Applying Rapid Whole-Genome Sequencing To Predict Phenotypic Antimicrobial Susceptibility Testing Results among Carbapenem-Resistant Klebsiella pneumoniae Clinical Isolates

Tamma, Pranita D.; Fan, Yunfan; Bergman, Yehudit; Pertea, Geo; Kazmi, Abida Q.; Lewis, Shawna; Carroll, Karen C.; Schatz, Michael C.; Timp, Winston; Simner, Patricia J.

doi:10.1128/aac.01923-18

Cited by 65 publications

(50 citation statements)

References 43 publications

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“…For instance, we can be confident that exposure of many bacteria to high doses of fluoroquinolones like ciprofloxacin may select for substitutions in residues 83 or 87 of the drug target, DNA gyrase A (Fàbrega et al, 2009; Wong and Kassen, 2011). Furthermore, effective prediction of drug resistance phenotypes based on genome sequence data has been demonstrated for certain bacterial species (Bradley et al, 2015; Tamma et al, 2019). These predictable outcomes are the product of very strong selection in populations with ample mutation supply and relatively few single mutations that can achieve high-level resistance (Ibacache-Quiroga et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Parallel evolution of tobramycin resistance across species and environments

Scribner

Santos-López

Marshall

et al. 2019

Preprint

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words)13 An important problem in evolution is identifying the genetic basis of how different species adapt 14 to similar environments. Understanding how various bacterial pathogens evolve in response to 15 antimicrobial treatment is a pressing example of this problem, where discovery of molecular 16 parallelism could lead to clinically useful predictions. Evolution experiments with pathogens in 17 environments containing antibiotics combined with periodic whole population genome 18 sequencing can be used to characterize the evolutionary dynamics of the pathways to 19 antimicrobial resistance. We separately propagated two clinically relevant Gram-negative 20 pathogens, Pseudomonas aeruginosa and Acinetobacter baumannii, in increasing 21 concentrations of tobramycin in two different environments each: planktonic and biofilm. 22Independent of the pathogen, populations adapted to tobramycin selection by parallel evolution 23 of mutations in fusA1, encoding elongation factor G, and ptsP, encoding phosphoenolpyruvate 24 phosphotransferase. As neither gene is a direct target of this aminoglycoside, both are relatively 25 novel and underreported causes of resistance. Additionally, both species acquired antibiotic-26 associated mutations that were more prevalent in the biofilm lifestyle than planktonic, in electron 27 transport chain components in A. baumannii and LPS biosynthesis enzymes in P. aeruginosa 28 populations. Using existing databases, we discovered both fusA1 and ptsP mutations to be 29 prevalent in antibiotic resistant clinical isolates. Additionally, we report site-specific parallelism of 30 fusA1 mutations that extend across several bacterial phyla. This study suggests that strong 31 selective pressures such as antibiotic treatment may result in high levels of predictability in 32 molecular targets of evolution despite differences between organisms' genetic background and 33 environment.The notion that evolution can be forecasted at the level of phenotype, gene, or even 36 amino acid is no longer a fantasy in the post-genomic era (Lässig et al., 2017). If we 37 acknowledge that most forecasting efforts rely on history to anticipate the future, the explosive 38 growth of whole-genome sequencing (WGS) sets the stage to resolve evolutionary phenomena 39 in action and suggest the next selected path. Among the best examples, bacterial populations 40 exposed to strong selection like antibiotics and analyzed by WGS are likely to identify gene 41 regions that produce resistance (Ahmed et al., 2018a; Cooper, 2018; Feng et al., 2016; Palmer 42 and Kishony, 2013). Repeated instances of the same antibiotic selection may enrich the same 43 types of mutations and ultimately enable some measure of predictability (Ibacache-Quiroga et 44 al., 2018; Wong et al., 2012). For instance, we can be confident that exposure of many bacteria 45 to high doses of fluoroquinolones like ciprofloxacin may select for substitutions in residues 83 or 46 87 of the drug target, DNA gyrase A (Fàbrega et al., 2009; Wong and Kassen, 2011).47 Furt...

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Section: Introductionmentioning

confidence: 99%

Parallel evolution of tobramycin resistance across species and environments

Scribner

Santos-López

Marshall

et al. 2019

Preprint

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“…In many instances, a significantly strong association confirmed an expected antibiotic resistance mechanism (Fig 3). For example, the sequence variant K18768.0 represents βlactamase (Bla) encoding gene bla KPC , the K. pneumoniae carbapenemase whose presence is significantly associated with resistance to meropenem in K. pneumoniae (P-value <0.0001) [10] (Fig 3A). Variant K18093.13 is oprD, a major porin responsible for uptake of carbapenems in Pseudomonas [14].…”

Section: Association Models Between Sequence Variants and Antibioticmentioning

confidence: 99%

“…To build prediction models for a broader spectrum of antimicrobials, it is necessary to use model-based based methods to study the complex relationship among resistance loci. For example, other groups have utilized NGS data to identify the presence of genes or short nucleotide sequences that confer resistance in a variety of pathogens using k-nn or adaBoost algorithms [8][9][10]. However, these studies have not taken advantage of gene orthology features.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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 protein 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. We curated the publicly available sequencing data for 3,393 bacterial isolates from 9 species that contained 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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“…Several studies published in recent years successfully used WGS for the determination of antimicrobial susceptibility of various pathogens such as Staphylococcus aureus, 13 Mycobacterium tuberculosis, 14 Escherichia coli, 15 Neisseria gonorrhoeae, 16 Klebsiella pneumoniae. 17 One recent study by Nguyen et al 18 is of particular interest. The authors of this study used whole genome sequencing data together with paired antimicrobial susceptibility data of more than five thousand nontyphoidal Salmonella strains to generate a learning model for predicting minimum inhibitory concentration (MICs) for 15 antibiotics.…”

Section: Antimicrobial Susceptibility Testingmentioning

confidence: 99%

Molecular diagnostics in the clinical microbiology laboratory

Obranić

2019

Mol. exp. biol. med.

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Molecular diagnostics is broadly available in clinical microbiology laboratories worldwide, especially for the detection and identification of difficult-to-cultivate microorganisms. The field of clinical microbiology has experienced significant changes over the past decade due to extensive molecular biology research that resulted in novel molecular diagnostics technologies. These new technologies are being introduced in clinical microbiology laboratories with the aim of improving sensitivity, specificity, accuracy and time-to-diagnosis, ensuring valuable data for effective infectious disease clinical management, infection control and surveillance. They have a potential to greatly improve general healthcare, but also present certain challenges, mainly regarding the cost and the proper definition of test ordering and interpretation. This review will discuss the current and potential application of next-generation sequencing, digital PCR and syndromic multiplex molecular assays in clinical microbiology.

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Applying Rapid Whole-Genome Sequencing To Predict Phenotypic Antimicrobial Susceptibility Testing Results among Carbapenem-Resistant Klebsiella pneumoniae Clinical Isolates

Cited by 65 publications

References 43 publications

Parallel evolution of tobramycin resistance across species and environments

Parallel evolution of tobramycin resistance across species and environments

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

Molecular diagnostics in the clinical microbiology laboratory

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