Whole-Genome Sequencing Accurately Identifies Resistance to Extended-Spectrum β-Lactams for Major Gram-Negative Bacterial Pathogens

Shelburne, Samuel A.; Kim, Jiwoong; Munita, José M.; Sahasrabhojane, Pranoti; Shields, Ryan K.; Press, Ellen G.; Li, Xiqi; Arias, César A.; Cantarel, Brandi L.; Jiang, Ying; Kim, Min S.; Aitken, Samuel L; Greenberg, David E.

doi:10.1093/cid/cix417

Cited by 62 publications

(63 citation statements)

References 40 publications

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“…Compared with other studies of BL/BLIs and E. coli causing human infections, less BL/BLI resistance was accounted for by inhibitor resistant beta-lactamases. 18 To identify resistance in our population-representative set of isolates, we found it critical to consider genetic features that alter expression of beta-lactamases. Although the individual effects of some of these features on MICs were small, they were important, because MICs for many isolates were around the breakpoint.…”

Section: Predictions Of Mic In An Independent Validation Setmentioning

confidence: 99%

“…17 Similar studies investigating other BL/BLIs, such as piperacillin-tazobactam, reported poor accuracy when predicting resistance from genotype. 18 We therefore investigated concordance between WGS-derived genotypes and coamoxiclav susceptibility phenotypes in a large, unselected set of Oxfordshire E. coli BSI isolates from 2013-2015. We assessed whether extending the usual presence/absence genetic approach to include features that might increase betalactamase expression (copy number, promoter type) would improve concordance, and quantified the impact of particular genetic variants and testing guidelines (EUCAST, CLSI) on minimum inhibitory concentrations (MICs).…”

Section: Introductionmentioning

confidence: 99%

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Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in Escherichia coli

Davies

Stoesser

Sheppard

et al. 2019

Preprint

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36Resistance to amoxicillin-clavulanate, a widely used beta-lactam/beta-lactamase inhibitor 37 combination antibiotic, is rising globally, yet susceptibility testing remains challenging. To test 38 whether whole-genome sequencing (WGS) could provide a more reliable assessment of 39 susceptibility than traditional methods, we predicted resistance from WGS for 976 E. coli 40 bloodstream infection isolates from Oxfordshire, UK, comparing against phenotypes from the 41 BD Phoenix (calibrated against EUCAST guidelines). 339/976 (35%) isolates were amoxicillin-42 clavulanate resistant. Predictions based solely on beta-lactamase presence/absence performed 43 poorly (sensitivity 23% (78/339)) but improved when genetic features associated with 44 penicillinase hyper-production (e.g. promoter mutations, copy number estimates) were 45 considered (sensitivity 82% (277/339); p<0.0001). Most discrepancies occurred in isolates with 46 peri-breakpoint MICs. We investigated two potential causes; the phenotypic reference and the 47 binary resistant/susceptible classification. We performed reference standard, replicated 48 phenotyping in a random stratified subsample of 261/976 (27%) isolates using agar dilution, 49 following both EUCAST and CLSI guidelines, which use different clavulanate concentrations. 50 As well as disagreeing with each other, neither agar dilution phenotype aligned perfectly with 51 genetic features. A random-effects model investigating associations between genetic features and 52 MICs showed that some genetic features had small, variable and additive effects, resulting in 53 variable resistance classification. Using model fixed-effects to predict MICs for the non-agar 54 dilution isolates, predicted MICs were in essential agreement (±1 doubling dilution) with 55 observed (BD Phoenix) MICs for 691/715 (97%) isolates. This suggests amoxicillin-clavulanate 56 resistance in E. coli is quantitative, rather than qualitative, explaining the poorly reproducible 57 4 binary (resistant/susceptible) phenotypes and suboptimal concordance between different 58 phenotypic methods and with WGS-based predictions. 59 60 61Rising amoxicillin-clavulanate resistance in E. coli is a major healthcare challenge, with 62 increasing incidence of resistant bloodstream infections (BSI)(1) threatening its utility as the 63 most commonly used antibiotic in Europe.(2) Consequently, many hospitals are considering 64 broadening their first-line empiric antibiotics for common infections. However, significant 65 uncertainty is created by observed differences between the two main assays for amoxicillin-66 clavulanate susceptibility in the classification of clinical samples.(3) These differences are so 67 large that increasing amoxicillin-clavulanate resistance was suggested to be primarily due to 68 laboratories switching from US Clinical Laboratory Standards Institute (CLSI) to European 69 Committee on Antimicrobial Susceptibility Testing (EUCAST) guidelines.(4) Recent work,(5) 70 however, suggests that changes in laboratory protocols ar...

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Section: Predictions Of Mic In An Independent Validation Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in Escherichia coli

Davies

Stoesser

Sheppard

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Shelburne et al demonstrated that WGS accurately predicted resistance to extended-spectrum ␤-lactams in major Gram-negative pathogens, suggesting that it may be feasible to use WGS to identify resistance phenotypes in clinical settings. 169 Still, resistanceprediction methods are limited by their ability to identify known markers, and existence of unknown resistance markers is a major concern for patients, as false susceptibility identification poses a real threat to patient outcomes. If clinical workflows are to move toward phenotype-independent susceptibility prediction, more effective approaches for prediction of unknown resistance genotypes that are scalable for real-time diagnostic workflows are needed.…”

Section: Clinical Diagnosticsmentioning

confidence: 99%

Genomic epidemiology of multidrug‐resistant Gram‐negative organisms

Hawken

Snitkin

2018

Annals of the New York Academy of Sciences

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The emergence and spread of antibiotic-resistant Gram-negative bacteria (rGNB) across global healthcare networks presents a significant threat to public health. As the number of effective antibiotics available to treat these resistant organisms dwindles, it is essential that we devise more effective strategies for controlling their proliferation. Recently, whole-genome sequencing has emerged as a disruptive technology that has transformed our understanding of the evolution and epidemiology of diverse rGNB species, and it has the potential to guide strategies for controlling the evolution and spread of resistance. Here, we review specific areas in which genomics has already made a significant impact, including outbreak investigations, regional epidemiology, clinical diagnostics, resistance evolution, and the study of epidemic lineages. While highlighting early successes, we also point to the next steps needed to translate this technology into strategies to improve public health and clinical medicine.

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“…The reported performance metrics included accuracy, F1-score, and area under the receiver operating characteristic curve (AUROC) (21). We assessed the prediction accuracy using an independent dataset of bacterial isolates recovered from cancer patients with bloodstream infections (6). In this study (11), all isolates were genetically (whole-genome sequence) and phenotypically (antibiotic susceptibility testing by broth microdilution assays) profiled.…”

Section: Vampr Prediction Model For Antibiotic Resistancementioning

confidence: 99%

“…Next-generation sequencing (NGS) technology enabling whole genome sequencing (WGS) of bacterial isolates is now both inexpensive and widely-used (5). We have previously shown that NGS can identify AMR determinants for a limited number of b-lactam antimicrobials and that genotype correlated well with classic phenotypic testing (6).…”

Section: Introductionmentioning

confidence: 99%

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

Kim

Greenberg

Pfifer

et al. 2019

Preprint

Self Cite

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Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combinations. Given the rapid, ongoing increase in availability of high density genomic data for a diverse array of bacteria, development of algorithms that could utilize genomic information to predict phenotype could both be useful clinically and assist with discovery of heretofore unrecognized AMR pathways. To facilitate understanding of the connections between DNA variation and phenotypic AMR, we developed a new bioinformatics tool, variant mapping and prediction of antibiotic resistance (VAMPr), to (1) derive gene ortholog-based sequence features for variants; (2) interrogate these explainable gene-level variants for their known or novel associations with AMR; and (3) build accurate models to predict AMR based on whole genome sequencing data. Following the Clinical & Laboratory Standards Institute (CLSI) guidelines, we curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species along with AMR phenotypes for 29 antibiotics. We detected 14,615 variant genotypes and built 93 association and prediction models. The association models confirmed known genetic antibiotic resistance mechanisms, such as blaKPC and carbapenem resistance consistent with the accurate nature of our approach. The prediction models achieved high accuracies (mean accuracy of 91.1% for all antibiotic-pathogen combinations) internally through nested cross validation and were also validated using external clinical datasets. The VAMPr variant detection method, association and prediction models will be valuable tools for AMR research for basic scientists with potential for clinical applicability.

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Whole-Genome Sequencing Accurately Identifies Resistance to Extended-Spectrum β-Lactams for Major Gram-Negative Bacterial Pathogens

Abstract: These data demonstrate the potential feasibility of using WGS to guide antibiotic treatment decisions for patients with life-threatening infections for an array of medically important pathogens.

Cited by 62 publications

References 40 publications

Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in Escherichia coli

Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in Escherichia coli

Genomic epidemiology of multidrug‐resistant Gram‐negative organisms

VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning

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