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