Incorporating Whole-Genome Sequencing into Public Health Surveillance: Lessons from Prospective Sequencing of Salmonella Typhimurium in Australia

Abstract

In Australia, the incidence of Salmonella Typhimurium has increased dramatically over the past decade. Whole-genome sequencing (WGS) is transforming public health microbiology, but poses challenges for surveillance. To compare WGS-based approaches with conventional typing for Salmonella surveillance, we performed concurrent WGS and multilocus variable-number tandem-repeat analysis (MLVA) of Salmonella Typhimurium isolates from the Australian Capital Territory (ACT) for a period of 5 months. We exchanged data via a central shared virtual machine and performed comparative genomic analyses. Epidemiological evidence was integrated with WGS-derived data to identify related isolates and sources of infection, and we compared WGS data for surveillance with findings from MLVA typing. We found that WGS data combined with epidemiological data linked an additional 9% of isolates to at least one other isolate in the study in contrast to MLVA and epidemiological data, and 19% more isolates than epidemiological data alone. Analysis of risk factors showed that in one WGS-defined cluster, human cases had higher odds of purchasing a single egg brand. While WGS was more sensitive and specific than conventional typing methods, we identified barriers to uptake of genomic surveillance around complexity of reporting of WGS results, timeliness, acceptability, and stability. In conclusion, WGS offers higher resolution of Salmonella Typhimurium laboratory surveillance than existing methods and can provide further evidence on sources of infection in case and outbreak investigations for public health action. However, there are several challenges that need to be addressed for effective implementation of genomic surveillance in Australia.

Introduction

Nontyphoidal Salmonella (NTS) enterica causes significant morbidity and mortality in Australia (Kirk et al., 2014). Rates of NTS, particularly Salmonella Typhimurium, have been increasing and are higher in Australia than other developed nations, including the United States and the European Union (Ford et al., 2016). While NTS is transmitted through several routes, approximately three-quarters of salmonellosis cases are estimated to be transmitted via contaminated food (Vally et al., 2014). Most foodborne NTS outbreaks in Australia are due to Salmonella Typhimurium and have been associated with a variety of sources, although eggs and poultry are the most common (OzFoodNet Working Group, 2015). In particular, the number of Salmonella Typhimurium outbreaks associated with eggs in Australia has increased from 2001 to 2011 (Moffatt et al., 2016a).

Whole-genome sequencing (WGS) using high-throughput next-generation sequencing offers an alternative to traditional subtyping methods for NTS and other foodborne pathogens (Deng et al., 2016). WGS has been shown to be more discriminatory and sometimes less expensive than traditional typing methods (Deng et al., 2016). Internationally, there have been several examples of the application of WGS to investigate outbreaks and for routine surveillance typing of NTS (Byrne et al., 2014; den Bakker et al., 2014; Leekitcharoenphon et al., 2014; Angelo et al., 2015; Ashton et al., 2015, 2016; Inns et al., 2017). However, there is limited evidence on the effective and actionable use of WGS data within a public health unit. To examine the potential benefits of WGS within a public health unit, we conducted a prospective trial of sequencing all Salmonella Typhimurium isolates in the Australian Capital Territory (ACT) over a period of 5 months. We evaluated WGS performance for routine public health surveillance using previously published guidelines as described by German et al. (2001).

Methods

Study design

In line with routine surveillance procedures, isolates cultured from human fecal, urine, or blood samples of ACT residents were notified by the diagnostic laboratory to the public health unit and forwarded to either the Microbiological Diagnostic Unit Public Health Laboratory (MDU PHL) in Melbourne or the Institute for Clinical Pathology and Medical Research (ICPMR)—Pathology West in Sydney for serotyping, and multilocus variable-number tandem-repeat analysis (MLVA) if Salmonella Typhimurium. All isolates collected between January 1, 2016, and June 2, 2016, serotyped as Salmonella Typhimurium, were included in this study and were sequenced on a fortnightly basis either at MDU PHL or at ICPMR. The WGS inclusion criteria were expanded to include human isolates from two outbreaks in Queensland and food/environmental isolates from one outbreak in New South Wales where epidemiological trace-back indicated a common source to outbreaks in the ACT.

Ethics approval for this project was granted by the ACT Health Human Research Ethics Committee (16.215) and the Australian National University Human Research Ethics Committee (2016/528).

Phenotypic serotyping

The antigenic formulae of Salmonella isolates were determined using antisera that agglutinated with specific O (somatic) and H (flagellar) antigens and were classified into serovars in accordance with the White–Kauffmann–Le Minor scheme (Issenhuth-Jeanjean et al., 2014). Isolates serotyped as Salmonella Typhimurium were submitted for MLVA and WGS.

Multilocus variable-number tandem-repeat analysis

MDU PHL and ICPMR performed MLVA on all Salmonella Typhimurium isolates as previously described (Lindstedt et al., 2004).

Whole-genome sequencing

Study isolates at either MDU PHL or ICPMR were subcultured for purity on nutrient agar twice before genomic DNA extractions from single colonies were performed using a JANUS automated workstation (PerkinElmer) and an automated DNA extraction instrument, Chemagic Prepito-D^® (PerkinElmer) based on magnetic particle separation enabling isolation of high-quality DNA. Extracted genomic DNA was diluted and DNA libraries prepared using the Illumina Nextera XT kit according to the manufacturer's instructions (Illumina, Inc.). Following DNA Library preparation, quality control was performed using a PerkinElmer LabChip GX Touch bioanalyzer and Thermo Fisher Qubit fluorometer to accurately quantitate the amount of DNA in each indexed library and to determine the average size and DNA fragment distribution within each library. After normalization, indexed libraries were mixed in the calculated ratios and WGS performed using the Illumina NextSeq 500 platform with 2 × 150 bp paired-end chemistry.

Data sharing and bioinformatic analysis

MDU PHL and ICPMR exchanged data via a dedicated shared virtual machine running on the Australian National Research Cloud (National eResearch Collaboration Tools and Resources project or NECTAR). Each participating laboratory was provided with an account on the remote computer with a Secure Shell login. This allowed us to upload sequencing data files over an encrypted channel, as well as browse and download the files uploaded by others. No identifiable patient data were uploaded to the cloud. Following the study, the data were deleted, the virtual machine was shut down, and its resources returned to the NECTAR cloud.

We used the “Nullarbor” pipeline (https://github.com/tseemann/nullarbor) to de novo assemble and align short read data against the Salmonella Typhimurium LT2 reference genome (GenBank accession Nos. AE006468 and AE006471). Similarity between isolates was assessed by the identification and alignment of core genome single-nucleotide polymorphisms (SNPs). The core genome comprised 4248 genes that are present in all isolates following comparison of the genomes. Maximum likelihood phylogenetic trees were estimated with FastTree software using the general time reversibility model, and SNP distances were calculated with Snippy v3.1 (http://github.com/tseemann/snippy).

Public health follow-up

Where possible, we interviewed all notified cases of NTS using a standardized questionnaire to obtain information about potential food and environmental exposures in the 7 d before illness onset under The Public Health Act 1997 in ACT. If the case was identified as part of an outbreak, we used additional questions specific to the outbreak, or only interviewed with an outbreak-specific questionnaire.

Definitions

For the purposes of this study, a case was defined as an individual with Salmonella Typhimurium notified to the public health unit, with a specimen collection date between January 1 and June 2, 2016. Epidemiological evidence of a link was defined as in the 7 d before illness onset: having household contact with a known case; reporting eating at the same commercial food premises as another case; buying the same egg brand; swimming in the same pool or lake as another case; or reporting a similar environmental contact as another case (e.g., handled the same pet food, attended the same petting zoo). Isolation of Salmonella Typhimurium from food or the environment in a food premises linked to a known outbreak was considered an epidemiological link for nonhuman samples. Cases who reported travel outside of Australia or within Australia but outside the ACT in the 7 d before illness onset were classified as travelling overseas or interstate, respectively.

Isolates were defined as linked by MLVA if they had an identical MLVA profile, and linked by WGS if they were ≤8 SNPs from any other isolate in the cluster. This SNP cutoff was chosen from observed SNPs in known outbreaks in these data. This cutoff will need to be further evaluated for future analyses.

An outbreak was defined as two or more cases who consumed a common food, or food from a common commercial food premises, and epidemiological and/or microbiological evidence implicated the food or premises as the source of illness. A cluster was defined as fully supported if all cases in the cluster were linked by epidemiological evidence, and defined as partially supported if two or more, but not all cases in the cluster, were linked by epidemiological evidence.

Analysis

We conducted descriptive analysis on deidentified data of all NTS cases in the ACT during the study period to determine the proportion of NTS isolates cultured and forwarded to a reference laboratory, as well as the number of isolates sequenced and the number in an epidemiological, MLVA, or WGS-defined cluster.

We used a case/case analysis for one WGS-identified cluster, where cases were isolates in the cluster, and controls were randomly selected from both Salmonella Typhimurium cases outside the cluster and non-Typhimurium NTS cases during the study period. Questionnaire data were combined with cluster variables for MLVA and WGS and analyzed using Microsoft Excel 2013 and Stata SE 14. Univariable analysis with a Fisher's exact test was used to generate an odds ratio and p-value.

Evaluation of WGS for surveillance

We qualitatively evaluated the implementation of WGS as a surveillance system based on its performance, particularly the sensitivity and specificity, positive predictive value, simplicity, timeliness, acceptability, and stability (German et al., 2001).

Results

Of the 108 Salmonella Typhimurium isolates sequenced, there were 92 human isolates from 90 ACT residents, 13 human isolates from Queensland residents, 2 food isolates, and 1 environmental isolate from a food premises. Overall, MDU PHL sequenced 80% (86/108) and ICPMR sequenced 20% (22/108) of the Salmonella Typhimurium isolates. Where two people provided two stool specimens on different dates (10 and 1 d apart, respectively), there were no SNP differences found between sequential isolates.

WGS data identified 14 clusters of 2 or more isolates with ≤8 SNPs (median cluster size 3, range 2–32 isolates), while MLVA data only identified 11 clusters with the same MLVA profile (median cluster size 5, range 2–31 isolates). Epidemiological evidence fully supported 43% (6/14) and partially supported a further 43% (6/14) of the WGS clusters, compared to fully supporting 18% (2/11) and partially supporting a further 73% (8/11) of the MLVA clusters (Table 1). Overall, WGS with epidemiological data linked 9% (10/108) more isolates to at least one other isolate in the study than MLVA and epidemiological data, and 19% (21/108) more isolates than epidemiological data alone.

Table 1.

Number and Percent of Salmonella Typhimurium Isolates Linked to at Least One Other Isolate in the Study Through Epidemiological Data, Multilocus Variable-Number Tandem-Repeat Analysis, and Whole-Genome Sequencing Data, Australian Capital Territory, January to June 2016

	Isolates (n = 108)
Linked by	N	%
Epidemiological data alone	48	44
MLVA alone	89	82
WGS alone	86	80
MLVA and epidemiological data	59	55
WGS and epidemiological data	69	64

MLVA, multilocus variable-number tandem-repeat analysis; WGS, whole-genome sequencing.

Of isolates linked by WGS, 24% (21/86) did not report an illness onset within 2 weeks of another isolate in the cluster or outbreak occurrence. While some of the WGS clusters represented already recognized outbreaks (Ford et al., 2017) (Supplementary Data and Supplementary Figure 1; Supplementary Data are available online at www.liebertpub.com/fpd), exposure data in some of these WGS clusters were not directly compared before receiving the WGS data due to differences in MLVA profiles or time of illness onsets. Epidemiological data overlaid with WGS data showed that cases in WGS clusters, who had not previously been linked by epidemiological data, often reported buying the same egg brand (Fig. 1). A case/case analysis of one of these clusters showed that cases within the WGS cluster had a higher odds of reporting egg brand D compared with cases outside the cluster (odds ratio 45, 95% confidence interval 1.34–2507.21, p = 0.01).

FIG. 1.

Maximum likelihood core genome SNP phylogeny of Salmonella Typhimurium isolates with epidemiological data, ACT, January to June 2016. Clusters identified by WGS are highlighted in the tree. Figure created with Interactive Tree of Life (https://itol.embl.de). Note: Where there was epidemiological or environmental evidence that an outbreak was associated with eggs, all cases in the outbreak were assigned that egg brand. Outbreak-associated case isolates were isolates epidemiologically linked with a known outbreak. ACT, Australian Capital Territory; SNP, single-nucleotide polymorphism; WGS, whole-genome sequencing. Color images available online at www.liebertpub.com/fpd

Evaluation

Sensitivity and specificity

While culture-independent diagnostic testing has increased recently (Iwamoto et al., 2015), pathology providers in the ACT still culture most stool specimens from patients with diarrhea for NTS (R Hundy, personal communication, 2017). During the study period, 7% of NTS notifications were derived from nucleic acid testing only with no isolates available, and 2% of notifications were cultured but isolates were not forwarded to the reference laboratory for further characterization.

WGS data were sensitive to detect outbreaks, identifying more genetically linked cases than epidemiological data and MLVA alone. WGS data were also specific by showing genetic differences within an MLVA profile and thus excluding some cases from an MLVA-defined cluster.

Predictive value positive

All isolates (100%) linked by both epidemiological data and MLVA were also linked by WGS, including cases in different states and territories. WGS data showed close genetic relatedness between sporadic cases (some occurring months after an outbreak) to point-source outbreaks or to other isolates of suspected sporadic infection.

Simplicity and timeliness

Reference laboratories perform and report NTS typing results independently of each other; however, in this study, sequencing data needed to be shared for a combined analysis and report for effective surveillance of all Salmonella Typhimurium in the ACT. A virtual machine was established to share data between two laboratories and bioinformatic expertise was required for the analysis and report of WGS data. Public health unit staff needed training in WGS data interpretation. With current systems, the WGS analysis was more time-consuming than the analysis of MLVA profiles, even for a relatively small number of isolates.

As seen for Listeria monocytogenes in the United States (Jackson et al., 2016), due to the highly discriminatory power of WGS, less data (fewer isolates) should be required to identify an outbreak, facilitating timely intervention. In this study, traditional typing methods were performed and their results reported before sequencing, and WGS data were not available early enough to be used to first identify an outbreak.

Acceptability and stability

The largest barriers to acceptability from the public health unit were the capacity to understand and use the data, and the cost of sequencing. While the costs of WGS have been decreasing and are likely to continue to decrease (Wang et al., 2015; Deng et al., 2016), at the time of this study, the cost of WGS to the public health unit was higher than the combined cost of serotyping and MLVA. Using WGS for surveillance relies on the expertise and communication among microbiologists, bioinformaticians, and epidemiologists, and is not flexible to the loss of personnel who can sequence and interpret the data.

Discussion

Our findings demonstrated that even with a relatively small number of culture-confirmed cases of Salmonella Typhimurium gastroenteritis, WGS of Salmonella Typhimurium was more discriminatory than MLVA. WGS with epidemiological data was both sensitive (linking 9% of cases that traditional typing had not linked) and specific (differentiating isolates with the same MLVA profile that were not part of an outbreak). WGS improved source attribution of Salmonella Typhimurium cases by linking food and environmental isolates in an outbreak, and by identifying clusters with higher precision for risk factor analysis—in this study, identifying egg brands for further investigation. This high discrimination is invaluable at the public health unit level to better targeted epidemiological investigations.

Our results are consistent with reports from Denmark and the United States, which identified benefits of WGS for Salmonella Enteritidis compared to pulsed-field gel electrophoresis (den Bakker et al., 2014; Leekitcharoenphon et al.,2014). Several other recent studies have used WGS to help retrospectively investigate NTS outbreaks in the United States, United Kingdom, and Europe (Byrne et al., 2014; Ashton et al., 2015; Inns et al., 2015; Taylor et al., 2015; Hoffmann et al., 2016). Characterizing NTS by WGS is becoming more common, with Public Health England now routinely using WGS for prospective characterization of all NTS (Ashton et al., 2016). The improved sensitivity and specificity of WGS compared to phage typing likely increased the speed at which a multicountry outbreak of NTS in the United Kingdom and Spain in 2015 was recognized (Inns et al., 2017).

Several studies have suggested that WGS can also be used to genetically link NTS isolates from food or environmental sources to isolates from human cases, which can help to rapidly identify the source of an outbreak (Yokoyama et al., 2014; Ashton et al., 2015; Inns et al., 2015). However, for this to occur, NTS isolates from food and environmental sources need to be sequenced, as, for example, has been done with the U.S. Food and Drug Administration's GenomeTrakr (Allard et al., 2016). A database of NTS sequences from local sources might have assisted this ACT project to identify sources of infection. Salmonella Typhimurium was only isolated from food or the environment in one outbreak, with the isolates being highly related to those of the human cases from that outbreak. While risk factor information from cases provided some evidence of food or environmental sources of infection, this evidence would be strengthened if isolates from these sources could be linked through WGS.

We identified a number of implementation challenges of WGS surveillance within the public health unit. While our use of SNP differences to assess relatedness between cases provided high discrimination, they may be dependent on the selection of reference genomes, can change as the number of isolates in the analysis increases, and require a national database for data sharing and analysis. This highlighted the need for a new system to integrate WGS results into current systems, as WGS data and epidemiological data needed to be manually overlaid for analysis. Unlike a Campylobacter outbreak in the ACT, where there were large SNP distances between epidemiologically linked cases (Moffatt et al., 2016b), we did not find any cases where epidemiological evidence linked them, but WGS data did not. However, WGS data suggested links between cases where there was no supporting epidemiological evidence. It is unclear how much evidence is needed to trigger further investigation or how resource intensive that investigation should be. Finally, routine WGS for Salmonella Typhimurium surveillance remains costly, and the current turn-around-time of WGS exceeds that of traditional methods. Using WGS as a surveillance system is unstable without sustainable funding for the costs of WGS. Investment into continued training and infrastructure to share data nationally and combine epidemiological data with WGS data is needed. While WGS is highly discriminatory, if the data are not received in a timely manner, this limits the utility for public health surveillance. We were unable to measure timeliness of WGS in this study as traditional typing methods were performed before sequencing began.

Our study had a relatively small number of isolates and small cluster sizes, which limited our power to detect significant associations between risk factors and illness within clusters. Despite this, our study showed the potential of WGS to link “sporadic” cases, which could be increased if other Australian states and territories contributed data.

In conclusion, WGS of Salmonella Typhimurium presents a more discriminatory approach to laboratory surveillance than MLVA and has the potential to identify outbreaks and sources of infection investigated by a small public health unit. If sequenced and reported in a timely manner, this could increase the speed of preventative action and reduce the number of illnesses. Several challenges need to be addressed before WGS can be used routinely in a public health unit, including reporting, triggers for investigation, and sustainable funding and resources. This study facilitated close collaboration among epidemiologists, microbiologists, and bioinformaticians, a key requirement for an effective transition to WGS for NTS.

Footnotes

Acknowledgments

The authors thank ACT Health and OzFoodNet. They also thank the laboratories that performed the serotyping, MLVA, and WGS, including the MDU PHL, the ICPMR, and Queensland Health Forensic and Scientific Services (FSS). Doherty Applied Microbial Genomics is funded by the Department of Microbiology and Immunology at The University of Melbourne. The National Health and Medical Research Council, Australia, funded a Practitioner Fellowship GNT1105905 to B.P.H. and Project Grant GNT1129770 to B.P.H., D.A.W, and M.D.K. Finally, they thank Milica Stefanovic, Russel Stafford, Kirsty Hope, Craig Shadbolt, and John Bates for their contribution to this project. This research is supported by an Australian Government Research Training Program (RTP) Scholarship.

Disclosure Statement

No competing financial interests exist.

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