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SummaryBackgroundDiagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drug-susceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistance-determining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all first-line and second-line drugs for tuberculosis.MethodsBetween Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identified from the drug-resistance scientific literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance.FindingsWe characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7–93·7) and 98·4% specificity (98·1–98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identified among mutations under selection pressure in non-candidate genes.InterpretationA broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workflows, phasing out phenotypic drug-susceptibility testing while reporting drug resistance early.

The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n=470). For M. tuberculosis, our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n=1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.

A robust high-throughput multilocus sequence typing (MLST) scheme for Clostridium difficile was developed and validated using a diverse collection of 50 reference isolates representing 45 different PCR ribotypes and 102 isolates from recent clinical samples. A total of 49 PCR ribotypes were represented overall. All isolates were typed by MLST and yielded 40 sequence types (STs). A web-accessible database was set up (http://pubmlst .org/cdifficile/) to facilitate the dissemination and comparison of C. difficile MLST genotyping data among laboratories. MLST and PCR ribotyping were similar in discriminatory abilities, having indices of discrimination of 0.90 and 0.92, respectively. Some STs corresponded to a single PCR ribotype (32/40), other STs corresponded to multiple PCR ribotypes (8/40), and, conversely, the PCR ribotype was not always predictive of the ST. The total number of variable nucleotide sites in the concatenated MLST sequences was 103/3,501 (2.9%). Concatenated MLST sequences were used to construct a neighbor-joining tree which identified four phylogenetic groups of STs and one outlier (ST-11; PCR ribotype 078). These groups apparently correlate with clades identified previously by comparative genomics. The MLST scheme was sufficiently robust to allow direct genotyping of C. difficile in total stool DNA extracts without isolate culture. The direct (nonculture) MLST approach may prove useful as a rapid genotyping method, potentially benefiting individual patients and informing hospital infection control.

Whole-genome sequencing has opened the way for investigating the dynamics and genomic evolution of bacterial pathogens during the colonization and infection of humans. The application of this technology to the longitudinal study of adaptation in an infected host--in particular, the evolution of drug resistance and host adaptation in patients who are chronically infected with opportunistic pathogens--has revealed remarkable patterns of convergent evolution, suggestive of an inherent repeatability of evolution. In this Review, we describe how these studies have advanced our understanding of the mechanisms and principles of within-host genome evolution, and we consider the consequences of findings such as a potent adaptive potential for pathogenicity. Finally, we discuss the possibility that genomics may be used in the future to predict the clinical progression of bacterial infections and to suggest the best option for treatment.

Objective. Based on a small clinical series and previously published case reports, concordance for systemic lupus erythematosus (SLE) among monozygous (MZ) twins has been reported to be as high as 69%. Using a larger and less biased sample, we provide another estimate of this percentage.Methods. We established a registry of twins with SLE, based upon self-reports and information provided by the patients' physicians. We used DNA fingerprinting to validate the reported zygosity in a sample of these twins.Results. firmed by DNA fingerprinting in a subsample of 15 self-described MZ twins and 7 self-described DZ twins. All individuals had correctly predicted their zygosity.Conclusion. MZ concordance for SLE is similar to that for other autoimmune diseases and is much lower than previously believed.Concordance for disease in pairs of twins provides evidence of the relative etiologic contributions of genetic and environmental factors, as well as the timing of these effects. Low rates of concordance for disease over a lifetime, especially among monozygotic (MZ) twins, suggests a greater influence of environment; a large difference in concordance between MZ and dizygotic (DZ) twins suggests a greater influence of genetic determinants. However, for most diseases, there are no population-based twin concordancy data.Although the estimated annual incidence rate for systemic lupus erythematosus (SLE) is 5CL75 per million (1,2), no population-based studies of concordance rates in twins have been published. One case series reported that 69% of the MZ twin pairs were concordant (3), and anecdotal reports describe a similar proportion of concordance among MZ twin pairs. These reports also suggest that a much lower proportion of DZ twin pairs are concordant. In the study presented here, we used a larger sample size and an ascertainment mechanism that is less overtly biased in an effort to derive more meaningful data concerning the etiology of SLE.

BACKGROUNDCandida auris is an emerging and multidrug-resistant pathogen. Here we report the epidemiology of a hospital outbreak of C. auris colonization and infection. METHODSAfter identification of a cluster of C. auris infections in the neurosciences intensive care unit (ICU) of the Oxford University Hospitals, United Kingdom, we instituted an intensive patient and environmental screening program and package of interventions. Multivariable logistic regression was used to identify predictors of C. auris colonization and infection. Isolates from patients and from the environment were analyzed by whole-genome sequencing. RESULTSA total of 70 patients were identified as being colonized or infected with C. auris between February 2, 2015, and August 31, 2017; of these patients, 66 (94%) had been admitted to the neurosciences ICU before diagnosis. Invasive C. auris infections developed in 7 patients. When length of stay in the neurosciences ICU and patient vital signs and laboratory results were controlled for, the predictors of C. auris colonization or infection included the use of reusable skin-surface axillary temperature probes (multivariable odds ratio, 6.80; 95% confidence interval [CI], 2.96 to 15.63; P<0.001) and systemic fluconazole exposure (multivariable odds ratio, 10.34; 95% CI, 1.64 to 65.18; P = 0.01). C. auris was rarely detected in the general environment. However, it was detected in isolates from reusable equipment, including multiple axillary skin-surface temperature probes. Despite a bundle of infection-control interventions, the incidence of new cases was reduced only after removal of the temperature probes. All outbreak sequences formed a single genetic cluster within the C. auris South African clade. The sequenced isolates from reusable equipment were genetically related to isolates from the patients. CONCLUSIONSThe transmission of C. auris in this hospital outbreak was found to be linked to reusable axillary temperature probes, indicating that this emerging pathogen can persist in the environment and be transmitted in health care settings.

Bacteria pose unique challenges for genome-wide association studies because of strong structuring into distinct strains and substantial linkage disequilibrium across the genome1,2. Although methods developed for human studies can correct for strain structure3,4, this risks considerable loss-of-power because genetic differences between strains often contribute substantial phenotypic variability5. Here, we propose a new method that captures lineage-level associations even when locus-specific associations cannot be fine-mapped. We demonstrate its ability to detect genes and genetic variants underlying resistance to 17 antimicrobials in 3,144 isolates from four taxonomically diverse clonal and recombining bacteria: Mycobacterium tuberculosis, Staphylococcus aureus, Escherichia coli and Klebsiella pneumoniae. Strong selection, recombination and penetrance confer high power to recover known antimicrobial resistance mechanisms and reveal a candidate association between the outer membrane porin nmpC and cefazolin resistance in E. coli. Hence, our method pinpoints locus-specific effects where possible and boosts power by detecting lineage-level differences when fine-mapping is intractable.