Browsing publications of the research group of microbial natural substances([HIPS]MINS) by Authors
Clinical Resistome Screening of 1,110 Escherichia coli Isolates Efficiently Recovers Diagnostically Relevant Antibiotic Resistance Biomarkers and Potential Novel Resistance Mechanisms.Volz, Carsten; Ramoni, Jonas; Beisken, Stephan; Galata, Valentina; Keller, Andreas; Plum, Achim; Posch, Andreas E; Müller, Rolf; HIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany. (Frontiers, 2019-01-01)Multidrug-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.
Integrating Culture-based Antibiotic Resistance Profiles with Whole-genome Sequencing Data for 11,087 Clinical Isolates.Galata, Valentina; Laczny, Cédric C; Backes, Christina; Hemmrich-Stanisak, Georg; Schmolke, Susanne; Franke, Andre; Meese, Eckart; Herrmann, Mathias; von Müller, Lutz; Plum, Achim; et al. (Elsevier, 2019-05-14)Emergingantibiotic resistanceis a major global health threat. The analysis of nucleic acidsequences linked to susceptibility phenotypes facilitates the study of genetic antibiotic resistancedeterminants to inform molecular diagnostics and drug development. We collected genetic data(11,087 newly-sequenced whole genomes) and culture-based resistance profiles (10,991 out of the11,087 isolates comprehensively tested against 22 antibiotics in total) of clinical isolates including18 main species spanning a time period of 30 years. Species and drug specific resistance patternswere observed including increased resistance rates forAcinetobacter baumanniito carbapenemsand forEscherichia colito fluoroquinolones. Species-levelpan-genomeswere constructed to reflectthe genetic repertoire of the respective species, including conserved essential genes and known resis-tance factors. Integrating phenotypes and genotypes through species-level pan-genomes allowed toinfer gene–drug resistance associations using statistical testing. The isolate collection and the analysis results have been integrated into GEAR-base, a resource available for academic research use free of charge athttps://gear-base.com