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Using Genomics to Track Global Antimicrobial Resistance Downloaded from orbit.dtu.dk on: Oct 02, 2021 Using Genomics to Track Global Antimicrobial Resistance Hendriksen, Rene S.; Bortolaia, Valeria; Tate, Heather; Tyson, Gregory H; Aarestrup, Frank Møller; McDermott, Patrick F. Published in: Frontiers in Public Health Link to article, DOI: 10.3389/fpubh.2019.00242 Publication date: 2019 Document Version Publisher's PDF, also known as Version of record Link back to DTU Orbit Citation (APA): Hendriksen, R. S., Bortolaia, V., Tate, H., Tyson, G. H., Aarestrup, F. M., & McDermott, P. F. (2019). Using Genomics to Track Global Antimicrobial Resistance. Frontiers in Public Health, 7, [242]. https://doi.org/10.3389/fpubh.2019.00242 General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research. You may not further distribute the material or use it for any profit-making activity or commercial gain You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. REVIEW published: 04 September 2019 doi: 10.3389/fpubh.2019.00242 Using Genomics to Track Global Antimicrobial Resistance Rene S. Hendriksen 1*, Valeria Bortolaia 1, Heather Tate 2, Gregory H. Tyson 2, Frank M. Aarestrup 1 and Patrick F. McDermott 2 1 European Union Reference Laboratory for Antimicrobial Resistance, World Health Organisation, Collaborating Center for Antimicrobial Resistance and Genomics in Food borne Pathogens, FAO Reference Laboratory for Antimicrobial Resistance, National Food Institute, Technical University of Denmark, Lyngby, Denmark, 2 Center for Veterinary Medicine, Office of Research, United States Food and Drug Administration, Laurel, MD, United States 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 Edited by: conducted a scientific literature review and here we present a description of examples of Marc Jean Struelens, European Centre for Disease available tools and databases for antimicrobial resistance (AMR) detection and provide Prevention and Control future perspectives and recommendations. At least 47 freely accessible bioinformatics (ECDC), Sweden resources for detection of AMR determinants in DNA or amino acid sequence data have Reviewed by: Sergey Eremin, been developed to date. These include, among others but not limited to, ARG-ANNOT, World Health Organization CARD, SRST2, MEGARes, Genefinder, ARIBA, KmerResistance, AMRFinder, and (Switzerland), Switzerland ResFinder. Bioinformatics resources differ for several parameters including type of Ana Afonso, University of São Paulo, Brazil accepted input data, presence/absence of software for search within a database of *Correspondence: AMR determinants that can be specific to a tool or cloned from other resources, and Rene S. Hendriksen for the search approach employed, which can be based on mapping or on alignment. [email protected] As a consequence, each tool has strengths and limitations in sensitivity and specificity Specialty section: of detection of AMR determinants and in application, which for some of the tools have This article was submitted to been highlighted in benchmarking exercises and scientific articles. The identified tools Infectious Diseases - Surveillance, are either available at public genome data centers, from GitHub or can be run locally. Prevention and Treatment, a section of the journal NCBI and European Nucleotide Archive (ENA) provide possibilities for online submission Frontiers in Public Health of both sequencing and accompanying phenotypic antimicrobial susceptibility data, Received: 18 June 2019 allowing for other researchers to further analyze data, and develop and test new tools. Accepted: 13 August 2019 Published: 04 September 2019 The advancement in whole genome sequencing and the application of online tools for Citation: real-time detection of AMR determinants are essential to identify control and prevention Hendriksen RS, Bortolaia V, Tate H, strategies to combat the increasing threat of AMR. Accessible tools and DNA sequence Tyson GH, Aarestrup FM and data are expanding, which will allow establishing global pathogen surveillance and AMR McDermott PF (2019) Using Genomics to Track Global tracking based on genomics. There is however, a need for standardization of pipelines Antimicrobial Resistance. and databases as well as phenotypic predictions based on the data. Front. Public Health 7:242. doi: 10.3389/fpubh.2019.00242 Keywords: global, antimicrobial resistance, surveillance, genomic, bioinformatics tools, microbiology Frontiers in Public Health | www.frontiersin.org 1 September 2019 | Volume 7 | Article 242 Hendriksen et al. Bioinformatics Resources for Prediction of AMR INTRODUCTION in an antimicrobial susceptible phenotype (true negative rate), have been observed depending on the bacterial species analyzed The science of infectious disease, along with other medical (Table 1) (7–18). Furthermore, a growing body of data shows and biological specialties, is undergoing rapid change brought that it is possible to predict AMR, and perhaps the MIC of an on by the advent of affordable whole genomic sequencing antimicrobial, applying machine or deep learning to genome (WGS) technologies (1–3). These technologies are rapidly sequence data (19–21). The comparison between phenotype and gaining acceptance as routine methods, and in the process, are genotype as well as the application of machine or deep learning transforming laboratory procedures. are however still in their infancy and additional data on bacterial The amount of bacterial genomic data being generated species beyond the foodborne pathogen domain are needed. is immense. As of this writing, for example, over 190,000 The most obvious advantage of WGS for microbial typing Salmonella genomes alone are in the public domain with and AMR surveillance is the unprecedented level of detail hundreds being added weekly. A complete genomic DNA in one assay that can be used to describe current trends sequence represents the highest practicable level of structural and distinguish emerging tendencies (22). AMR bacteria can detail on the individuating traits of an organism or population. be typed and traced by specific allele profiles, rather than As such, it can be used to provide more reliable microbial just according to phenotypic patterns by drug class. This is identification, definitive phylogenetic relationships, and a exemplified by a study of emerging aminoglycoside-resistant comprehensive catalog of traits relevant for epidemiological Campylobacter in the USA, where WGS revealed that the rising investigations. This is having a major impact on outbreak trend was driven by nine different resistance alleles, six of investigations and the diagnosis and treatment of infectious which had never been detected in Campylobacter previously diseases, as well as the practice of microbiology and epidemiology and would not have been found easily using PCR (10). (4). Furthermore, DNA sequences are a universal dataset from Similarly, in one of the first large-scale applications of WGS which, theoretically, any biological feature can be inferred. to investigate a drug-resistant foodborne outbreak in the US in In clinical applications, this includes the ability to detect 2011, inconsistent resistance patterns among indistinguishable antimicrobial resistance (AMR), and to track the evolution and PFGE types of Salmonella serovar Heidelberg were revealed by spread of AMR bacteria in a hospital or the community. sequence analysis to be a polymicrobic contamination event, AMR is a global health problem that contributes to tens involving various combinations of plasmids and strain types (23). of thousands of deaths per year [Chaired by Jim O’Neill, DNA sequence-based surveillance makes it possible also to (5)]. Historically, AMR has been detected as a measurement define multidrug-resistance (MDR) with much greater precision of the growth inhibitory effects of a chemotherapeutic agent compared to phenotypic tests (22). It has long been a common on a bacterial population cultured under specific laboratory practice to define MDR as resistance to compounds from three or conditions. Despite some ancillary enhancements, clinical more drug classes (24), a definition with limited practical value. laboratories to this day rely mainly on diffusion and dilution Bioinformatic analysis can reveal the co-carriage of specific genes methods to guide clinical therapy and to monitor AMR over underlying different MDR patterns, allelic trends over time, their time. Accumulating data show that AMR can be accurately genetic context including the potential for horizontal transfer, predicted from the genomic sequence for many bacteria. The and their distribution by source. In addition, the presence of sequence-based approach to AMR detection requires robust co-resistances not assayed on standard drug panels is revealed, bioinformatics tools to analyze and visualize the genomic such as disinfectant
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