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dc.contributor.authorVolz, Carsten
dc.contributor.authorRamoni, Jonas
dc.contributor.authorBeisken, Stephan
dc.contributor.authorGalata, Valentina
dc.contributor.authorKeller, Andreas
dc.contributor.authorPlum, Achim
dc.contributor.authorPosch, Andreas E
dc.contributor.authorMüller, Rolf
dc.date.accessioned2019-09-11T09:51:08Z
dc.date.available2019-09-11T09:51:08Z
dc.date.issued2019-01-01
dc.identifier.citationFront Microbiol. 2019 Aug 13;10:1671. doi: 10.3389/fmicb.2019.01671. eCollection 2019.en_US
dc.identifier.issn1664-302X
dc.identifier.pmid31456751
dc.identifier.doi10.3389/fmicb.2019.01671
dc.identifier.urihttp://hdl.handle.net/10033/621932
dc.description.abstractMultidrug-resistant pathogens represent one of the biggest global healthcare challenges. Molecular diagnostics can guide effective antibiotics therapy but relies on validated, predictive biomarkers. Here we present a novel, universally applicable workflow for rapid identification of antimicrobial resistance (AMR) biomarkers from clinical Escherichia coli isolates and quantitatively evaluate the potential to recover causal biomarkers for observed resistance phenotypes. For this, a metagenomic plasmid library from 1,110 clinical E. coli isolates was created and used for high-throughput screening to identify biomarker candidates against Tobramycin (TOB), Ciprofloxacin (CIP), and Trimethoprim-Sulfamethoxazole (TMP-SMX). Identified candidates were further validated in vitro and also evaluated in silico for their diagnostic performance based on matched genotype-phenotype data. AMR biomarkers recovered by the metagenomics screening approach mechanistically explained 77% of observed resistance phenotypes for Tobramycin, 76% for Trimethoprim-Sulfamethoxazole, and 20% Ciprofloxacin. Sensitivity for Ciprofloxacin resistance detection could be improved to 97% by complementing results with AMR biomarkers that are undiscoverable due to intrinsic limitations of the workflow. Additionally, when combined in a multiplex diagnostic in silico panel, the identified AMR biomarkers reached promising positive and negative predictive values of up to 97 and 99%, respectively. Finally, we demonstrate that the developed workflow can be used to identify potential novel resistance mechanisms.en_US
dc.language.isoenen_US
dc.publisherFrontiersen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectantibiotic resistanceen_US
dc.subjectbioinformaticsen_US
dc.subjectbiomarkersen_US
dc.subjectbiostatisticsen_US
dc.subjectfunctional metagenomicsen_US
dc.subjecthigh-throughput screeningen_US
dc.subjectnext-generation sequencingen_US
dc.titleClinical Resistome Screening of 1,110 Escherichia coli Isolates Efficiently Recovers Diagnostically Relevant Antibiotic Resistance Biomarkers and Potential Novel Resistance Mechanisms.en_US
dc.typeArticleen_US
dc.contributor.departmentHIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany.en_US
dc.identifier.journalFrontiers in Microbiologyen_US
refterms.dateFOA2019-09-11T09:51:08Z
dc.source.journaltitleFrontiers in microbiology


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