WGS accurately predicts antimicrobial resistance in<i>Escherichia coli</i>

Tyson, Gregory H.; McDermott, Patrick F.; Cong, Lin; Chen, Yuansha; Tadesse, Daniel A.; Mukherjee, Sampa; Bodeis-Jones, Sonya; Kabera, Claudine; Gaines, Stuart A.; Loneragan, Guy H.; Edrington, Tom S.; Torrence, M. B.; Harhay, Dayna M.; Zhao, Shaohua

doi:10.1093/jac/dkv186

Cited by 227 publications

(184 citation statements)

References 31 publications

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“…The same four genes were detected in chloramphenicol-resistant E. coli strains in a previous study, which also reported floR as the most prevalent gene (33).…”

Section: Ere(b) Erm(b) Mph(a) and Mph(b) In 23 Isolates The Ersupporting

confidence: 77%

Whole-Genome Analysis of Antimicrobial-Resistant and Extraintestinal Pathogenic Escherichia coli in River Water

Gomi

Matsuda

Matsumura

et al. 2017

Appl Environ Microbiol

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Contamination of surface waters by antimicrobial-resistant bacteria and pathogenic bacteria is a great concern. In this study, 531 Escherichia coli isolates obtained from the Yamato River in Japan were evaluated phenotypically for resistance to 25 antimicrobials. Seventy-six isolates (14.3%) were multidrug resistant (MDR), 66 (12.4%) were nonsusceptible to one or two classes of agents, and 389 (73.3%) were susceptible. We performed whole-genome sequencing of selected strains by using Illumina technology. In total, the genome sequences of 155 strains were analyzed for antibiotic resistance determinants and phylogenetic characteristics. More than 50 different resistance determinants, including acquired resistance genes and chromosomal resistance mutations, were detected. Among the sequenced MDR strains (n ϭ 66), sequence type 155 (ST155) complex (n ϭ 9), ST10 complex (n ϭ 9), and ST69 complex (n ϭ 7) were prevalent. Among extraintestinal pathogenic E. coli (ExPEC) strains (n ϭ 58), clinically important clonal groups, namely, ST95 complex (n ϭ 18), ST127 complex (n ϭ 8), ST12 complex (n ϭ 6), ST14 complex (n ϭ 6), and ST131 complex (n ϭ 6), were prevalent, demonstrating the clonal distribution of environmental ExPEC strains. Typing of the fimH (type 1 fimbrial adhesin) gene revealed that ST131 complex strains carried fimH22 or fimH41, and no strains belonging to the fimH30 subgroup were detected. Fine-scale phylogenetic analysis and virulence gene content analysis of strains belonging to the ST95 complex (one of the major clonal ExPEC groups causing community-onset infections) revealed no significant differences between environmental and clinical strains. The results indicate contamination of surface waters by E. coli strains belonging to clinically important clonal groups.IMPORTANCE The prevalence of antimicrobial-resistant and pathogenic E. coli strains in surface waters is a concern because surface waters are used as sources for drinking water, irrigation, and recreational purposes. In this study, MDR and ExPEC strains in river water were characterized by genomic sequencing and analysis. We detected more than 50 resistance determinants and identified clonal groups specific to MDR and ExPEC strains. This study showed contamination of surface waters by E. coli strains belonging to clinically important clonal groups. Overall, this study advances our understanding of environmental MDR and ExPEC strains.KEYWORDS antimicrobial resistance, Escherichia coli, ExPEC, whole-genome sequencing E scherichia coli is a commensal member of the gastrointestinal tract of humans and other warm-blooded animals. Consequently, E. coli, as well as Enterococcus spp., has long been used as an indicator of fecal contamination to assess the microbial quality of surface waters. Although most E. coli strains are harmless, some strains are pathogenic

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“…The same four genes were detected in chloramphenicol-resistant E. coli strains in a previous study, which also reported floR as the most prevalent gene (33).…”

Section: Ere(b) Erm(b) Mph(a) and Mph(b) In 23 Isolates The Ersupporting

confidence: 77%

Whole-Genome Analysis of Antimicrobial-Resistant and Extraintestinal Pathogenic Escherichia coli in River Water

Gomi

Matsuda

Matsumura

et al. 2017

Appl Environ Microbiol

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“…DNA extractions were performed using Qiagen DNeasy blood and tissue kits, with library preparations performed using either Illumina v2 or v3 reagent kits with paired-end reads. Each isolate was subjected to whole-genome sequencing on the MiSeq platform, as described previously, with minimum N50 values of 30 kb and 20-fold coverage required for each assembled genome (9).…”

Section: Methodsmentioning

confidence: 99%

“…For most bacteria, the evidence for using WGS to infer antimicrobial susceptibility data is sparse, although some studies have shown a high correlation between resistance phenotypes and the presence of resistance genes or resistance-associated mutations. This agreement has been best demonstrated with enteric bacteria, including Escherichia coli, Campylobacter spp., and nontyphoidal Salmonella (8)(9)(10)(11)(12). In addition, other DNAbased methods have been used to detect resistance determinants and even guide therapy for infections caused by Mycobacterium tuberculosis, Staphylococcus aureus, HIV, and other pathogens (13)(14)(15).…”

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confidence: 99%

Establishing Genotypic Cutoff Values To Measure Antimicrobial Resistance in Salmonella

Tyson

Zhao

Liu

et al. 2017

Antimicrob Agents Chemother

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Whole-genome sequencing (WGS) has transformed our understanding of antimicrobial resistance, helping us to better identify and track the genetic mechanisms underlying phenotypic resistance. Previous studies have demonstrated high correlations between phenotypic resistance and the presence of known resistance determinants. However, there has never been a large-scale assessment of how well resistance genotypes correspond to specific MICs. We performed antimicrobial susceptibility testing and WGS of 1,738 nontyphoidal Salmonella strains to correlate over 20,000 MICs with resistance determinants. Using these data, we established what we term genotypic cutoff values (GCVs) for 13 antimicrobials against Salmonella. For the drugs we tested, we define a GCV as the highest MIC of isolates in a population devoid of known acquired resistance mechanisms. This definition of GCV is distinct from epidemiological cutoff values (ECVs or ECOFFs), which currently differentiate wild-type from non-wild-type strains based on MIC distributions alone without regard to genetic information. Due to the large number of isolates involved, we observed distinct MIC distributions for isolates with different resistance gene alleles, including for ciprofloxacin and tetracycline, suggesting the potential to predict MICs based on WGS data alone.

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“…Previously reported antibiotic resistance genes were downloaded from GenBank to a local database that contained 2,546 resistance genes from a number of bacterial sources, with gene variants representing resistance to all major antimicrobial classes (12). Additional databases published previously were used as references (13,14).…”

Section: Antimicrobial Susceptibility Testing (Ast)mentioning

confidence: 99%

Whole-Genome Sequencing Analysis Accurately Predicts Antimicrobial Resistance Phenotypes in Campylobacter spp

Zhao

Tyson

Chen

et al. 2016

Appl Environ Microbiol

228

189

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The objectives of this study were to identify antimicrobial resistance genotypes for Campylobacter and to evaluate the correlation between resistance phenotypes and genotypes using in vitro antimicrobial susceptibility testing and whole-genome sequencing (WGS). A total of 114 Campylobacter species isolates (82 C. coli and 32 C. jejuni) obtained from 2000 to 2013 from humans, retail meats, and cecal samples from food production animals in the United States as part of the National Antimicrobial Resistance Monitoring System were selected for study. Resistance phenotypes were determined using broth microdilution of nine antimicrobials. Genomic DNA was sequenced using the Illumina MiSeq platform, and resistance genotypes were identified using assembled WGS sequences through blastx analysis. Eighteen resistance genes, including tet ( There was a high degree of correlation between phenotypic resistance to a given drug and the presence of one or more corresponding resistance genes. Phenotypic and genotypic correlation was 100% for tetracycline, ciprofloxacin/nalidixic acid, and erythromycin, and correlations ranged from 95.4% to 98.7% for gentamicin, azithromycin, clindamycin, and telithromycin. All isolates were susceptible to florfenicol, and no genes associated with florfenicol resistance were detected. There was a strong correlation (99.2%) between resistance genotypes and phenotypes, suggesting that WGS is a reliable indicator of resistance to the nine antimicrobial agents assayed in this study. WGS has the potential to be a powerful tool for antimicrobial resistance surveillance programs. W hole-genome sequencing (WGS) technology has become a fast and affordable tool that is revolutionizing research in the fields of genetics, microbiology, and ecology, as well as public health surveillance and response. Recent studies have shown that WGS analysis can potentially be a single, rapid, and cost-effective approach to define resistance genotypes and predict resistance phenotypes of bacteria with great sensitivity and specificity (1-4).Current laboratory methods, such as broth microdilution, measure antimicrobial susceptibility based on the growth response of bacteria following exposure to the drugs. These methods are advantageous in that they help us understand the concentration of drug necessary to prevent growth of the organism, a measure that is directly related to the clinical success of antimicrobial therapy. However, such phenotypic testing can be costly and timeconsuming, especially for bacteria such Campylobacter that require special growth conditions. Several antimicrobial susceptibility testing methods have been developed for Campylobacter, including agar dilution, disk diffusion, Etest, and broth microdilution (5-7). Each of these methods can test only a limited number of antimicrobials. Reproducibility between laboratories also can be problematic, even when published guidelines are followed, due to variations in medium, incubation conditions, inoculum density, and antimicrobial stability (1,8). Several studi...

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WGS accurately predicts antimicrobial resistance inEscherichia coli

Cited by 227 publications

References 31 publications

Whole-Genome Analysis of Antimicrobial-Resistant and Extraintestinal Pathogenic Escherichia coli in River Water

Whole-Genome Analysis of Antimicrobial-Resistant and Extraintestinal Pathogenic Escherichia coli in River Water

Establishing Genotypic Cutoff Values To Measure Antimicrobial Resistance in Salmonella

Whole-Genome Sequencing Analysis Accurately Predicts Antimicrobial Resistance Phenotypes in Campylobacter spp

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