039

Informations de publication

Date de publication:
Sep 2023
Historique:
received: 25 04 2023
revised: 18 07 2023
accepted: 19 07 2023
medline: 18 9 2023
pubmed: 4 8 2023
entrez: 3 8 2023
Statut: ppublish

Résumé

Bronchiolitis is a leading cause of infant hospitalization. Recent research suggests the heterogeneity within bronchiolitis and the relationship of airway viruses and bacteria with bronchiolitis severity. However, little is known about the pathobiological role of fungi. We aimed to identify bronchiolitis mycotypes by integrating fungus and virus data, and determine their association with bronchiolitis severity and biological characteristics. In a multicentre prospective cohort study of 398 infants (age <1 year, male 59%) hospitalized for bronchiolitis, we applied clustering approaches to identify mycotypes by integrating nasopharyngeal fungus (detected in RNA-sequencing data) and virus data (respiratory syncytial virus [RSV], rhinovirus [RV]) at hospitalization. We examined their association with bronchiolitis severity-defined by positive pressure ventilation (PPV) use and biological characteristics by nasopharyngeal metatranscriptome and transcriptome data. In infants hospitalized for bronchiolitis, we identified four mycotypes: A) fungi In this multicentre cohort, fungus-virus clustering identified distinct mycotypes of infant bronchiolitis with differential severity risks and unique biological characteristics. This study was supported by the National Institutes of Health.

Sections du résumé

BACKGROUND BACKGROUND
Bronchiolitis is a leading cause of infant hospitalization. Recent research suggests the heterogeneity within bronchiolitis and the relationship of airway viruses and bacteria with bronchiolitis severity. However, little is known about the pathobiological role of fungi. We aimed to identify bronchiolitis mycotypes by integrating fungus and virus data, and determine their association with bronchiolitis severity and biological characteristics.
METHODS METHODS
In a multicentre prospective cohort study of 398 infants (age <1 year, male 59%) hospitalized for bronchiolitis, we applied clustering approaches to identify mycotypes by integrating nasopharyngeal fungus (detected in RNA-sequencing data) and virus data (respiratory syncytial virus [RSV], rhinovirus [RV]) at hospitalization. We examined their association with bronchiolitis severity-defined by positive pressure ventilation (PPV) use and biological characteristics by nasopharyngeal metatranscriptome and transcriptome data.
RESULTS RESULTS
In infants hospitalized for bronchiolitis, we identified four mycotypes: A) fungi
INTERPRETATION CONCLUSIONS
In this multicentre cohort, fungus-virus clustering identified distinct mycotypes of infant bronchiolitis with differential severity risks and unique biological characteristics.
FUNDING BACKGROUND
This study was supported by the National Institutes of Health.

Identifiants

pubmed: 37536062
pii: S2352-3964(23)00307-9
doi: 10.1016/j.ebiom.2023.104742
pmc: PMC10415709
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

104742

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI127507
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI134940
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI137091
Pays : United States
Organisme : NIAID NIH HHS
ID : K01 AI153558
Pays : United States
Organisme : NIH HHS
ID : UG3 OD023253
Pays : United States

Informations de copyright

Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of interests All authors have no conflicts of interest relevant to this article to disclose.

Auteurs

Ryohei Shibata (R)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. Electronic address: RSHIBATA@mgh.harvard.edu.

Zhaozhong Zhu (Z)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Michihito Kyo (M)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Tadao Ooka (T)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Department of Health Science, University of Yamanashi, Chuo, Yamanashi, Japan.

Robert J Freishtat (RJ)

Center for Genetic Medicine Research, Children's National Research Institute, Washington, DC, USA; Division of Emergency Medicine, Children's National Hospital, Washington, DC, USA; Department of Pediatrics, The George Washington University School of Medicine and Health Sciences, Washington, DC, USA.

Jonathan M Mansbach (JM)

Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.

Marcos Pérez-Losada (M)

Computational Biology Institute, Department of Biostatistics and Bioinformatics, The George Washington University, Washington, DC, USA.

Carlos A Camargo (CA)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Kohei Hasegawa (K)

Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

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Classifications MeSH