Disease-associated genotypes of the commensal skin bacterium Staphylococcus epidermidis

Abstract

Some of the most common infectious diseases are caused by bacteria that naturally colonise humans asymptomatically. Combating these opportunistic pathogens requires an understanding of the traits that differentiate infecting strains from harmless relatives. Staphylococcus epidermidis is carried asymptomatically on the skin and mucous membranes of virtually all humans but is a major cause of nosocomial infection associated with invasive procedures. Here we address the underlying evolutionary mechanisms of opportunistic pathogenicity by combining pangenome-wide association studies and laboratory microbiology to compare S. epidermidis from bloodstream and wound infections and asymptomatic carriage. We identify 61 genes containing infection-associated genetic elements (k-mers) that correlate with in vitro variation in known pathogenicity traits (biofilm formation, cell toxicity, interleukin-8 production, methicillin resistance). Horizontal gene transfer spreads these elements, allowing divergent clones to cause infection. Finally, Random Forest model prediction of disease status (carriage vs. infection) identifies pathogenicity elements in 415 S. epidermidis isolates with 80% accuracy, demonstrating the potential for identifying risk genotypes pre-operatively.

Peer Review #3 of "Genomic diversity of prevalent Staphylococcus epidermidis multidrug-resistant strains isolated from a Children’s Hospital in México City in an eight-years survey (v0.4)"

“…In the phylogenetic trees reported, pathogenic SE strains are intermingled with commensal SE strains with no pathogenic cluster found (Meric et al 2018;Miragaia et al 2005). Recently, Meric et al suggested, that pathogenic SE subpopulations occur within the commensal SE strains, which contain genes and alleles necessary for colonization at distinct infection sites (Meric et al 2018).…”

“…The genetic basis for pathogenicity of SE strains includes genes related to biofilm formation (adhesion), phenol soluble modulins (PSMs), and diverse Mobile Genetic Elements (MGEs) like phages, insertion sequences (ISs), and pathogenicity islands, that may be associated with the transfer of antibiotic and virulence traits (Bouchami et al 2016;Miragaia et al 2009;Rolo et al 2017) (Conlan et al 2012;Kozitskaya et al 2005;Meric et al 2015). There is no clear genetic distinction between pathogenic and commensal non-pathogenic SE strains, even though nosocomial strains are enriched in virulence and antibiotic resistance genes (Conlan et al 2012;Kozitskaya et al 2005;Meric et al 2018). It has been proposed that these genes are within the pool of accessory genome mobilized within and between species (Meric et al 2015;Rolo et al 2017).…”

Bacteriophages for Chronic Wound Treatment: From Traditional to Novel Delivery Systems

“…For instance, Staphylococcus epidermidis, a commensal inhabitant of human skin and mucous membranes, has emerged as a relevant opportunistic pathogen in hospitals. This opportunistic pathogen causes infections, mainly in the elderly and immunocompromised individuals [32,33]. Wound The exposed subcutaneous tissue, devitalized tissue (ischemic, hypoxic, or necrotic), and the compromised immune system of the host provide optimal conditions for microbial colonization and growth [31].…”

“…For instance, Staphylococcus epidermidis, a commensal inhabitant of human skin and mucous membranes, has emerged as a relevant opportunistic pathogen in hospitals. This opportunistic pathogen causes infections, mainly in the elderly and immunocompromised individuals [32,33]. Wound contamination can also occur through cross-infection events, such as poor hand hygiene practiced by healthcare clinicians after wound cleansing and dressing procedures, coughing and sneezing, dirty bedding, unsterilized medical equipment, and prolonged use of catheters, tubes, or intravenous lines [34][35][36][37][38].…”

Using Artificial Intelligence in Infection Prevention

“…In the clinical microbiology laboratory, machine learning algorithms developed from population genomics could be used to predict infection risks from the genomic features of Staphylococcus epidermidis and potentially identify highrisk genotypes preoperatively to target pre and postoperative HAI preventative programs [18]. AI-enhanced laboratory microscopy could streamline the rapid diagnosis of patients with infection and assist AMR prevention programs by facilitating targeted antimicrobial management and IPC intervention.…”