Using Genomics to Track Global Antimicrobial Resistance

Abstract

The recent advancements in rapid and affordable DNA sequencing technologies have revolutionized diagnostic microbiology and microbial surveillance. The availability of bioinformatics tools and online accessible databases has been a prerequisite for this. We conducted a scientific literature review and here we present a description of examples of available tools and databases for antimicrobial resistance (AMR) detection and provide future perspectives and recommendations. At least 47 freely accessible bioinformatics resources for detection of AMR determinants in DNA or amino acid sequence data have been developed to date. These include, among others but not limited to, ARG-ANNOT, CARD, SRST2, MEGARes, Genefinder, ARIBA, KmerResistance, AMRFinder, and ResFinder. Bioinformatics resources differ for several parameters including type of accepted input data, presence/absence of software for search within a database of AMR determinants that can be specific to a tool or cloned from other resources, and for the search approach employed, which can be based on mapping or on alignment. As a consequence, each tool has strengths and limitations in sensitivity and specificity of detection of AMR determinants and in application, which for some of the tools have been highlighted in benchmarking exercises and scientific articles. The identified tools are either available at public genome data centers, from GitHub or can be run locally. NCBI and European Nucleotide Archive (ENA) provide possibilities for online submission of both sequencing and accompanying phenotypic antimicrobial susceptibility data, allowing for other researchers to further analyze data, and develop and test new tools. The advancement in whole genome sequencing and the application of online tools for real-time detection of AMR determinants are essential to identify control and prevention strategies to combat the increasing threat of AMR. Accessible tools and DNA sequence data are expanding, which will allow establishing global pathogen surveillance and AMR tracking based on genomics. There is however, a need for standardization of pipelines and databases as well as phenotypic predictions based on the data.

Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne microorganisms

“…Especially, short‐read WGS has difficulties for managing direct repeats and plasmid analysis and can be misleading in investigating plasmid‐related AMR genes. Different databases/bioinformatic tools/nomenclature could hamper the comparative accuracy of the results. A comprehensive and curated catalogue of resistance mutations and genes has to be set up and agreed on, as well as the use of a harmonised computational approach to predict AMR from WGS data (Hendriksen et al., 2019a). Also, clear criteria to define a gene as ‘novel’ (i.e.…”

Host genetics affected the resistome and its expression patterns in the rumen of beef cattle raised without antibiotics used in humans

“…Presently, there are at least 47 bioinformatic resources/tools, but no a 'standard' pipeline has been developed speci cally to characterize the resistome. In this regard, the results are heavily dependent on the analysis methods (assembly-based or read-based) or reference database [34]. In this study, the ARG-OAP (v2) pipeline was applied, which uses a custom database with a hybrid UBLAST and BLASTX algorithm, re ecting the critical need for a comprehensive database combined with lower identity matching for antimicrobial resistance gene annotation of metagenomic data [35].…”

A recently isolated human commensal Escherichia coli ST10 clone member mediates enhanced thermotolerance and tetrathionate respiration on a P1 phage‐derived IncY plasmid

“…showed that the pFec10 plasmid does not encode genetic elements that mediate antibiotic resistance (Hendriksen et al, 2019). Plasmid stability is most likely maintained by the type II toxin-anti-toxin system YdeE-YdeD (Prevent host death-death on curing (Phd-Doc)), whereby ydeD encodes a "death on curing" toxin (Lehnherr et al, 1993;Liu et al, 2008).…”