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We investigated genetic, epidemiologic, and environmental factors contributing to positive Staphylococcus epidermidis joint cultures.
Retrospective cohort study with whole-genome sequencing (WGS).
We identified S. epidermidis isolates from hip or knee cultures in patients with 1 or more prior corresponding intra-articular procedure at our hospital.
WGS and single-nucleotide polymorphism–based clonality analyses were performed, including species identification, in silico multilocus sequence typing (MLST), phylogenomic analysis, and genotypic assessment of the prevalence of specific antibiotic resistance and virulence genes. Epidemiologic review was performed to compare cluster and noncluster cases.
In total, 60 phenotypically distinct S. epidermidis isolates were identified. After removal of duplicates and impure samples, 48 isolates were used for the phylogenomic analysis, and 45 (93.7%) isolates were included in the clonality analysis. Notably, 5 S. epidermidis strains (10.4%) showed phenotypic susceptibility to oxacillin yet harbored mecA, and 3 (6.2%) strains showed phenotypic resistance despite not having mecA. Smr was found in all isolates, and mupA positivity was not observed. We also identified 6 clonal clusters from the clonality analysis, which accounted for 14 (31.1%) of the 45 S. epidermidis isolates. Our epidemiologic investigation revealed ties to common aspirations or operative procedures, although no specific common source was identified.
Most S. epidermidis isolates from clinical joint samples are diverse in origin, but we identified an important subset of 31.1% that belonged to subclinical healthcare–associated clusters. Clusters appeared to resolve spontaneously over time, suggesting the benefit of routine hospital infection control and disinfection practices.
Background: Traditional hospital outbreak-detection methods are typically limited to select multidrug-resistant pathogens in a single unit, which can miss transmission of many medically important healthcare-transmissible pathogens. Whole-genome sequencing (WGS) enables comprehensive genomic resolution for accurate identification of clonal transmission. Previously, lack of scalability limited the use of WGS for hospital surveillance. Methods: We conducted prospective surveillance of select bacteria from all inpatient clinical cultures plus all bacteria from clinical cultures from ICUs and oncology units at the University of California Irvine (UCI) Clinical Microbiology Laboratory from September 2021 to February 2022. Due to pandemic stressors, this pilot test was a prelude to a real-time demonstration project. Its goal was to demonstrate the efficiency and scalability of the WGS platform when receiving samples monthly and analyzing results quarterly without the intent for real-time response. Bacterial isolates slated for discard were collected weekly and sent monthly to Day Zero Diagnostics for sequencing. In total, 1,036 samples from 926 patients were analyzed for genomic relatedness, a scalable and automated analysis pipeline already in use for rapid (days) characterization of genomic-relatedness in small and large sets of isolates. Mapping and SNP calling was performed against high-quality, best-match reference genomes. Sets of samples with pairwise distance of 2 persons with genomically related isolates and were denoted as “clusters.” Separately, we also investigated within-patient diversity by quantifying the genomic relatedness of isolates collected from individual patients. Results: Isolates represented 28 distinct species. We identified 10 Escherichia coli clusters (range, 2–4 patients; median, 2 patients), 2 Klebsiella pneumoniae clusters (range, 2–4 patients), and 1 Enterococcus faecium cluster (3 patients). All but 1 involved genomically matched isolates from multiple hospital locations. There were 4 Escherichia coli ST131 clusters spanning 4 months, including 1 with 4 patients across 3 different hospital locations. At a species level, there were distinct differences between the observed SNP distances between samples isolated from the same versus different patients (Fig. 1). All identified clusters had not been flagged by routine outbreak detection methods used by the UCI infection prevention program. Conclusions: Comprehensive WGS-based surveillance of hospital clinical isolates identified multiple potential transmission events between patients not in the same unit at the time cultures were taken. Combining WGS detection and real-time epidemiologic investigation may identify new avenues of transmission risk and could provide early warnings of clonal transmission to prevent larger outbreaks. High-volume surveillance of hospital isolates can also provide species- and context-specific clonality.
Financial support: This study was funded by Day Zero Diagnostics.
Background: Fecal microbiota transplantation (FMT) is a widely used modality for safe and effective treatment of recurrent Clostridium difficile infections, and FMT is being explored for the treatment of additional indications including gastrointestinal diseases and neurological disorders. Although microbiota-based therapies like FMT utilize rigorous donor screening procedures, these procedures are limited in resolution and scope, and there remains a risk of transmission of FMT-associated infectious agents from donor stool to a FMT recipient. Critically, these health concerns led the FDA to issue a 2019 safety alert for the transmission risks associated with FMT and to update its guidelines for screening and reporting. In a suspected transmission event, there is uncertainty around the source of infection; thus, methods are needed to rapidly determine whether a patient’s infection is linked to the donor stool product. Methods: Here, we developed a laboratory service sequencing and bioinformatics pipeline within our CLIA-certified laboratory for investigating suspected FMT infection transmission by measuring genomic relatedness. Our pipeline performs deep sequencing of a metagenomic sample, whole-genome sequencing (WGS) of an isolate derived from the implicated patient infection and determines the genomic relatedness between the 2 using a SNP-based analysis. The workflow was validated in silico with synthetic metagenomic samples spiked-in with WGS of clinically relevant isolate strains at varying abundance. Results: The sample and sequencing library preparation workflow was optimized across a panel of metagenomic and mock fecal microbiome samples demonstrating reproducible and reduced-bias sequencing of metagenomic samples. Our pipeline demonstrates high sensitivity and specificity for clonality calls when a spiked in isolate genome achieves 5× depth for >50% of the genome. We also demonstrated an interplay between abundance rate and sequencing depth for determining a clonality limit of detection. Conclusions: Taken together, our pipeline represents a new method that can support the clinical efforts of FMT and other microbiota-based therapies. References: US Food and Drug Administration. Important safety alert regarding use of fecal microbiota for transplantation and risk of serious adverse reactions due to transmission of multidrug-resistant organisms. Rockville, MD: Food and Drug Administration, 2019. DeFilipp Z, Bloom PP, Torres Soto M, et al. Drug-resistant E. coli bacteremia transmitted by fecal microbiota transplant. N Engl J Med 2019;381:2043–2050.
Financial support: This study was funded by Day Zero Diagnostics.
Background: Prosthetic joint infections (PJIs) are costly and cause increased morbidity and mortality for patients. Staphylococcus epidermidis is a common cause of both early postoperative and late-presenting PJIs. Although S. epidermidis is a normal part of the human skin microflora, its ability to form biofilm on implanted medical devices make it an important causative pathogen of PJIs. We investigated genetic, epidemiologic, and environmental factors contributing to S. epidermidis PJIs by performing whole-genome sequencing and clinical epidemiologic investigation of isolates collected from infected patients between 2017 and 2020. Methods: Patients with S. epidermidis isolated from a prosthetic joint that was placed at our orthopedic specialty hospital were identified using the microbiology laboratory records and electronic medical records. Whole-genome sequencing and single-nucleotide polymorphism (SNP)–based clonality analyses were performed using the epiXact service at Day Zero Diagnostics. These analyses included species identification, in silico MLST typing, phylogenomic analysis, as well as genotypic assessment of the prevalence of specific antibiotic resistance genes, virulence genes, and other relevant genes. For clonal isolates, additional reviews of surgical history and clinical data were performed. Results: In total, 62 S. epidermidis joint isolates were identified from 46 patients. Among these isolates, 52 were of sufficient purity to be used for genomic analysis (Fig. 1). A number of genes appeared in every isolate including sepA, smr, cap, sesB, sesG, and embp. Also, 6 S. epidermidis samples had a discrepancy between phenotypic resistance to oxacillin and the presence of the mecA resistance gene. We also identified 6 distinct clusters of isolates, all of which had SNP distances <10 base pairs (Fig. 2). Each cluster consisted of 2–4 patients. Cluster isolates accounted for 29.8% of all S. epidermidis prosthetic joint isolates. Most clonal isolates occurred in patients who were heavily exposed to different healthcare settings. Further epidemiologic investigation showed that some of these clonal isolates had ties to aspirations or procedures, whereas no clear connection could be determined for others. Conclusions:S. epidermidis isolated from clinical prosthetic joint samples contains a high degree of genetic resistance, including a mismatch between presence of mecA and phenotypic oxacillin resistance and genetic propensity for chlorhexidine resistance. Mupirocin resistance was not observed. Of all isolates, 29.8% belonged to multiple clusters, confirming hospital spread of this commensal organism in some cases.
Background: Whole-genome sequencing (WGS) is well established as a high-resolution method for measuring bacterial relatedness to better understand infection transmission in cases of healthcare-associated infections (HAIs). However, sequencing is still rarely used in HAI investigations due to a lack of access to computational analysis platforms with actionable turnaround times. Single-nucleotide polymorphism (SNP) analysis is typically used to determine bacterial relatedness. However, SNP-based methods often require a suite of bioinformatics tools that can be difficult to use and interpret without the expertise of a trained computational biologist. These obstacles become more significant in the case of prospective, real-time surveillance of HAIs, which can require the analysis of a large number of isolates. To enable the use of WGS for proactive determination of infection outbreaks, a rapid, automated method that can scale to large data sets is needed. Methods: Here, we demonstrate the capabilities of ksim, a novel automated algorithm to determine the clonality of bacterial samples using WGS. ksim measures the number of shared kmers (genomic subsequences of length k) between bacterial samples to determine their relatedness. ksim also filters out accessory genomic regions, such as plasmids, that can confound genetic relatedness estimates. We benchmarked the accuracy and speed of ksim relative to an SNP-based pipeline on simulated data sets (with sequencing reads generated in silico) and on 9 clinical-cluster data sets (6 publicly available and 3 real-time data sets from Massachusetts General Hospital [MGH]). We also used ksim to determine the relatedness of >5,000 historical clinical bacterial isolates from MGH, collected between 2015 and 2019. Results: ksim first preprocesses raw sequencing data to generate a common data structure, after which it computes the genomic distance between bacterial samples in ∼0.2 seconds in simple cases and in ∼4 seconds in complex cases when accessory genome filtering is required. In simulations across 5 species, ksim determined clonality (defined as <40 SNPs) with high accuracy (sensitivity, 99.7% and specificity, 99.6%). ksim performance on 9 clinical HAI data sets demonstrated its sensitivity (99.4%) and specificity (90.8%) compared to an SNP-based pipeline. ksim efficiently analyzed >5,000 clinical samples from MGH and found previously unidentified transmission clusters. Conclusions:ksim shows promise for rapid clonality determination in HAI outbreaks and has the potential to scale to tens of thousands of samples. This method could enable infection control teams to use WGS for prospective outbreak detection via an automated computational pipeline without the need for specialized computational biology training.
Funding: Day Zero Diagnostics and the NIH provided Funding: for this study.
Disclosures: Mohamad Sater reports salary from Day Zero Diagnostics.
To describe an investigation into 5 clinical cases of carbapenem-resistant Acinetobacter baumannii (CRAB).
Epidemiological investigation supplemented by whole-genome sequencing (WGS) of clinical and environmental isolates.
A tertiary-care academic health center in Boston, Massachusetts.
Patients or participants:
Individuals identified with CRAB clinical infections.
A detailed review of patient demographic and clinical data was conducted. Clinical isolates underwent phenotypic antimicrobial susceptibility testing and WGS. Infection control practices were evaluated, and CRAB isolates obtained through environmental sampling were assessed by WGS. Genomic relatedness was measured by single-nucleotide polymorphism (SNP) analysis.
Four clinical cases spanning 4 months were linked to a single index case; isolates differed by 1–7 SNPs and belonged to a single cluster. The index patient and 3 case patients were admitted to the same room prior to their development of CRAB infection, and 2 case patients were admitted to the same room within 48 hours of admission. A fourth case patient was admitted to a different unit. Environmental sampling identified highly contaminated areas, and WGS of 5 environmental isolates revealed that they were highly related to the clinical cluster.
We report a cluster of highly resistant Acinetobacter baumannii that occurred in a burn ICU over 5 months and then spread to a separate ICU. Two case patients developed infections classified as community acquired under standard epidemiological definitions, but WGS revealed clonality, highlighting the risk of burn patients for early-onset nosocomial infections. An extensive investigation identified the role of environmental reservoirs.
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