Functional analysis of the first complete genome sequence of a multidrug resistant sequence type 2 Staphylococcus epidermidis

Staphylococcus epidermidis is a significant opportunistic pathogen of humans. The ST2 lineage is frequently multidrug-resistant and accounts for most of the clinical disease worldwide. However, there are no publically available, closed ST2 genomes and pathogenesis studies have not focused on these strains. We report the complete genome and methylome of BPH0662, a multidrug-resistant, hospital-adapted, ST2 S. epidermidis, and describe the correlation between resistome and phenotype, as well as demonstrate its relationship to publically available, international ST2 isolates. Furthermore, we delineate the methylome determined by the two type I restriction modification systems present in BPH0662 through heterologous expression in Escherichia coli, allowing the assignment of each system to its corresponding target recognition motif. As the first, to our knowledge, complete ST2 S. epidermidis genome, BPH0662 provides a valuable reference for future genomic studies of this clinically relevant lineage. Defining the methylome and the construction of these E. coli hosts provides the foundation for the development of molecular tools to bypass restriction modification systems in this lineage that has hitherto proven intractable

Clinical evaluation of four commercial immunoassays for the detection of antibodies against established SARS-CoV-2 infection

A comparison of the clinical performance of the Elecsys Anti-SARS-CoV-2, Liaison SARS-CoV-2 S1/S2 IgG, Access SARS-CoV-2 IgG and Vitros Immunodiagnostic Products Anti-SARS-CoV-2 IgG immunoassays for the diagnosis of COVID-19 infection was performed. Patient sera were collected at least 6 weeks following onset of COVID-19 infection symptoms. Negative control specimens were stored specimens from those without COVID-19, collected in April–May 2019. Sensitivity and specificity with 95% confidence intervals (CI) were calculated. Linear regression was used to examine the relationship between the magnitude of serological response and clinical characteristics. There were 80 patients from whom 86 sera specimens were collected; six patients had duplicate specimens. There were 95 negative control specimens from 95 patients. The clinical sensitivity of the Elecsys assay was 98.84% (95% CI 93.69–99.97), specificity was 100% (95% CI 96.19–100.00); the Liaison assay clinical sensitivity was 96.51% (95% CI 90.14–99.27), specificity was 97.89% (95% CI 92.60–99.74); the Access assay clinical sensitivity was 84.88% (95% CI 75.54–91.70), specificity was 98.95% (95% CI 94.27–99.97); and the Vitros assay clinical sensitivity was 97.67% (95% CI 91.85–99.72), specificity was 100% (95% CI 96.15–100.00). A requirement for hospitalisation for COVID-19 infection was associated with a larger Vitros, Liaison and Access IgG response whilst fever was associated with a larger Elecsys response. All assays evaluated with the exception of the Access assay demonstrated similar performance. The Elecsys assay demonstrated the highest sensitivity and specificity

COVID-MATCH65-A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2

OBJECTIVES: We report on the key clinical predictors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present a clinical decision rule that can risk stratify patients for COVID-19. DESIGN, PARTICIPANTS AND SETTING: A prospective cohort of patients assessed for COVID-19 at a screening clinic in Melbourne, Australia. The primary outcome was a positive COVID-19 test from nasopharyngeal swab. A backwards stepwise logistic regression was used to derive a model of clinical variables predictive of a positive COVID-19 test. Internal validation of the final model was performed using bootstrapped samples and the model scoring derived from the coefficients, with modelling performed for increasing prevalence. RESULTS: Of 4226 patients with suspected COVID-19 who were assessed, 2976 patients underwent SARS-CoV-2 testing (n = 108 SARS-CoV-2 positive) and were used to determine factors associated with a positive COVID-19 test. The 7 features associated with a positive COVID-19 test on multivariable analysis were: COVID-19 patient exposure or international travel, Myalgia/malaise, Anosmia or ageusia, Temperature, Coryza/sore throat, Hypoxia-oxygen saturation < 97%, 65 years or older-summarized in the mnemonic COVID-MATCH65. Internal validation showed an AUC of 0.836. A cut-off of ≥ 1.5 points was associated with a 92.6% sensitivity and 99.5% negative predictive value (NPV) for COVID-19. CONCLUSIONS: From the largest prospective outpatient cohort of suspected COVID-19 we define the clinical factors predictive of a positive SARS-CoV-2 test. The subsequent clinical decision rule, COVID-MATCH65, has a high sensitivity and NPV for SARS-CoV-2 and can be employed in the pandemic, adjusted for disease prevalence, to aid COVID-19 risk-assessment and vital testing resource allocation

A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

Public health agencies are increasingly relying on genomics during Legionnaires' disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure, with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires' disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia, between 1994 and 2014. This collection included one of the largest reported Legionnaires' disease outbreaks, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK

Panton-Valentine leukocidin-associated Staphylococcus aureus necrotizing pneumonia in infants: A report of four cases and review of the literature

Four children under 16 months of age presented within an 18-month period with severe, rapidly progressive Panton-Valentine leukocidin-associated ST93 Staphylococcus aureus necrotizing pneumonia. Two of the cases that required extracorporeal membranous oxygenation and proved fatal had poor prognostic features of leukopaenia, rash and pulmonary haemorrhage. All four cases had recent contact with S. aureus infection in a family member. Reported cases of S. aureus necrotizing pneumonia in infants are reviewed, and approach to management is discussed