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Review
. 2019 Jun;20(6):356-370.
doi: 10.1038/s41576-019-0108-4.

Sequencing-based Methods and Resources to Study Antimicrobial Resistance

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Free PMC article
Review

Sequencing-based Methods and Resources to Study Antimicrobial Resistance

Manish Boolchandani et al. Nat Rev Genet. .
Free PMC article

Abstract

Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.

Conflict of interest statement

Competing interests

The authors declare no competing interests.

Figures

Fig. 1 |
Fig. 1 |. Antimicrobial targets and resistance mechanisms.
a | Antimicrobials are grouped by target site. Drug classes are in bold, and example drugs from that class are in parentheses below the drug classes. Antimicrobial resistance mechanisms that act on that class are depicted left of the antimicrobial class according to the layout shown in part b. b | Mechanisms of antimicrobial resistance are depicted with susceptible organisms represented on the left and resistant organisms represented on the right. To the left of each labelled mechanism is the legend annotation position used in part a. c | Genetic underpinnings of antimicrobial resistance are illustrated.
Fig. 2 |
Fig. 2 |. Assembly versus read mapping.
a | The process for sequencing data generation for metagenomic and genomic samples. b | Steps for the read-based and the assembly-based methods of in silico resistance gene identification are contrasted. c | Examples of analysis that can be conducted on samples after resistance gene identification. CARD, Comprehensive Antibiotic Resistance Database.
Fig. 3 |
Fig. 3 |. Functional metagenomics to interrogate acquired resistance genes in different environments and human pathogens.
A summary of experimental and computational steps involved in functional metagenomics. First, sample collection and extraction occur. Metagenomic DNA is isolated from the sample (for example, soil or faeces). Second, functional selection using an expression vector and the host system is performed. The metagenomic DNA is sheared to a target size of 2–5 kb, and the fragments are then cloned into an expression vector and transformed into a host system (for example, Escherichia coli). The transformants are then selected using antimicrobials at concentrations that are inhibitory to the wild-type host system. Third, barcoded sequencing of pooled DNA fragments is performed. The resistance-conferring fragments are PCR amplified, barcoded and pooled together for sequencing. The sequencing reads are computationally demultiplexed using barcode assembly and quality trimmed to obtain high-quality clean reads. Fourth, iterative assembly of sequencing reads by sample is performed. The clean reads are assembled with computational pipeline Parallel Annotation and Reassembly of Functional Metagenomic Selections (PARFuMS), in which ensemble-based assembly is performed using multiple rounds of a short-read assembler (Velvet), and intermediate contigs are then used in a long-read assembler (Phrap) to give full-length contigs. Finally, resistance gene annotation of assembled reads is performed. The annotation of contigs is accomplished using BLAST-based and hidden Markov model (HMM)-based databases.

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