ena-STUDY-UNIVERISTY OF OXFORD-27-01-2014-12:20:38:426-115
Prediction of Staphylococcus aureus antimicrobial resistance from whole genome sequencing
Whole genome sequencing (WGS) could potentially provide a single platform for extracting all the information required to predict an organism’s phenotype. However, its ability to provide accurate predictions has not yet been demonstrated in large independent studies for specific organisms. In this study we aimed to develop a genotypic prediction method for antimicrobial susceptibilities.  501 unrelated S. aureus isolates were whole genome sequenced and the assembled genomes interrogated using BLASTn for a panel of known resistance determinants (chromosomal mutations and genes carried on plasmids). Results were compared with phenotypic susceptibility testing for 12 commonly used antimicrobials (penicillin, methicillin, erythromycin, clindamycin, tetracycline, ciprofloxacin, vancomycin, trimethoprim, gentamicin, fusidic acid, rifampicin, mupirocin) performed by the routine clinical laboratory. We investigated discrepancies by repeat susceptibility testing and manual inspection of the sequences, and used this information to optimise the resistance determinant panel and BLASTn algorithm. We then tested performance of the optimised tool in an independent validation set of 491 unrelated isolates, with phenotypic results obtained in duplicate by automated broth dilution (BD Phoenix) and disc diffusion. In the validation set, overall sensitivity and specificity of the genomic prediction method were 0.97 (95% CI 0.95-0.98) and 0.99 (95% CI 0.99-1) respectively when compared to standard susceptibility testing methods. The very major error rate was 0.5% and the major error rate was 0.7% WGS was as sensitive and specific as routine antimicrobial susceptibility testing methods, and is a promising alternative to culture methods for resistance prediction in S. aureus and ultimately other major bacterial pathogens.
Whole genome sequencing (WGS) could potentially provide a single platform for extracting all the information required to predict an organism’s phenotype. However, its ability to provide accurate predictions has not yet been demonstrated in large independent studies for specific organisms. In this study we aimed to develop a genotypic prediction method for antimicrobial susceptibilities.  501 unrelated S. aureus isolates were whole genome sequenced and the assembled genomes interrogated using BLASTn for a panel of known resistance determinants (chromosomal mutations and genes carried on plasmids). Results were compared with phenotypic susceptibility testing for 12 commonly used antimicrobials (penicillin, methicillin, erythromycin, clindamycin, tetracycline, ciprofloxacin, vancomycin, trimethoprim, gentamicin, fusidic acid, rifampicin, mupirocin) performed by the routine clinical laboratory. We investigated discrepancies by repeat susceptibility testing and manual inspection of the sequences, and used this information to optimise the resistance determinant panel and BLASTn algorithm. We then tested performance of the optimised tool in an independent validation set of 491 unrelated isolates, with phenotypic results obtained in duplicate by automated broth dilution (BD Phoenix) and disc diffusion. In the validation set, overall sensitivity and specificity of the genomic prediction method were 0.97 (95% CI 0.95-0.98) and 0.99 (95% CI 0.99-1) respectively when compared to standard susceptibility testing methods. The very major error rate was 0.5% and the major error rate was 0.7% WGS was as sensitive and specific as routine antimicrobial susceptibility testing methods, and is a promising alternative to culture methods for resistance prediction in S. aureus and ultimately other major bacterial pathogens.