Detection of hidden antibiotic resistance through real-time genomics

Ela Sauerborn, Nancy Carolina Corredor, Tim Reska, Albert Perlas, Samir Vargas da Fonseca Atum, Nick Goldman, Nina Wantia, Clarissa Prazeres da Costa, Ebenezer Foster-Nyarko and Lara Urban ()
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Ela Sauerborn: Helmholtz Zentrum Muenchen
Nancy Carolina Corredor: Technical University of Munich
Tim Reska: Helmholtz Zentrum Muenchen
Albert Perlas: Helmholtz Zentrum Muenchen
Samir Vargas da Fonseca Atum: Helmholtz Zentrum Muenchen
Nick Goldman: Wellcome Genome Campus
Nina Wantia: Technical University of Munich
Clarissa Prazeres da Costa: Technical University of Munich
Ebenezer Foster-Nyarko: London School of Hygiene & Tropical Medicine
Lara Urban: Helmholtz Zentrum Muenchen

Nature Communications, 2024, vol. 15, issue 1, 1-8

Abstract: Abstract 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.

Date: 2024
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DOI: 10.1038/s41467-024-49851-4

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