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Sauerborn, E. ; Corredor, N.C.* ; Reska, T.T.M. ; Perlas Puente,A. ; Vargas da Fonseca Atum, S. ; Goldman, N.* ; Wantia, N.* ; Prazeres da Costa, C.U.* ; Foster-Nyarko, E.* ; Urban, L.

Detection of hidden antibiotic resistance through real-time genomics.

Nat. Commun. 15:5494 (2024)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Real-time genomics through nanopore sequencing holds the promise of fast antibiotic resistance prediction directly in the clinical setting. However, concerns about the accuracy of genomics-based resistance predictions persist, particularly when compared to traditional, clinically established diagnostic methods. Here, we leverage the case of a multi-drug resistant Klebsiella pneumoniae infection to demonstrate how real-time genomics can enhance the accuracy of antibiotic resistance profiling in complex infection scenarios. Our results show that unlike established diagnostics, nanopore sequencing data analysis can accurately detect low-abundance plasmid-mediated resistance, which often remains undetected by conventional methods. This capability has direct implications for clinical practice, where such "hidden" resistance profiles can critically influence treatment decisions. Consequently, the rapid, in situ application of real-time genomics holds significant promise for improving clinical decision-making and patient outcomes.
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Publication type Article: Journal article
Document type Scientific Article
Language english
Publication Year 2024
HGF-reported in Year 2024
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Journal Nature Communications
Quellenangaben Volume: 15, Issue: 1, Pages: , Article Number: 5494 Supplement: ,
Publisher Nature Publishing Group
Publishing Place London
Reviewing status Peer reviewed
Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Helmholtz Pioneer Campus (HPC)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
Pioneer Campus
PSP Element(s) G-530013-001
G-510011-001
Grants Projekt DEAL
Bavarian Ministry of Commerce
C.P.dC.'s DZIF grant
Helmholtz Principal Investigator Grant
DOI 10.1038/s41467-024-49851-4
WOS ID 001259174500006
Scopus ID 85197121231
PubMed ID 38944650
Erfassungsdatum 2024-07-08
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