The gut microbiome, resistome, and mycobiome in preterm newborn infants and mouse pups: lack of lasting effects by antimicrobial therapy or probiotic prophylaxis.

Antibiotics Infloran Mice model Microbiome Mycobiome Preterm infants Resistome

Journal

Gut pathogens
ISSN: 1757-4749
Titre abrégé: Gut Pathog
Pays: England
ID NLM: 101474263

Informations de publication

Date de publication:
12 May 2024
Historique:
received: 13 09 2023
accepted: 13 04 2024
medline: 13 5 2024
pubmed: 13 5 2024
entrez: 12 5 2024
Statut: epublish

Résumé

Enhancing our understanding of the underlying influences of medical interventions on the microbiome, resistome and mycobiome of preterm born infants holds significant potential for advancing infection prevention and treatment strategies. We conducted a prospective quasi-intervention study to better understand how antibiotics, and probiotics, and other medical factors influence the gut development of preterm infants. A controlled neonatal mice model was conducted in parallel, designed to closely reflect and predict exposures. Preterm infants and neonatal mice were stratified into four groups: antibiotics only, probiotics only, antibiotics followed by probiotics, and none of these interventions. Stool samples from both preterm infants and neonatal mice were collected at varying time points and analyzed by 16 S rRNA amplicon sequencing, ITS amplicon sequencing and whole genome shotgun sequencing. The human infant microbiomes showed an unexpectedly high degree of heterogeneity. Little impact from medical exposure (antibiotics/probiotics) was observed on the strain patterns, however, Bifidobacterium bifidum was found more abundant after exposure to probiotics, regardless of prior antibiotic administration. Twenty-seven antibiotic resistant genes were identified in the resistome. High intra-variability was evident within the different treatment groups. Lastly, we found significant effects of antibiotics and probiotics on the mycobiome but not on the microbiome and resistome of preterm infants. Although our analyses showed transient effects, these results provide positive motivation to continue the research on the effects of medical interventions on the microbiome, resistome and mycobiome of preterm infants.

Sections du résumé

BACKGROUND BACKGROUND
Enhancing our understanding of the underlying influences of medical interventions on the microbiome, resistome and mycobiome of preterm born infants holds significant potential for advancing infection prevention and treatment strategies. We conducted a prospective quasi-intervention study to better understand how antibiotics, and probiotics, and other medical factors influence the gut development of preterm infants. A controlled neonatal mice model was conducted in parallel, designed to closely reflect and predict exposures. Preterm infants and neonatal mice were stratified into four groups: antibiotics only, probiotics only, antibiotics followed by probiotics, and none of these interventions. Stool samples from both preterm infants and neonatal mice were collected at varying time points and analyzed by 16 S rRNA amplicon sequencing, ITS amplicon sequencing and whole genome shotgun sequencing.
RESULTS RESULTS
The human infant microbiomes showed an unexpectedly high degree of heterogeneity. Little impact from medical exposure (antibiotics/probiotics) was observed on the strain patterns, however, Bifidobacterium bifidum was found more abundant after exposure to probiotics, regardless of prior antibiotic administration. Twenty-seven antibiotic resistant genes were identified in the resistome. High intra-variability was evident within the different treatment groups. Lastly, we found significant effects of antibiotics and probiotics on the mycobiome but not on the microbiome and resistome of preterm infants.
CONCLUSIONS CONCLUSIONS
Although our analyses showed transient effects, these results provide positive motivation to continue the research on the effects of medical interventions on the microbiome, resistome and mycobiome of preterm infants.

Identifiants

pubmed: 38735967
doi: 10.1186/s13099-024-00616-w
pii: 10.1186/s13099-024-00616-w
doi:

Types de publication

Journal Article

Langues

eng

Pagination

27

Subventions

Organisme : HORIZON EUROPE Marie Sklodowska-Curie Actions
ID : H2020-MSCA-ITN-2018
Organisme : Innovative Training Networks
ID : 813781 "BestTreat"
Organisme : Bundesministerium für Bildung und Forschung
ID : Project NeoBIOM - 03ZZ0829A

Informations de copyright

© 2024. The Author(s).

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Auteurs

Elizabeth Y Yuu (EY)

Data Analytics & Computational Statistics, Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 , Potsdam, Germany.

Christoph Bührer (C)

Charité - Universitätsmedizin, Berlin, Germany.

Tim Eckmanns (T)

Robert Koch Institute, Berlin, Germany.

Marcus Fulde (M)

Department of Mathematics and Computer Science, Freie Universität Berlin, 14195, Berlin, Germany.

Michaela Herz (M)

Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany.

Oliver Kurzai (O)

Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany.
Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, 07745 , Jena, Germany.

Christin Lindstedt (C)

Charité - Universitätsmedizin, Berlin, Germany.

Gianni Panagiotou (G)

Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, 07745 , Jena, Germany.
Faculty of Biological Sciences, Friedrich Schiller University, 07745, Jena, Germany.
Department of Medicine, The University of Hong Kong, Hong Kong, China.

Vitor C Piro (VC)

Data Analytics & Computational Statistics, Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 , Potsdam, Germany.
Department of Mathematics and Computer Science, Freie Universität Berlin, 14195, Berlin, Germany.

Aleksandar Radonic (A)

Robert Koch Institute, Berlin, Germany.

Bernhard Y Renard (BY)

Data Analytics & Computational Statistics, Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 , Potsdam, Germany.

Annicka Reuss (A)

Robert Koch Institute, Berlin, Germany.
Ministry of Justice and Health, Schleswig-Holstein, Kiel , Germany.

Sara Leal Siliceo (SL)

Department of Microbiome Dynamics, Leibniz Institute for Natural Product Research and Infection Biology - Hans Knöll Institute, Beutenbergstraße 11A, 07745 , Jena, Germany.

Nadja Thielemann (N)

Institute for Hygiene and Microbiology, University of Würzburg, Würzburg, Germany.

Andrea Thürmer (A)

Robert Koch Institute, Berlin, Germany.

Kira van Vorst (KV)

Department of Mathematics and Computer Science, Freie Universität Berlin, 14195, Berlin, Germany.

Lothar H Wieler (LH)

Data Analytics & Computational Statistics, Hasso Plattner Institute, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, 14482 , Potsdam, Germany.
Robert Koch Institute, Berlin, Germany.

Sebastian Haller (S)

Robert Koch Institute, Berlin, Germany. HallerS@rki.de.

Classifications MeSH