N. Claire Gordon

Identifying lineage effects when controlling for population structure improves power in bacterial association studies.

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.

The usefulness of whole genome sequencing in the management of Staphylococcus aureus infections

Staphylococcus aureus remains a leading cause of hospital-acquired and community-associated infection worldwide. The burden of disease is exacerbated by the emergence of virulent strains with reduced susceptibility to commonly used antibiotics and their dissemination in healthcare settings and in the community. Whole genome sequencing (WGS) has the potential to revolutionize our understanding and management of S. aureus infection. As a research tool, WGS has provided insights into the origins of antibiotic-resistant strains, the genetic basis of virulence, the emergence and spread of lineages, and the population structure of S. aureus. As a frontline tool, WGS offers the prospect of a method that could be used to predict resistance, assess virulence, and type isolates at the highest possible resolution. The results generated could be used to guide clinical management and infection control practice. Studies using bench-top sequencing machines have already demonstrated the feasibility of such approaches. Infection control management is compromised by our incomplete understanding of transmission, which in turn reflects the suboptimal resolution offered by conventional typing methods. As the costs of sequencing begin to approach those of conventional methods, high-resolution typing with WGS could realistically be implemented for hospital infection control, as well as for local and national surveillance practice. Translation into routine practice will require the development of a knowledge base, reliable automated bioinformatic tools, the capacity to store, exchange and interrogate large volumes of genomic data, and an acceptance of WGS by clinicians, infection control specialists, and laboratory staff.

Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis.

Rapid and accurate detection of antibiotic resistance in pathogens is an urgent need, affecting both patient care and population-scale control. Microbial genome sequencing promises much, but many barriers exist to its routine deployment. Here, we address these challenges, using a de Bruijn graph comparison of clinical isolate and curated knowledge-base to identify species and predict resistance profile, including minor populations. This is implemented in a package, Mykrobe predictor, for S. aureus and M. tuberculosis, running in under three minutes on a laptop from raw data. For S. aureus, we train and validate in 495/471 samples respectively, finding error rates comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.3%/99.5% across 12 drugs. For M. tuberculosis, we identify species and predict resistance with specificity of 98.5% (training/validating on 1920/1609 samples). Sensitivity of 82.6% is limited by current understanding of genetic mechanisms. Finally, we demonstrate feasibility of an emerging single-molecule sequencing technique.

Severe infections emerge from commensal bacteria by adaptive evolution

Bacteria responsible for the greatest global mortality colonize the human microbiota far more frequently than they cause severe infections. Whether mutation and selection among commensal bacteria are associated with infection is unknown. We investigated de novo mutation in 1163 Staphylococcus aureus genomes from 105 infected patients with nose colonization. We report that 72% of infections emerged from the nose, with infecting and nose-colonizing bacteria showing parallel adaptive differences. We found 2.8-to-3.6-fold adaptive enrichments of protein-altering variants in genes responding to rsp, which regulates surface antigens and toxin production; agr, which regulates quorum-sensing, toxin production and abscess formation; and host-derived antimicrobial peptides. Adaptive mutations in pathogenesis-associated genes were 3.1-fold enriched in infecting but not nose-colonizing bacteria. None of these signatures were observed in healthy carriers nor at the species-level, suggesting infection-associated, short-term, within-host selection pressures. Our results show that signatures of spontaneous adaptive evolution are specifically associated with infection, raising new possibilities for diagnosis and treatment.

Robust Prediction of Resistance to Trimethoprim in Staphylococcus aureus

The rise of antibiotic resistance threatens modern medicine; to combat it new diagnostic methods are required. Sequencing the whole genome of a pathogen offers the potential to accurately determine which antibiotics will be effective to treat a patient. A key limitation of this approach is that it cannot classify rare or previously unseen mutations. Here we demonstrate that alchemical free energy methods, a well-established class of methods from computational chemistry, can successfully predict whether mutations in Staphylococcus aureus dihydrofolate reductase confer resistance to trimethoprim. We also show that the method is quantitatively accurate by calculating how much the most common resistance-conferring mutation, F99Y, reduces the binding free energy of trimethoprim and comparing predicted and experimentally measured minimum inhibitory concentrations for seven different mutations. Finally, by considering up to 32 free energy calculations for each mutation, we estimate its specificity and sensitivity.

Severe infections emerge from the microbiome by adaptive evolution

Bacteria responsible for the greatest global mortality colonize the human microbiome far more frequently than they cause severe infections. Whether mutation and selection within the microbiome precipitate infection is unknown. To address this question, we investigated de novo mutation in 1163 Staphylococcus aureus genomes from 105 infected patients with nose-colonization. We report that 72% of the infections emerged from the microbiome, with infecting and nose-colonizing bacteria showing systematic adaptive differences. We found 3.6-fold, 2.9-fold and 2.8-fold enrichments of protein-altering variants in genes responding to rsp, which regulates surface antigens and toxicity; agr, which regulates quorum-sensing, toxicity and abscess formation; and host-derived antimicrobial peptides, respectively. These adaptive signatures were not observed in healthy carriers and differed from prevailing species-level signals of selection, suggesting disease-associated, short-term, within-host selection pressures. Our results show that infection, like a cancer of the microbiome, emerges through spontaneous adaptive evolution, raising new possibilities for diagnosis and treatment.

AMR Surveillance in low and middle-income settings - A roadmap for participation in the Global Antimicrobial Surveillance System (GLASS)

Drug-resistant infections caused by bacteria with increasing antimicrobial resistance (AMR) threaten our ability to treat life-threatening conditions. Tackling AMR requires international collaboration and partnership. An early and leading priority to do this is to strengthen AMR surveillance, particularly in low-income countries where the burden of infectious diseases is highest and where data are most limited. The World Health Organization (WHO) has developed the Global AMR Surveillance System (GLASS) as one of a number of measures designed to tackle the problem of AMR, and WHO member states have been encouraged to produce National Action Plans for AMR by 2017. However, low-income countries are unlikely to have the resources or capacity to implement all the components in the GLASS manual. To facilitate their efforts, we developed a guideline that is aligned to the GLASS procedures, but written specifically for implementation in low-income countries. The guideline allows for flexibility across different systems, but has sufficient standardisation of core protocols to ensure that, if followed, data will be valid and comparable. This will ensure that the surveillance programme can provide health intelligence data to inform evidence-based interventions at local, national and international levels.

Survival following Staphylococcus aureus bloodstream infection; a prospective multinational cohort study assessing the impact of place of care

BACKGROUND Staphylococcus aureus bloodstream infection (SAB) is a common, life-threatening infection with a high mortality. Survival can be improved by implementing quality of care bundles in hospitals. We previously observed marked differences in mortality between hospitals and now assessed whether mortality could serve as a valid and easy to implement quality of care outcome measure. METHODS We conducted a prospective observational study between January 2013 and April 2015 on consecutive, adult patients with SAB from 11 tertiary care centers in Germany, South Korea, Spain, Taiwan, and the United Kingdom. Factors associated with mortality at 90 days were analyzed by Cox proportional hazards regression and flexible parametric models. RESULTS 1851 patients with a median age of 66 years (64% male) were analyzed. Crude 90-day mortality differed significantly between hospitals (range 23-39%). Significant variation between centers was observed for methicillin-resistant S. aureus, community-acquisition, infective foci, as well as measures of comorbidities, and severity of disease. In multivariable analysis, factors independently associated with mortality at 90 days were age, nosocomial acquisition, unknown infective focus, pneumonia, Charlson comorbidity index, SOFA score, and study center. The risk of death varied over time differently for each infective focus. Crude mortality differed markedly from adjusted mortality. DISCUSSION We observed significant differences in adjusted mortality between hospitals, suggesting differences in quality of care. However, mortality is strongly influenced by patient mix and thus, crude mortality is not a suitable quality indicator.