Rapid Whole-Genome Sequencing for Surveillance of Salmonella enterica Serovar Enteritidis

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Henk C. den Bakker, Marc W. Allard, Dianna Bopp, Eric W. Brown, John Fontana, Zamin Iqbal, Aristea Kinney, Ronald Limberger, Kimberlee A. Musser, Matthew Shudt, Errol Strain, Martin Wiedmann, and William J. Wolfgang

Author affiliations: Cornell University, Ithaca, New York, USA (H. den Bakker, M. Wiedmann); US Food and Drug Administration, College Park, Maryland, USA (M.W. Allard, E.W. Brown, E. Strain); New York State Department of Health/Wadsworth Center, Albany, New York, USA (D. Bopp, R. Limberger, M. Shudt, K.A. Musser, W.J. Wolfgang); Connecticut Department of Public Health, Rocky Hill, Connecticut, USA (J. Fontana, A. Kinney); Wellcome Trust Centre for Human Genetics, Oxford, UK (Z. Iqbal)

Abstract

For Salmonella enterica serovar Enteritidis, 85% of isolates can be classified into 5 pulsed-field gel electrophoresis (PFGE) types. However, PFGE has limited discriminatory power for outbreak detection. Although whole-genome sequencing has been found to improve discrimination of outbreak clusters, whether this procedure can be used in real-time in a public health laboratory is not known. Therefore, we conducted a retrospective and prospective analysis. The retrospective study investigated isolates from 1 confirmed outbreak. Additional cases could be attributed to the outbreak strain on the basis of whole-genome data. The prospective study included 58 isolates obtained in 2012, including isolates from 1 epidemiologically defined outbreak. Whole-genome sequencing identified additional isolates that could be attributed to the outbreak, but which differed from the outbreak-associated PFGE type. Additional putative outbreak clusters were detected in the retrospective and prospective analyses. This study demonstrates the practicality of implementing this approach for outbreak surveillance in a state public health laboratory.

For genetically monomorphic bacteria, current typing methods often prove inadequate for outbreak detection, trace back, and identification of transmission routes. Some of these bacteria, such as Salmonella entericaserovar Enteritidis, S. enterica serovar Montevideo, Staphylococcus aureus, Clostridium difficile, Klebsiella pneumoniae, and Mycobacterium tuberculosis, cause diseases that have major public health effects. For these pathogens, retrospective studies have unambiguously demonstrated that phylogenetic analysis based on whole-genome–derived single nucleotide polymorphisms (SNPs) improves cluster resolution and would be an invaluable tool in epidemiologic investigations (1–8). We refer to this approach as whole-genome cluster analysis.

Introduction of small, affordable, and rapid benchtop whole-genome sequencers, such as the Illumina MiSeq (Illumina, San Diego, CA, USA) MiSeq and the Ion Torrent PGM (Life Technologies, Carlsbad, CA, USA), has made it possible for clinical and public health laboratories to contemplate adding genome sequencing as a rapid typing tool. Eyre et al. (9) showed the utility of Illumina MiSeq in the detection of nosocomial outbreaks of S. aureus and C.difficile infections. Although Eyre et al. (9) showed the utility of this approach in improving typing of these monomorphic pathogens, the utility of these sequencers in a larger public health setting, in which capacity and turn-around times are critical parameters, has not been demonstrated.

The standard typing method for Salmonella species, which is used by PulseNet laboratories, is pulsed-field gel electrophoresis (PFGE) (10). However, PFGE has limited discriminatory power for S. enterica serovar Enteritidis strains and clusters. At the New York State Department of Health (NYSDOH) Wadsworth Laboratories (Albany, NY, USA), ≈50% of the 350–500 S. enterica serovar Enteritidis isolates received each year are PFGE type JEGX01.0004. Multilocus variable-number tandem-repeat analysis (MLVA) of these isolates improves discrimination of disease clusters for this pathogen, but even this tool assigns 30% of isolates to a single MLVA type. Because genomic homogeneity of S. enterica serovar Enteritidis is also observed on a national and international level (11), a clear need exists for improved typing methods for S. enterica serovar Enteritidis in the public health laboratory.

To determine if whole-genome cluster analysis can improve subtype discrimination and cluster detection in the public health laboratory, we sequenced 93 S. enterica serovar Enteritidis isolates received during routine surveillance activities at the NYSDOH by using the Ion Torrent PGM located in the core sequencing facility. The sequence data were used to create SNP-based phylogenetic trees. This study consisted of 2 parts. First, we conducted a retrospective analysis that focused on an epidemiologically defined outbreak of S. enterica serovar Enteritidis JEGX01.0004 within a long-term care facility (LTCF). Second, we conducted a prospective study in which nearly all S. enterica serovar Enteritidis PFGE patterns JEGX01.0004 and JEGX01.0021 were sequenced during a 4-month period during the summer of 2012. In addition, we retrospectively sequenced JEGX01.0009 S. entericaserovar Enteritidis isolates that had been associated with contaminated ground beef early in the summer of 2012.

The retrospective part of the study serves as a proof of principle and clearly demonstrates increased resolution of whole-genome cluster analysis for typing of common PFGE pattern types and subsequent outbreak detection. In the prospective study, we show the feasibility of detecting outbreaks in near real time, as well as improved resolution, of the method that enables detection of numerous potential outbreak clusters that would likely go undetected by PFGE.