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 of specific organisms. In this study, we aimed to develop a genotypic prediction method for antimicrobial susceptibilities. The whole genomes of 501 unrelated Staphylococcus aureus isolates were sequenced, and the assembled genomes were 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 antimicrobial agents (penicillin, methicillin, erythromycin, clindamycin, tetracycline, ciprofloxacin, vancomycin, trimethoprim, gentamicin, fusidic acid, rifampin, and 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 optimize the resistance determinant panel and BLASTn algorithm. We then tested performance of the optimized 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, the overall sensitivity and specificity of the genomic prediction method were 0.97 (95% confidence interval [95% CI], 0.95 to 0.98) and 0.99 (95% CI, 0.99 to 1), respectively, 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. WGS is a promising alternative to culture methods for resistance prediction in S. aureus and ultimately other major bacterial pathogens.

SummaryBackgroundSlow and cumbersome laboratory diagnostics for Mycobacterium tuberculosis complex (MTBC) risk delayed treatment and poor patient outcomes. Whole-genome sequencing (WGS) could potentially provide a rapid and comprehensive diagnostic solution. In this prospective study, we compare real-time WGS with routine MTBC diagnostic workflows.MethodsWe compared sequencing mycobacteria from all newly positive liquid cultures with routine laboratory diagnostic workflows across eight laboratories in Europe and North America for diagnostic accuracy, processing times, and cost between Sept 6, 2013, and April 14, 2014. We sequenced specimens once using local Illumina MiSeq platforms and processed data centrally using a semi-automated bioinformatics pipeline. We identified species or complex using gene presence or absence, predicted drug susceptibilities from resistance-conferring mutations identified from reference-mapped MTBC genomes, and calculated genetic distance to previously sequenced UK MTBC isolates to detect outbreaks. WGS data processing and analysis was done by staff masked to routine reference laboratory and clinical results. We also did a microcosting analysis to assess the financial viability of WGS-based diagnostics.FindingsCompared with routine results, WGS predicted species with 93% (95% CI 90–96; 322 of 345 specimens; 356 mycobacteria specimens submitted) accuracy and drug susceptibility also with 93% (91–95; 628 of 672 specimens; 168 MTBC specimens identified) accuracy, with one sequencing attempt. WGS linked 15 (16% [95% CI 10–26]) of 91 UK patients to an outbreak. WGS diagnosed a case of multidrug-resistant tuberculosis before routine diagnosis was completed and discovered a new multidrug-resistant tuberculosis cluster. Full WGS diagnostics could be generated in a median of 9 days (IQR 6–10), a median of 21 days (IQR 14–32) faster than final reference laboratory reports were produced (median of 31 days [IQR 21–44]), at a cost of £481 per culture-positive specimen, whereas routine diagnosis costs £518, equating to a WGS-based diagnosis cost that is 7% cheaper annually than are present diagnostic workflows.InterpretationWe have shown that WGS has a scalable, rapid turnaround, and is a financially feasible method for full MTBC diagnostics. Continued improvements to mycobacterial processing, bioinformatics, and analysis will improve the accuracy, speed, and scope of WGS-based diagnosis.FundingNational Institute for Health Research, Department of Health, Wellcome Trust, British Colombia Centre for Disease Control Foundation for Population and Public Health, Department of Clinical Microbiology, Trinity College Dublin.

Background New approaches are urgently required to address increasing rates of gonorrhoea and the emergence and global spread of antibiotic-resistant Neisseria gonorrhoeae. Whole genome sequencing (WGS) can be applied to study transmission and track resistance. Methods We performed WGS on 1659 isolates from Brighton, UK, and 217 additional isolates from other UK locations. We included WGS data (n=196) from the USA. Estimated mutation rates, plus diversity observed within patients across anatomical sites and probable transmission pairs, were used to fit a coalescent model to determine the number of single nucleotide polymorphisms (SNPs) expected between sequences related by direct/indirect transmission, depending on the time between samples. Findings We detected extensive local transmission. 281/1061(26%) Brighton cases were indistinguishable (0 SNPs) to ≥1 previous case(s), and 786(74%) had evidence of a sampled direct or indirect Brighton source. There was evidence of sustained transmission of some lineages. We observed multiple related samples across geographic locations. Of 1273 infections in Brighton, 225(18%) were linked to another case from elsewhere in the UK, and 115(9%) to a case from the USA. Four lineages initially identified in Brighton could be linked to 70 USA sequences, including 61 from a lineage carrying the mosaic penA XXXIV associated with reduced cefixime susceptibility. Interpretation We present a WGS-based tool for genomic contact tracing of N. gonorrhoeae and demonstrate local, national and international transmission. WGS can be applied across geographical boundaries to investigate gonorrhoea transmission and to track antimicrobial resistance. Funding Oxford NIHR Health Protection Research Unit and Biomedical Research Centre.

BackgroundTracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes.ObjectivesWe investigate whether WGS and simultaneous analysis of multiple resistance determinants can be used to predict antimicrobial susceptibilities to the level of MICs in N. gonorrhoeae.MethodsWGS was used to identify previously reported potential resistance determinants in 681 N. gonorrhoeae isolates, from England, the USA and Canada, with phenotypes for cefixime, penicillin, azithromycin, ciprofloxacin and tetracycline determined as part of national surveillance programmes. Multivariate linear regression models were used to identify genetic predictors of MIC. Model performance was assessed using leave-one-out cross-validation.ResultsOverall 1785/3380 (53%) MIC values were predicted to the nearest doubling dilution and 3147 (93%) within ±1 doubling dilution and 3314 (98%) within ±2 doubling dilutions. MIC prediction performance was similar across the five antimicrobials tested. Prediction models included the majority of previously reported resistance determinants. Applying EUCAST breakpoints to MIC predictions, the overall very major error (VME; phenotypically resistant, WGS-prediction susceptible) rate was 21/1577 (1.3%, 95% CI 0.8%–2.0%) and the major error (ME; phenotypically susceptible, WGS-prediction resistant) rate was 20/1186 (1.7%, 1.0%–2.6%). VME rates met regulatory thresholds for all antimicrobials except cefixime and ME rates for all antimicrobials except tetracycline. Country of testing was a strongly significant predictor of MIC for all five antimicrobials.ConclusionsWe demonstrate a WGS-based MIC prediction approach that allows reliable MIC prediction for five gonorrhoea antimicrobials. Our approach should allow reasonably precise prediction of MICs for a range of bacterial species.