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Whole-genome sequencing for neonatal intensive care unit outbreak investigations: Insights and lessons learned

Published online by Cambridge University Press:  24 June 2021

Sarah E. Sansom*
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
Latania K. Logan
Affiliation:
Division of Infectious Diseases, Department of Pediatrics, Rush University Medical Center, Chicago, Illinois
Stefan J. Green
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
Nicholas M. Moore
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
Mary K. Hayden
Affiliation:
Division of Infectious Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
*
Author for correspondence: Sarah Sansom, DO, Division of Infectious Diseases, Rush University Medical Center, 600 S Paulina St, Ste 143, Chicago, IL 60612. E-mail: sarah_e_sansom@rush.edu

Abstract

Infectious diseases outbreaks are a cause of significant morbidity and mortality among hospitalized patients. Infants admitted to the neonatal intensive care unit (NICU) are particularly vulnerable to infectious complications during hospitalization. Thus, rapid recognition of and response to outbreaks in the NICU is essential. At Rush University Medical Center, whole-genome sequencing (WGS) has been utilized since early 2016 as an adjunctive method for outbreak investigations. The use of WGS and potential lessons learned are illustrated for 3 different NICU outbreak investigations involving methicillin-resistant Staphylococcus aureus (MRSA), group B Streptococcus (GBS), and Serratia marcescens. WGS has contributed to the understanding of the epidemiology of outbreaks in our NICU, and it has also provided further insight in settings of unusual diseases or when lower-resolution typing methods have been inadequate. WGS has emerged as the new gold standard for evaluating strain relatedness. As barriers to implementation are overcome, WGS has the potential to transform outbreak investigation in healthcare settings.

Information

Type
Original Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America
Figure 0

Table 1. Characteristics of Infants with MRSA Isolates

Figure 1

Fig. 1. MRSA outbreak investigation. (A) PFGE of MRSA isolates from healthcare workers and infants. Total genomic DNA of MRSA isolates was digested using SmaI restriction enzyme and resolved in 1% agarose in 0.5× TBE buffer (pulse time, 1–50 seconds; run time, 21 hours). Selected infant isolates (B1, B2, and B4) showed similar banding patterns to HCP isolates (H1 and H2) (all USA 300). Molecular weight marker (M) is shown in the first column. (B) Neighbor-joining phylogenetic tree based on whole-genome sequencing analysis of MRSA isolates from the NICU outbreak. Genomic sequence data were processed using the software package SPANDx37 to generate a variant matrix. This matrix was used to create a bootstrapped phylogenetic tree within the software package MEGA.38 The scale bar represents genetic distances based on number of nucleotide differences, and the numbers at nodes represent bootstrap support (1,000 iterations; only nodes with >70% support are indicated). Isolates were compared to a reference MRSA USA 300 genome (GenBank accession no. NC 010079). Isolates collected from the infants (“B” isolates) were highly similar; at most, 5 SNVs were detected between infant isolates. HCP isolates (H1 and H2) were highly divergent from the infant isolates, and unrelated to each other. One HCP isolate (H3) shared a common ancestor more recently with the infant isolates than the other HCP isolates but differed from the infant isolates by >100 nucleotides, indicating that H3 was not the source of the outbreak. Note. HCP, healthcare personnel; MRSA, methicillin-resistant Staphylococcus aureus; SNV, single nucleotide variant.

Figure 2

Table 2. Characteristics of Infants with GBS Isolates

Figure 3

Fig. 2. Epidemiologic map of 2 patients with late-onset disease group B Streptococcus (GBS). The infants were located in separate NICU pods at the time of positive clinical cultures, but previously shared a 2-day overlap period in the same NICU pod. WGS showed that the isolates were highly similar. In total, results of the outbreak investigation suggested horizontal transmission.

Figure 4

Table 3. Characteristics of Infants With Serratia marcescens Isolates

Figure 5

Fig. 3. Serratia marcescens outbreak investigation. (A) Epidemiologic map of 7 patients with S. marcescens. Clinical and screening isolates are shown. Most of the patients were housed within close spatial proximity to each other in the NICU. Infants B1, B5, B6, and B7 were located in adjacent rooms in pod II. Infants B2, B3, and B4 were located in adjacent rooms in pod I. (B) Neighbor-joining phylogenetic tree based on whole-genome sequencing analysis of Serratia marcescens isolates from the NICU outbreak. The phylogenetic tree was created as described previously. The scale bar represents genetic distances based on number of nucleotide differences, and the numbers at nodes represent bootstrap support (1,000 iterations; only nodes with >70% support are indicated). Isolates were compared to a reference S. marcescens genome (GenBank accession no. CP018917). Two separate clusters of 3 organisms each were identified, with the seventh organism not closely related to either group (B7). The genomes of the organisms within each cluster were identical or nearly identical. The genomes of isolates of cluster 1 (comprised of isolates B2, B3, and B4) were identical without detectable nucleotide differences. The genomes of isolates of cluster 2 (comprised of B1, B5, and B6) had at most 1 nucleotide mismatch between isolates. The clusters were highly distinct from one another, with tens of thousands of nucleotide differences.