ArticleThe Journal of antimicrobial chemotherapy2013
Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data.
Article in The Journal of antimicrobial chemotherapy, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers, 2 of them syntheses that pooled it.
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Who cites it
170 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Large-scale assessment of antimicrobial resistance marker databases for genetic phenotype prediction: a systematic review.The Journal of antimicrobial chemotherapy · 2020Pooled it
- Global Extraintestinal Pathogenic Escherichia coli (ExPEC) Lineages.Clinical microbiology reviews · 2019Pooled it
- A multicenter clinical evaluation of polymerase chain reaction coupled with quantum dot fluorescence analysis in pathogen detection for patients with suspected bloodstream infections.Microbiology spectrum · 2026Article
- Antimicrobial Resistance and Biofilm in Bacteria from Rehabilitated Sapajus libidinosus.EcoHealth · 2026Article
- Integrated Phenotypic and Genomic Characterization of Cefotaxime/Clavulanic Acid Inhibitor-Positive Multidrug-ResistantAnimals : an open access journal from MDPI · 2026Article
- Whole-Genome Sequence Analysis of Colistin-Resistant,Antibiotics (Basel, Switzerland) · 2026Article
- A metabolic atlas of the Klebsiella pneumoniae species complex reveals lineage-specific metabolism and capacity for intra-species co-operation.PLoS biology · 2025Article
- An explainable artificial intelligence framework reveals mutations associated with drug resistance inBiosafety and health · 2025Article
- Escherichia coli phylogeny drives co-amoxiclav resistance through variable expression of TEM-1 beta-lactamase.Nature communications · 2025Article
- Machine learning-selected minimal features drive high-accuracy rule-based antibiotic susceptibility predictions forMicrobiology spectrum · 2025Article
- Article
- aiGeneR 3.0: an enhanced deep network model for resistant strain identification and multi-drug resistance prediction inFrontiers in genetics · 2025Article
- Metagenomic Next-Generation Sequencing of Bronchoalveolar Lavage Fluids Improves Pathogen Detection and Antimicrobial Stewardship in Lower Respiratory Tract Infections: A Retrospective Study.Infection and drug resistance · 2025Article
- PanARGMiner (Pan-Genomic Antimicrobial Resistance Gene Miner): An advanced feature selection framework for extracting key resistance genes from pan-genomic datasets.Computational and structural biotechnology journal · 2025Article
- Detecting antibiotic resistance: classical, molecular, advanced bioengineering, and AI-enhanced approaches.Frontiers in microbiology · 2025Review
- Microbial community structure and resistome dynamics on elevator buttons in response to surface disinfection practices.Frontiers in public health · 2025Article
- Neural network-based predictions of antimicrobial resistance phenotypes in multidrug-resistantAntimicrobial agents and chemotherapy · 2024Article
- Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains.Computational and structural biotechnology journal · 2024Article
- The application status of sequencing technology in global respiratory infectious disease diagnosis.Infection · 2024Review
- Exploring uncatalogued genetic variation in antimicrobial resistance gene families in Escherichia coli: an observational analysis.The Lancet. Microbe · 2024Observational
110 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectivesWhole-genome sequencing potentially represents a single, rapid and cost-effective approach to defining resistance mechanisms and predicting phenotype, and strain type, for both clinical and epidemiological purposes. This retrospective study aimed to determine the efficacy of whole genome-based antimicrobial resistance prediction in clinical isolates of Escherichia coli and Klebsiella pneumoniae.
methodsSeventy-four E. coli and 69 K. pneumoniae bacteraemia isolates from Oxfordshire, UK, were sequenced (Illumina HiSeq 2000). Resistance phenotypes were predicted from genomic sequences using BLASTn-based comparisons of de novo-assembled contigs with a study database of >100 known resistance-associated loci, including plasmid-associated and chromosomal genes. Predictions were made for seven commonly used antimicrobials: amoxicillin, co-amoxiclav, ceftriaxone, ceftazidime, ciprofloxacin, gentamicin and meropenem. Comparisons were made with phenotypic results obtained in duplicate by broth dilution (BD Phoenix). Discrepancies, either between duplicate BD Phoenix results or between genotype and phenotype, were resolved with gradient diffusion analyses.
resultsA wide variety of antimicrobial resistance genes were identified, including blaCTX-M, blaLEN, blaOKP, blaOXA, blaSHV, blaTEM, aac(3')-Ia, aac-(3')-IId, aac-(3')-IIe, aac(6')-Ib-cr, aadA1a, aadA4, aadA5, aadA16, aph(6')-Id, aph(3')-Ia, qnrB and qnrS, as well as resistance-associated mutations in chromosomal gyrA and parC genes. The sensitivity of genome-based resistance prediction across all antibiotics for both species was 0.96 (95% CI: 0.94-0.98) and the specificity was 0.97 (95% CI: 0.95-0.98). Very major and major error rates were 1.2% and 2.1%, respectively.
conclusionsOur method was as sensitive and specific as routinely deployed phenotypic methods. Validation against larger datasets and formal assessments of cost and turnaround time in a routine laboratory setting are warranted.
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