High-Resolution Typing of Staphylococcus epidermidis Based on Core Genome Multilocus Sequence Typing To Investigate the Hospital Spread of Multidrug-Resistant Clones

Improved Genomic Prediction of Staphylococcus epidermidis Isolation Sources with a Novel Polygenic Score

Staphylococcus epidermidis infections can be challenging to diagnose due to the species frequent contamination of clinical specimens and indolent course of infection. Nevertheless, S. epidermidis is the major cause of late-onset sepsis among premature infants and of intravascular infection in all age groups. Prior work has shown that bacterial virulence factors, antimicrobial resistances, and strains have up to 80% in-sample accuracy to distinguish hospital from community sources, but are unable to distinguish true bacteremia from blood culture contamination. Here, a phylogeny-informed genome-wide association study of 88 isolates was used to estimate effect sizes of particular genomic variants for isolation sources. A "polygenic score" was calculated for each isolate as the summed effect sizes of its repertoire of genomic variants. Predictive models of isolation sources based on polygenic scores were tested with in-samples and out-samples from prior studies of different patient populations. Polygenic scores from accessory genes (AGs) distinguished hospital from community sources with the highest accuracy to date, up to 98% for in-samples and 65% to 91% for various out-samples, whereas scores from single nucleotide polymorphisms (SNPs) had lower accuracy. Scores from AGs and SNPs achieved the highest in-sample accuracy to date, up to 76%, in distinguishing infection from contaminant sources within a hospital. Model training and testing data sets with more similar population structures resulted in more accurate predictions. This study reports the first use of a polygenic score for predicting a complex bacterial phenotype and shows the potential of this approach for enhancing S. epidermidis diagnosis.

Development of a novel core genome MLST scheme for tracing multidrug resistant Staphylococcus capitis

Staphylococcus capitis, which causes bloodstream infections in neonatal intensive care units, is a common cause of healthcare-associated infections. Thus, a standardized high-resolution typing method to document the transmission and dissemination of multidrug-resistant S. capitis isolates is required. We aimed to establish a core genome multilocus sequence typing (cgMLST) scheme to surveil S. capitis. The cgMLST scheme was defined based on primary and validation genome sets and tested with outbreaks of linezolid-resistant isolates and a validation set. Phylogenetic analysis was performed to investigate the population structure and compare it with the result of cgMLST analysis. The S. capitis population consists of 1 dominant, NRCS-A, and 4 less common clones. In this work, a multidrug-resistant clone (L clone) with linezolid resistance is identified. With the features of type III SCCmec and multiple copies of mutations of G2576T and C2104T in the 23S rRNA, the L clone has been spreading silently across China.

Staphylococcus capitis is a common causative agent of bloodstream infections in neonatal intensive care units, with multidrug resistant isolates complicating treatment. Authors aimed to establish a core genome multilocus sequence typing (cgMLST) scheme to document the transmission and dissemination of multidrug-resistant S. capitis isolates.

Look Who's Talking: Host and Pathogen Drivers of Staphylococcus epidermidis Virulence in Neonatal Sepsis

Preterm infants are at increased risk for invasive neonatal bacterial infections. S. epidermidis, a ubiquitous skin commensal, is a major cause of late-onset neonatal sepsis, particularly in high-resource settings. The vulnerability of preterm infants to serious bacterial infections is commonly attributed to their distinct and developing immune system. While developmentally immature immune defences play a large role in facilitating bacterial invasion, this fails to explain why only a subset of infants develop infections with low-virulence organisms when exposed to similar risk factors in the neonatal ICU. Experimental research has explored potential virulence mechanisms contributing to the pathogenic shift of commensal S. epidermidis strains. Furthermore, comparative genomics studies have yielded insights into the emergence and spread of nosocomial S. epidermidis strains, and their genetic and functional characteristics implicated in invasive disease in neonates. These studies have highlighted the multifactorial nature of S. epidermidis traits relating to pathogenicity and commensalism. In this review, we discuss the known host and pathogen drivers of S. epidermidis virulence in neonatal sepsis and provide future perspectives to close the gap in our understanding of S. epidermidis as a cause of neonatal morbidity and mortality.

Using Neisseria meningitidis genomic diversity to inform outbreak strain identification

Meningococcal disease is a life-threatening illness caused by the human-restricted bacterium Neisseria meningitidis. Outbreaks in the USA involve at least two cases in an organization or community caused by the same serogroup within three months. Genome comparisons, including phylogenetic analysis and quantification of genome distances can provide confirmatory evidence of pathogen transmission during an outbreak. Interpreting genome distances depends on understanding their distribution both among isolates from outbreaks and among those not from outbreaks. Here, we identify outbreak strains based on phylogenetic relationships among 141 N. meningitidis isolates collected from 28 outbreaks in the USA during 2010-2017 and 1516 non-outbreak isolates collected through contemporaneous meningococcal surveillance. We show that genome distance thresholds based on the maximum SNPs and allele distances among isolates in the phylogenetically defined outbreak strains are sufficient to separate most pairs of non-outbreak isolates into separate strains. Non-outbreak isolate pairs that could not be distinguished from each other based on genetic distances were concentrated in the clonal complexes CC11, CC103, and CC32. Within each of these clonal complexes, phylodynamic analysis identified a group of isolates with extremely low diversity, collected over several years and multiple states. Clusters of isolates with low genetic diversity could indicate increased pathogen transmission, potentially resulting in local outbreaks or nationwide clonal expansions.