Titel
Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples
Peter Májek
Ares Genetics GmbH
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Abstract
Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.
Stichwort
antimicrobial resistanceantimicrobial susceptibility testingmachine learningbioinformaticsNGSclinical metagenomics
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
Erschienen in
Titel
Antibiotics
Band
12
Ausgabe
2
ISSN
2079-6382
Erscheinungsdatum
2023
Publication
MDPI AG
Erscheinungsdatum
2023
Zugänglichkeit
Rechte
Rechteangabe
© 2023 by the authors
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