2014
DOI: 10.1093/gbe/evu092
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Dissecting Vancomycin-Intermediate Resistance in Staphylococcus aureus Using Genome-Wide Association

Abstract: Vancomycin-intermediate Staphylococcus aureus (VISA) is currently defined as having minimal inhibitory concentration (MIC) of 4–8 µg/ml. VISA evolves through changes in multiple genetic loci with at least 16 candidate genes identified in clinical and in vitro-selected VISA strains. We report a whole-genome comparative analysis of 49 vancomycin-sensitive S. aureus and 26 VISA strains. Resistance to vancomycin was determined by broth microdilution, Etest, and population analysis profile-area under the curve (PAP… Show more

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Cited by 130 publications
(130 citation statements)
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“…Alam et al reported that rpoB H481 is the predominant locus associated with an increased vancomycin MIC (Alam et al, 2014). The mutations H481Y/N in rpoB play a dual role in rifampin and vancomycin resistance (Watanabe et al, 2011; Gao et al, 2013).…”
Section: Discussionmentioning
confidence: 99%
“…Alam et al reported that rpoB H481 is the predominant locus associated with an increased vancomycin MIC (Alam et al, 2014). The mutations H481Y/N in rpoB play a dual role in rifampin and vancomycin resistance (Watanabe et al, 2011; Gao et al, 2013).…”
Section: Discussionmentioning
confidence: 99%
“…It is now appreciated that large numbers of microbial genome sequences can be used for robust genome-wide association studies (GWAS), enabling the detection of genetic factors underlying phenotypic variation (Falush and Bowden 2006;Farhat et al 2013;Sheppard et al 2013;Alam et al 2014;Laabei et al 2014). Here, in light of the open nature of the E. coli pan-genome, we observed a significant number of novel sequences present neither in reference genomes nor previous isolates with each additional strain that was sequenced.…”
Section: De Novo Identification Of Antibiotic Resistance Factorsmentioning
confidence: 94%
“…In contrast, the machine-learning approach does not consider the inter-dependence of individuals, which is not necessarily required if the purpose is simply to build a machine-learning classifier. In order to conduct a GWAS rather than a machine-learning approach, controlling for the inter-dependence of strains, or for population structure, is necessary to avoid inflation of the type I error, as described in previous reports of bacterial GWAS approaches12323334, one of which also took a machine-learning approach and succeeded in building a classifier of virulence32. We utilized the method implemented in the bugwas package, which, unlike a pioneering bacterial GWAS method based on clonal phylogeny12, does not involve splitting the data into each clonal complex for separate analysis, thus reducing the sample size for GWAS discovery.…”
Section: Discussionmentioning
confidence: 99%