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The emergence of microbial antibiotic resistance is a global health threat. In clinical settings, the key to controlling spread of resistant strains is accurate and rapid detection. As traditional culture-based methods are time consuming, genetic approaches have recently been developed for this task. The detection of antibiotic resistance is typically made by measuring a few known determinants previously identified from genome sequencing, and thus requires the prior knowledge of its biological mechanisms. To overcome this limitation, we employed machine learning models to predict resistance to 11 compounds across four classes of antibiotics from existing and novel whole genome sequences of 1936 E. coli strains. We considered a range of methods, and examined population structure, isolation year, gene content, and polymorphism information as predictors. Gradient boosted decision trees consistently outperformed alternative models with an average accuracy of 0.91 on held-out data (range 0.81–0.97). While the best models most frequently employed gene content, an average accuracy score of 0.90 could be obtained using population structure information alone. Single nucleotide variation data were less useful, and significantly improved prediction only for two antibiotics, including ciprofloxacin. These results demonstrate that antibiotic resistance in E. coli can be accurately predicted from whole genome sequences without a priori knowledge of mechanisms, and that both genomic and epidemiological data can be informative. This paves way to integrating machine learning approaches into diagnostic tools in the clinic. [ABSTRACT FROM AUTHOR]

Background: Enterococcus faecium is a leading cause of hospital-acquired infection, particularly in the immunocompromised. Here, we use whole genome sequencing of E. faecium to study within-host evolution and the transition from gut carriage to invasive disease. Methods: We isolated and sequenced 180 E. faecium from four immunocompromised patients who developed bloodstream infection during longitudinal surveillance of E. faecium in stool and their immediate environment. Results: A phylogenetic tree based on single nucleotide polymorphisms (SNPs) in the core genome of the 180 isolates demonstrated several distinct clones. This was highly concordant with the population structure inferred by Bayesian methods, which contained four main BAPS (Bayesian Analysis of Population Structure) groups. The majority of isolates from each patient resided in a single group, but all four patients also carried minority populations in stool from multiple phylogenetic groups. Bloodstream isolates from each case belonged to a single BAPS group, which differed in all four patients. Analysis of 87 isolates (56 from blood) belonging to a single BAPS group that were cultured from the same patient over 54 days identified 30 SNPs in the core genome (nine intergenic, 13 non-synonymous, eight synonymous), and 250 accessory genes that were variably present. Comparison of these genetic variants in blood isolates versus those from stool or environment did not identify any variants associated with bloodstream infection. The substitution rate for these isolates was estimated to be 128 (95% confidence interval 79.82 181.77) mutations per genome per year, more than ten times higher than previous estimates for E. faecium. Within-patient variation in vancomycin resistance associated with vanA was common and could be explained by plasmid loss, or less often by transposon loss. Conclusions: These findings demonstrate the diversity of E. faecium carriage by individual patients and significant within-host diversity of E. faecium, but do not provide evidence for adaptive genetic variation associated with invasion. [ABSTRACT FROM AUTHOR]

Background: Klebsiella pneumoniae is a gram-negative bacterial species capable of occupying a broad range of environmental and clinical habitats. Known as an opportunistic pathogen, it has recently become a major causative agent of clinical infections worldwide. Despite growing knowledge about the highly diverse population of K. pneumoniae, the evolution and clinical significance of environmental K. pneumoniae, as well as the relationship between clinical and environmental K. pneumoniae, are poorly defined. Methods: We isolated and sequenced K. pneumoniae from in-patients in a single hospital in Thailand, as well as hospital sewage, and surrounding canals and farms within a 20-km radius. Results: Phylogenetic analysis of 77 K. pneumoniae (48 clinical and 29 non-clinical isolates) demonstrated that the two groups were intermixed throughout the tree and in some cases resided in the same clade, suggesting recent divergence from a common ancestor. Phylogenetic comparison of the 77 Thai genomes with 286 K. pneumoniae from a global collection showed that Thai isolates were closely related to the clinical sub-population of the global collection, indicating that Thai clinical isolates belonged to globally circulating lineages. Dating of four Thai K. pneumoniae clades indicated that they emerged between 50 and 150 years ago. Despite their phylogenetic relatedness, virulence factors and β-lactamase resistance genes were more numerous in clinical than in environmental isolates. Our results indicate that clinical and environmental K. pneumoniae are closely related, but that hospitals may select for isolates with a more resistant and virulent genotype. Conclusions: These findings highlight the clinical relevance of environmental K. pneumoniae isolates. [ABSTRACT FROM AUTHOR]