Investigation of a Cluster of Sphingomonas koreensis Infections

Abstract

BACKGROUND Plumbing systems are an infrequent but known reservoir for opportunistic microbial pathogens that can infect hospitalized patients. In 2016, a cluster of clinical sphingomonas infections prompted an investigation. METHODS We performed whole-genome DNA sequencing on clinical isolates of multidrug-resistant Sphingomonas koreensis identified from 2006 through 2016 at the National Institutes of Health (NIH) Clinical Center. We cultured S. koreensis from the sinks in patient rooms and performed both whole-genome and shotgun metagenomic sequencing to identify a reservoir within the infrastructure of the hospital. These isolates were compared with clinical and environmental S. koreensis isolates obtained from other institutions. RESULTS The investigation showed that two isolates of S. koreensis obtained from the six patients identified in the 2016 cluster were unrelated, but four isolates shared more than 99.92% genetic similarity and were resistant to multiple antibiotic agents. Retrospective analysis of banked clinical isolates of sphingomonas from the NIH Clinical Center revealed the intermittent recovery of a clonal strain over the past decade. Unique single-nucleotide variants identified in strains of S. koreensis elucidated the existence of a reservoir in the hospital plumbing. Clinical S. koreensis isolates from other facilities were genetically distinct from the NIH isolates. Hospital remediation strategies were guided by results of microbiologic culturing and fine-scale genomic analyses. CONCLUSIONS This genomic and epidemiologic investigation suggests that S. koreensis is an opportunistic human pathogen that both persisted in the NIH Clinical Center infrastructure across time and space and caused health care-associated infections. (Funded by the NIH Intramural Research Programs.)

Genomic and Metagenomic Approaches for Predictive Surveillance of Emerging Pathogens and Antibiotic Resistance

“…17 Four of the S. koreensis patient isolates had high genomic similarity to each other (>99.9% average nucleotide identity (ANI)) and to environmental S. koreensis isolates from water and faucet samples collected from inside patient rooms (>99.7% ANI). 17 This genomic similarity suggested that there was a common environmental reservoir for this pathogen that had persisted in the hospital infrastructure over time. 17 Further, banked S. koreensis isolates from 2006 also showed high similarity to isolates from the 2016 outbreak (>99.8% ANI), suggesting this reservoir had persisted for a decade.…”

“…17 This genomic similarity suggested that there was a common environmental reservoir for this pathogen that had persisted in the hospital infrastructure over time. 17 Further, banked S. koreensis isolates from 2006 also showed high similarity to isolates from the 2016 outbreak (>99.8% ANI), suggesting this reservoir had persisted for a decade. 17 These studies demonstrate the capabilities of WGS in tracking individual strains in outbreaks.…”

“…Beyond identification and characterization of AROs, it is critical to track and predict antibiotic-resistant outbreaks and reservoirs of AROs and ARGs. 16,17,29 To do this, studies must be able to accurately identify the relatedness and antibiotic resistance profiles of isolates. A recent study in Australia used WGS and culture-based methods to track an emerging pathogen, vanA-possessing vancomycin-resistant Enterococcus faecium (VREfm).…”

“…17 Sphingomonas species are common in built and aquatic environments but have been implicated in a range of infections, including pneumonia, meningitis, catheter-associated bloodstream infections, and wound infections. 17 This study investigated six infections of Sphingomonas over a 6-month period at the US National Institutes of Health (NIH). Core genome single nucleotide variants were used to identify and track strains across patients and clinical water sources.…”

“…10,14,15 These genomic assays are ideally combined with functional and culture-based assays STATE of the ART to improve surveillance and prediction of emerging resistance threats. 16,17 Further, by characterizing the vast biosynthetic capacity of the microbiome, we can accelerate the development of new antibiotic therapies to combat resistance. 18,19 In this review, we provide an overview of how high-throughput sequencing-based methods can be combined with classic culture-based assays to improve diagnosis and treatment of infectious disease.…”