Detecting, mapping, and suppressing the spread of a decade-long Pseudomonas aeruginosa nosocomial outbreak with genomics

  1. William Stribling
  2. Lindsey R. Hall
  3. Aubrey Powell
  4. Casey Harless
  5. Melissa J. Martin
  6. Brendan W. Corey
  7. Erik Snesrud
  8. Ana Ong
  9. Rosslyn Maybank
  10. Jason Stam
  11. Katie Bartlett
  12. Brendan T. Jones
  13. Lan N. Preston
  14. Katherine F. Lane
  15. Bernadette Thompson
  16. Lynn M. Young
  17. Yoon I. Kwak
  18. Alice E. Barsoumian
  19. Ana-Elizabeth Markelz
  20. John L. Kiley
  21. Robert J. Cybulski
  22. Jason W. Bennett
  23. Patrick T. Mc Gann
  24. Francois Lebreton

  • Curated by eLife

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    eLife assessment

    This important work presents an example of how genomic data can be used to improve understanding of an ongoing, long-term bacterial outbreak in a hospital with an application to multi-drug resistant Pseudomonas aeruginosa, and will be of interest to researchers concerned with the spread of drug-resistant bacteria in hospital settings. The convincing genomic analyses highlight the value of routine surveillance of patients and environmental sampling and show how such data can help in dating the origin of the outbreak and in characterising the epidemic lineages. These findings highlight the importance of understanding environmental factors contributing to the transmission of P. aeruginosa for guiding and tailoring infection control efforts; however, epidemiological information was limited and the sampling methodology was inconsistent, complicating interpretation of inferences about exact transmission routes.

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Abstract

Whole-genome sequencing is revolutionizing bacterial outbreak investigation but its application to the clinic remains limited. In 2020, prospective and retrospective surveillance detected a Pseudomonas aeruginosa outbreak with 254 isolates collected from 82 patients in 27 wards of a hospital. Its origin was dated to the late 90s, just after the facility opened, and patient-to-patient and environment-to-patient cases of transmission were inferred. Over time, two epidemic subclones evolved in separate hosts and hospital areas, including newly opened wards, and hospital-wide sampling confirmed reservoirs persisted in the plumbing. Pathoadaptive mutations in genes associated with virulence, cell wall biogenesis, and antibiotic resistance were identified. While the latter correlated with the acquisition of phenotypic resistances to 1st (cephalosporin), 2nd (carbapenems) and 3rd (colistin) lines of treatment, maximum parsimony suggested that a truncation in a lipopolysaccharide component coincided with the emergence of a subclone prevalent in chronic infections. Since initial identification, extensive infection control efforts guided by routine, near real-time surveillance have proved successful at slowing transmission. Every year, millions of hospital-associated infections are threatening patient lives. This, in a world in which rates of resistances to existing antibiotics are increasing. And this, at a time dubbed the post-antibiotic era when new drugs are scarce. But now is also the golden age of genomics. Here, applying this transformative technology to the clinic revealed an outbreak of Pseudomonas aeruginosa , resistant to last line antibiotics, that had escaped detection for decades. The mapping of transmission chains, through hospital floors, pointed to environmental reservoirs in intensive care units but also provided critical insights into the evolution and adaptation of this pathogen. Genomic data, shared in near real-time with the hospital, resulted in targeted interventions and the prevention of new cases.

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  1. Version published to 10.7554/elife.93181.1 on eLife
  2. Version published to 10.7554/elife.93181 on eLife
  3. eLife

    eLife assessment

    This important work presents an example of how genomic data can be used to improve understanding of an ongoing, long-term bacterial outbreak in a hospital with an application to multi-drug resistant Pseudomonas aeruginosa, and will be of interest to researchers concerned with the spread of drug-resistant bacteria in hospital settings. The convincing genomic analyses highlight the value of routine surveillance of patients and environmental sampling and show how such data can help in dating the origin of the outbreak and in characterising the epidemic lineages. These findings highlight the importance of understanding environmental factors contributing to the transmission of P. aeruginosa for guiding and tailoring infection control efforts; however, epidemiological information was limited and the sampling methodology was inconsistent, complicating interpretation of inferences about exact transmission routes.

  4. eLife

    Reviewer #1 (Public Review):

    Summary:
    This is a manuscript describing outbreaks of Pseudomonas aeruginosa ST 621 in a facility in the US using genomic data. The authors identified and analysed 254 P. aeruginosa ST 621 isolates collected from a facility from 2011 to 2020. The authors described the relatedness of the isolates across different locations, specimen types (sources), and sampling years. Two concurrently emerged subclones were identified from the 254 isolates. The authors predicted that the most recent common ancestor for the isolates can be dated back to approximately 1999 after the opening of the main building of the facility in 1996. Then the authors grouped the 254 isolates into two categories: 1) patient-to-patient; or 2) environment-to-patient using SNP thresholds and known epidemiological links. Finally, the authors described the changes in resistance gene profiles, virulence genes, cell wall biogenesis, and signaling pathway genes of the isolates over the sampling years.

    Strengths:
    The major strength of this study is the utilisation of genomic data to comprehensively describe the characteristics of a long-term Pseudomonas aeruginosa ST 621 outbreak in a facility. This fills the data gap of a clone that could be clinically important but easily missed from microbiology data alone.

    Weaknesses:
    The work would further benefit from a more detailed discussion on the limitations due to the lack of data on patient clinical information, ward movement, and swabs collected from healthcare workers to verify the transmission of Pseudomonas aeruginosa ST 621, including potential healthcare worker to patient transmission, patient-to-patient transmission, patient-to-environment transmission, and environment-to-patient transmission. For instance, the definition given in the manuscript for patient-to-patient transmission could not rule out the possibility of the existence of a shared contaminated environment. Equally, as patients were not routinely swabbed, unobserved carriers of Pseudomonas aeruginosa ST 621 could not be identified and the possibility of misclassifying the environment-to-patient transmissions could not be ruled out. Moreover, reporting of changes in rates of resistance to imipenem and cefepime could be improved by showing the exact p-values (perhaps with three decimal places) rather than dichotomising the value at 0.05. By doing so, readers could interpret the strength of the evidence of changes.

    Impact of the work:
    First, the work adds to the growing evidence implicating sinks as long-term reservoirs for important MDR pathogens, with direct infection control implications. Moreover, the work could potentially motivate investments in generating and integrating genomic data into routine surveillance. The comprehensive descriptions of the Pseudomonas aeruginosa ST 621 clones outbreak is a great example to demonstrate how genomic data can provide additional information about long-term outbreaks that otherwise could not be detected using microbiology data alone. Moreover, identifying the changes in resistance genes and virulence genes over time would not be possible without genomic data. Finally, this work provided additional evidence for the existence of long-term persistence of Pseudomonas aeruginosa ST 621 clones, which likely occur in other similar settings.

  5. eLife

    Reviewer #2 (Public Review):

    Summary:
    The authors present a report of a large Pseudomonas aeruginosa hospital outbreak affecting more than 80 patients with first sampling dates in 2011 that stretched over more than 10 years and was only identified through genomic surveillance in 2020. The outbreak strain was assigned to the sequence type 621, an ST that has been associated with carpabapenem resistance across the globe. Ongoing transmission coincided with both increasing resistance without acquisition of carbapenemase genes as well as the convergence of mutations towards a host-adapted lifestyle.

    Strengths:
    The convincing genomic analyses indicate spread throughout the hospital since the beginning of the century and provide important benchmark findings for future comparison.

    The sampling was based on all organisms sent to the Multidrug-resistant Organism Repository and Surveillance Network across the U.S. Military Health System.

    Using sequencing data from patient and environmental samples for phylogenetic and transmission analyses as well as determining recurring mutations in outbreak isolates allows for insights into the evolution of potentially harmful pathogens with the ultimate aim of reducing their spread in hospitals.

    Weaknesses:
    The epidemiological information was limited and the sampling methodology was inconsistent, thus complicating the inference of exact transmission routes. Epidemiological data relevant to this analysis include information on the reason for sampling, patient admission and discharge data, and underlying frequency of sampling and sampling results in relation to patient turnover.

  6. eLife

    Reviewer #3 (Public Review):

    Summary:
    This paper by Stribling and colleagues sheds light on a decade-long P. aeruginosa outbreak of the high-risk lineage ST-621 in a US Military hospital. The origins of the outbreak date back to the late 90s and it was mainly caused by two distinct subclones SC1 and SC2. The data of this outbreak showed the emergence of antibiotic resistance to cephalosporin, carbapenems, and colistin over time highlighting the emerging risk of extensively resistant infections due to P. aeruginosa and the need for ongoing surveillance.

    Strengths:
    This study overall is well constructed and clearly written. Since detailed information on floor plans of the building and transfers between facilities was available, the authors were able to show that these two subclones emerged in two separate buildings of the hospital. The authors support their conclusions with prospective environmental sampling in 2021 and 2022 and link the role of persistent environmental contamination to sustaining nosocomial transmission. Information on resistance genes in repeat isolates for the same patients allowed the authors to detect the emergence of resistance within patients. The conclusions have broader implications for infection control at other facilities. In particular, the paper highlights the value of real-time surveillance and environmental sampling in slowing nosocomial transmission of P. aeruginosa.

    Weaknesses:
    My major concern is that the authors used fixed thresholds and definitions to classify the origin of an infection. As such, they were not able to give uncertainty measures around transmission routes nor quantify the relative contribution of persistent environmental contamination vs patient-to-patient transmission. The latter would allow the authors to quantify the impact of certain interventions. In addition, these results represent a specific US military facility and the transmission patterns might be specific to that facility. The study also lacked any data on antibiotic use that could have been used to relate to and discuss the temporal trends of antimicrobial resistance.

  7. Version published to 10.1101/2023.07.24.550326v2 on bioRxiv
  8. Version published to 10.1101/2023.07.24.550326v1 on bioRxiv