Skip to main content
PMC home page
PLOS ONE logoLink to PLOS ONE
. 2018 Feb 5;13(2):e0192233. doi: 10.1371/journal.pone.0192233

Impact of the choice of reference genome on the ability of the core genome SNV methodology to distinguish strains of Salmonella enterica serovar Heidelberg

Valentine Usongo 1,2, Chrystal Berry 3, Khadidja Yousfi 1, Florence Doualla-Bell 1, Genevieve Labbé 4, Roger Johnson 4, Eric Fournier 1, Celine Nadon 3, Lawrence Goodridge 2, Sadjia Bekal 1,5,*
Editor: Joël Mossong6
PMCID: PMC5798827  PMID: 29401524

Abstract

Salmonella enterica serovar Heidelberg (S. Heidelberg) is one of the top serovars causing human salmonellosis. The core genome single nucleotide variant pipeline (cgSNV) is one of several whole genome based sequence typing methods used for the laboratory investigation of foodborne pathogens. SNV detection using this method requires a reference genome. The purpose of this study was to investigate the impact of the choice of the reference genome on the cgSNV-informed phylogenetic clustering and inferred isolate relationships. We found that using a draft or closed genome of S. Heidelberg as reference did not impact the ability of the cgSNV methodology to differentiate among 145 S. Heidelberg isolates involved in foodborne outbreaks. We also found that using a distantly related genome such as S. Dublin as choice of reference led to a loss in resolution since some sporadic isolates were found to cluster together with outbreak isolates. In addition, the genetic distances between outbreak isolates as well as between outbreak and sporadic isolates were overall reduced when S. Dublin was used as the reference genome as opposed to S. Heidelberg.

Introduction

Nontyphoidal Salmonella (NTS) enterica serovars are the most important causes of bacterial gastroenteritis. Among the NTS serovars, Heidelberg is ranked as the second and third most frequent serovar recovered from clinical cases in Québec and Canada respectively [1]. In Québec between 2004 and 2014, 23% of S. Heidelberg clinical isolates were from blood specimens, compared to 7% for S. enterica serovar Enteritidis and 5% for S. enterica serovar Typhimurium, suggesting an increased capacity of this serovar to cause invasive systemic disease [2].

Pulsefield gel electrophoresis (PFGE) has been the gold standard method used by PulseNet Canada (PNC) since the 1990s for the molecular typing of Salmonella during outbreak investigations. However, a major drawback with the use of PFGE in outbreak investigation is the low resolution power of this technique that is further exacerbated when applied to S. Heidelberg typing owing to the extremely low genetic diversity of this serovar. For example, 70% of S. Heidelberg isolated in Québec belonged to pulsovar 2 [2].

Whole genome sequence (WGS) based methods owing to their growing availability and high genomic resolution are rapidly replacing traditional typing methods such as PFGE within major public health laboratories including PNC [3]. Two popular methods that are increasingly applied in the field of bacterial genomic epidemiology are: the gene-by-gene methods which is basically an extension of the 7 gene MLST typing technique to encompass the entire genome (whole genome MLST, wgMLST) or just the core genome (core genome MLST, cgMLST) [4, 5] and the single nucleotide variant (SNV) methods which identifies single nucleotide variants by comparing a population of target genomes against a reference [6, 7]. We recently found that the cgSNV method provided superior discriminatory power than traditional methods during outbreak investigations involving Salmonella Heidelberg [2].

The choice of the reference genome has been previously proposed as a potential consideration affecting core genome SNV (cgSNV)–based analysis and outcomes. For example, choosing a distantly related strain as a source of reference may tend to cluster isolates that are otherwise genetically distant. Another concern with the choice of reference genome is the sequencing status of the reference genome. It is generally perceived that high-quality complete genomes are preferred to ensure accurate and epidemiologically concordant phylogenetic analysis and outbreak investigation. These concerns were not addressed in our previous work on the cgSNV method [2]. Here using draft de novo assembled and completely sequenced or closed genomes of S. Heidelberg as well as a distantly related genome such as S. Dublin as references, we assessed the ability of the cgSNV methodology to differentiate amongst 145 S. Heidelberg strains involved in four distinct outbreaks and sporadic cases of salmonellosis in Québec.

Materials and method

Collection and characterization of bacterial isolates

The 145 S. Heidelberg clinical isolates described in this study were collected as part of the Quebéc surveillance program on human salmonellosis established since 2003 to ensure rapid detection of outbreaks. The food isolates were collected by the Ministère de l'Agriculture, des Pêcheries et de l'Alimentation du Québec (MAPAQ) during routine food-poisoning investigations. Isolates were grown on triple sugar iron agar at 37°C and stored at -80°C in trypticase soy spiked with 10% glycerol. PFGE and serotyping was performed at the Laboratoire de Santé Publique du Québec (LSPQ) following PNC guidelines.

Whole genome sequencing

Frozen bacterial isolates were cultured overnight at 37°C in brain heart infusion broth and genomic DNA was extracted using the Metagenomic DNA isolation Kit for Water (Epicentre, Madison, WI). Samples were prepared using Nextera XT chemistry (Illumina, Inc., San Diego, CA) and were sequenced using Illumina Miseq paired-end read technology using 300 base read lengths. Five strains were selected from the outbreak isolates to serve as references and their reads were de novo assembled using SPAdes v. 3.9 [8]. The complete genome counterparts for these strains have been reported in a previous work [9]. Two draft and three complete, unrelated reference genomes were also downloaded from NCBI and included in this analysis making a total of 15 assessed reference genomes (Table 1).

Table 1. General features of the strains used as reference genomes for the cgSNV analysis of 145 S. Heidelberg isolates.

Strain ID Genome Status NCBI Accession No Source Serovar Genome used as reference in Tree number
ID117795 Draft NA Human Heidelberg 1
ID117795 Completely sequenced CP016507 Human Heidelberg 2
ID128787 Draft NA Human Heidelberg 3
ID128787 Completely sequenced CP016586 Human Heidelberg 4
ID128902 Draft NA Human Heidelberg 5
ID128902 Completely sequenced CP016579 Human Heidelberg 6
ID134609 Draft NA Human Heidelberg 7
ID134609 Completely sequenced CP016581 Human Heidelberg 8
ID135140 Draft NA Food Heidelberg 9
ID135140 Completely sequenced CP016510 Food Heidelberg 10
SL486 Draft NZ_ABEL00000000 Human Heidelberg 11
CFSAN024776 Draft NC_JWQE00000000 Human Heidelberg 12
SL476 Completely sequenced NC_011083 Ground turkey Heidelberg 13
B182 Completely sequenced CP003416 Bovine feces Heidelberg 14
SL477 Completely sequenced CP001144 Human Dublin NA
Open in a new tab

NA, Not available.

CgSNV typing

CgSNV analysis was performed using the SNVPhyl pipeline [10] v.1.0 integrated within the NML instance of the Galaxy platform [11]. Briefly, paired-end sequence reads from the 145 isolates were aligned against each of the 15 reference genomes using SMALT v.0.7.5 (http://www.sanger.ac.uk/science/tools/smalt-0). MUMmer v.3.23 [12] and PHAST [13] were used to identify repeat and prophage regions in each reference genome respectively and these regions were excluded from the analysis. Variants were called using two independent variant calling algorithms: FreeBayes v.0.9.20 and SAMtools [14] /BCFtools based on predefined criteria described elsewhere [10]. To infer the relationship between these isolates, minimum spanning trees were constructed from the SNVphyl output data using the geoBURST algorithm built into PHYLOViZ v2.0 [15].

Topological similarity

We assessed the topological similarity of the phylogenetic trees using the Robinson and Foulds (RF) test [16]. This test is a widely used tree metric for tree-to-tree distances and is defined as the minimum number of operations needed to transform one tree into the other. Briefly, newick tree files from the SNVphyl pipeline generated using each of the 14 genomes as references were concatenated and the resulting file was submitted to the online phylogenetic tool T-REX [17] to compute the topological distances between the trees.

Nucleotide sequence accession numbers

The sequence data supporting the results of this article have been deposited in the NCBI Sequence Read Archive under accession number SRP098783.

Results

Epidemiological characteristics and PFGE subtyping results of the 145 S. Heidelberg isolates

Epidemiologic and PFGE fingerprinting results of the 145 S. Heidelberg isolates used in this study are presented in Table 2.

Table 2. Epidemiologic and subtyping results of the 145 S. Heidelberg clinical and food isolates used in this study.

Isolate No. Source Isolation date Outbreak code Pulsotype Phage type NCBI accession no.
ID117793 Human 05–2012 1 2 19 SH12-001
ID117794 Human 05–2012 1 2 19 SH12-002
ID117795 Human 05–2012 1 2 19 SH12-003
ID117796 Human 05–2012 1 2 19 SH12-004
ID117797 Human 05–2012 1 2 19 SH12-005
ID117798 Human 05–2012 1 2 19 SH12-006
ID117799 Human 05–2012 1 2 19 SH12-007
ID117800 Human 05–2012 1 2 19 SH12-008
ID118040 Food 05–2012 1 2 19 SH12-009
ID117870 Food 05–2012 1 2 19 SH12-010
ID128696 Human 11–2013 2 2 26 SH13-001
ID128783 Human 11–2013 2 2 26 SH13-002
ID128786 Human 11–2013 2 2 26 SH13-003
ID128787 Human 11–2013 2 2 26 SH13-004
ID128808 Human 11–2013 2 2 26 SH13-005
ID128902 Human 11–2013 2 2 26 SH13-006
ID128908 Human 11–2013 2 2 26 SH13-007
ID128910 Human 11–2013 2 2 26 SH13-008
ID134557 Human 07–2014 3 2 19 SH14-001
ID134930 Human 08–2014 3 2 19 SH14-002
ID134612 Human 08–2014 3 2 19 SH14-003
ID134421 Human 08–2014 3 2 19 SH14-004
ID134719 Human 08–2014 3 2 19 SH14-005
ID134608 Human 08–2014 3 2 19 SH14-006
ID135122 Human 08–2014 3 2 19 SH14-007
ID134610 Human 08–2014 3 2 19 SH14-008
ID134609 Human 08–2014 3 2 19 SH14-009
ID134565 Human 08–2014 3 2 17 SH14-010
ID134559 Human 08–2014 3 2 17 SH14-011
ID134929 Human 08–2014 3 2 ATHE-35 SH14-012
ID134879 Food 08–2014 3 2 19 SH14-013
ID134880 Food 08–2014 3 2 19 SH14-014
ID134881 Food 08–2014 3 2 19 SH14-015
ID134882 Food 08–2014 3 2 19 SH14-016
ID134883 Food 08–2014 3 2 19 SH14-017
ID134884 Food 08–2014 3 2 19 SH14-018
ID134885 Food 08–2014 3 2 19 SH14-019
ID134886 Food 08–2014 3 2 19 SH14-020
ID134887 Food 08–2014 3 2 19 SH14-021
ID134888 Food 08–2014 3 2 19 SH14-022
ID134889 Food 08–2014 3 2 19 SH14-023
ID134890 Food 08–2014 3 2 19 SH14-024
ID135137 Food 08–2014 3 2 19 SH14-025
ID135138 Food 08–2014 3 2 19 SH14-026
ID135139 Food 08–2014 3 2 19 SH14-027
ID135140 Food 08–2014 3 2 19 SH14-028
ID148030 Human 03–2016 4 2 19 SRR5228105
ID148149 Human 03–2016 4 2 19 SRR5228097
ID148230 Human 03–2016 4 2 19 SRR5228082
ID148231 Human 03–2016 4 2 19 SRR5228079
ID148280 Human 03–2016 4 2 19 SRR5228104
ID148286 Human 03–2016 4 2 19 SRR5228087
ID148337 Human 03–2016 4 2 19 SRR5228078
ID148338 Human 03–2016 4 2 19 SRR5228091
ID094525 Human 12–2007 NA 2 19 SRR5227118
ID095996 Human 04–2008 NA 3 11 SRR5227171
ID097320 Human 07–2008 NA 2 19 SRR5227121
ID099254 Human 10–2008 NA 2 29 SRR5227124
ID099787 Human 12–2008 NA 2 19 SRR5228101
ID100344 Human 01–2009 NA 2 19 SRR5227119
ID100753 Human 02–2009 NA 2 19 SRR5227155
ID101488 Human 04–2009 NA 122 16 SRR5227148
ID102666 Human 07–2009 NA 2 26 SRR5227120
ID102743 Human 08–2009 NA 2 19 SRR5227166
ID102860 Human 08–2009 NA 17 19 SRR5227126
ID102963 Human 08–2009 NA 2 26 SRR5228093
ID103472 Human 09–2009 NA 2 19 SRR5227163
ID103849 Human 10–2009 NA 1 2 SRR5227117
ID103978 Human 10–2009 NA 138 16 SRR5227128
ID104279 Human 11–2009 NA 140 1 SRR5227146
ID104398 Human 12–2009 NA 1 2 SRR5227169
ID105089 Human 02–2010 NA 6 32 SRR5227122
ID105144 Human 02–2010 NA 2 19 SRR5227127
ID106827 Human 06–2010 NA 87 32 SH12-013
ID107176 Human 07–2010 NA 2 29 SRR5228100
ID107454 Human 07–2010 NA 1 2 SRR5227152
ID108191 Human 08–2010 NA 2 19 SH10-001
ID108221 Human 08–2010 NA 2 26 SH10-014
ID108759 Human 09–2010 NA 86 26 SRR5227162
ID108677 Human 09–2010 NA 2 26 SH10-015
ID110275 Human 01–2011 NA 165 35 SRR5227139
ID110331 Human 01–2011 NA 107 22 SRR5227156
ID110403 Human 01–2011 NA 2 19 SRR5227141
ID110674 Human 02–2011 NA 168 atypical SRR5227174
ID110801 Human 02–2011 NA 2 26 SH11-002
ID111466 Human 04–2011 NA 2 19 SRR5227167
ID113160 Human 08–2011 NA 66 29 SRR5227130
ID113273 Human 08–2011 NA 175 47 SRR5227140
ID113787 Human 09–2011 NA 178 atypical SRR5227173
ID114520 Human 10–2011 NA 2 29 SRR5227135
ID114593 Human 11–2011 NA 2 29 SRR5227157
ID115377 Human 12–2011 NA 2 19 SRR5227165
ID115568 Human 01–2012 NA 86 29 SRR5227133
ID116136 Human 02–2012 NA 2 29 SRR5227164
ID116271 Human 02–2012 NA 87 32 SH10-014
ID116824 Human 03–2012 NA 107 ATHE-10 SRR5227129
ID117211 Human 04–2012 NA 2 29 SRR5227151
ID117095 Human 04–2012 NA 2 29 SRR5227172
ID117506 Human 04–2012 NA 2 19 SH10-002
ID117683 Human 04–2012 NA 2 19 SRR5227143
ID117578 Human 05–2012 NA 2 19 SH12-011
ID118209 Human 05–2012 NA 4 5 SRR5227123
ID118236 Human 05–2012 NA 86 29 SRR5227154
ID118280 Human 05–2012 NA 107 ATHE-10 SRR5227150
ID118551 Human 06–2012 NA 52 10 SRR5227136
ID118532 Human 06–2012 NA 52 10 SRR5227175
ID118700 Human 06–2012 NA 52 10 SRR5227158
ID118759 Human 06–2012 NA 2 18 SH12-012
ID118979 Human 07–2012 NA 2 19 SRR5227159
ID119224 Human 07–2012 NA 2 29 SRR5227125
ID119366 Human 07–2012 NA 186 10 SRR5227138
ID119464 Human 08–2012 NA 2 17 SRR5227145
ID119539 Human 08–2012 NA 2 19 SRR5227147
ID119674 Human 08–2012 NA 2 19 SRR5227170
ID119888 Human 08–2012 NA 52 10 SRR5227161
ID119967 Human 08–2012 NA 2 19 SRR5227144
ID120403 Human 09–2012 NA 2 19 SRR5227160
ID120598 Human 09–2012 NA 2 19 SRR5227132
ID120747 Human 09–2012 NA 2 19 SRR5227131
ID121956 Human 11–2012 NA 189 10 SRR5227149
ID122356 Human 12–2012 NA 2 19 SRR5227176
ID124024 Human 03–2013 NA 2 19 SRR5227168
ID124305 Human 04–2013 NA 2 29 SRR5227153
ID124498 Human 04–2013 NA 4 35 SRR5227142
ID125378 Human 06–2013 NA 2 19 SRR5227137
ID126392 Human 07–2013 NA 2 19 SRR5227134
ID126712 Human 08–2013 NA 2 19 SRR5228099
ID126777 Human 08–2013 NA 2 19 SRR5228083
ID126776 Human 08–2013 NA 2 19 SRR5228080
ID126696 Human 08–2013 NA 2 19 SRR5228081
ID126825 Human 08–2013 NA 2 29 SRR5228085
ID147047 Human 02–2016 NA 4 5 SRR5228102
ID147120 Human 02–2016 NA 231 32 SRR5228084
ID147091 Human 02–2016 NA 52 10 SRR5228088
ID147129 Human 02–2016 NA 2 19 SRR5228092
ID147253 Human 02–2016 NA 194 29 SRR5228086
ID147255 Human 02–2016 NA 229 10 SRR5228090
ID147457 Human 02–2016 NA 225 19 SRR5228089
ID147462 Human 02–2016 NA 214 29 SRR5228094
ID147990 Human 03–2016 NA 52 10 SRR5228106
ID147796 Human 03–2016 NA 52 10 SRR5228095
ID147816 Human 03–2016 NA 2 19 SRR5228103
ID147910 Human 03–2016 NA 214 29 SRR5228077
ID147841 Human 03–2016 NA 2 19 SRR5228096
ID148066 Human 03–2016 NA 2 19 SRR5228098
Open in a new tab

NA, Not available.

Isolates from four distinct outbreaks that occurred in Quebéc between 2012 and 2016 were also included in the analysis. These outbreaks were designated as follows: outbreak 1, 2012 (n = 10; 8 human and 2 food isolates) outbreak 2, 2013 (n = 8 human isolates) outbreak 3, 2014 (n = 28; 12 human and 16 food isolates and outbreak 4, 2016 (n = 8 human isolates). All human cases and food items linked to these outbreaks were confirmed by epi-data. Outbreak 1 was linked to a wedding party, outbreak 2 and 3 were traced to separate restaurants and outbreak 4 was associated with a daycare catering service. In addition to the outbreak isolates (n = 54) we also added 91 sporadic clinical isolates collected in Quebéc between 2007 and 2016 into the analysis.

Whole genome sequencing results

An average of 983,919 reads was obtained per isolate (range, 339,270–2,974,917) for the set of 145 S. Heidelberg isolates, corresponding to an estimated average genome coverage of 121x (range, 42x -365x). The number of SPAdes-assembled contigs for the five outbreak isolates that were selected to act as the reference ranged from 24–27 with all the isolates assembled into fewer than 27 contigs (Table 3). The completely sequenced genome equivalent of these isolates have been published in a previous study [9].

Table 3. Assembly statistics for the 5 S. Heidelberg isolates that served as reference genomes.

Strain Total length (bp) No of contigs N50 (bp) Coverage
ID117795 4,751,241 24 694,16 137
ID128787 4,747,971 27 363,273 43
ID128902 4,746,565 27 412,159 64
ID134609 4,853,519 27 412,096 130
ID135140 4,753,550 27 412,162 116
Open in a new tab

Core genome single nucleotide analysis

After removing repeats and prophages as well as SNV-dense regions from all the reference genomes, an average of 4,008,254 genomic positions (range 3,681,444–4,049,343) representing an average of 86% of the reference genomes (range 84.63–86.72%) had sufficient coverage (≥15x) across all 145 isolates for reference mapping. For all the S. Heidelberg reference genomes, an average of 769 high-quality consensus SNVs (range 751–819) were identified by both variant callers as common to all isolates and used for subsequent phylogenetic clustering. For the distantly related S. Dublin genome, 18,155 high-quality core genome SNVs were used to construct the phylogeny. In total, 15 minimum spanning trees were generated with 14 of these trees representing the 14 S. Heidelberg reference genomes. All outbreak isolates formed distinct clusters with all the 14 S. Heidelberg reference genomes and the topologies of these trees were highly similar (Fig 1A and 1B).

Fig 1. Minimum spanning phylogenetic tree of the core genome of 145 S. Heidelberg sequenced isolates generated using A) draft genome or B) closed referenced genome (ID117795) as an example.

Fig 1

Open in a new tab

Isolates in the same circle have 0 hqSNVs and the size of each circle is proportional to the number of isolates in the circle.

Using S. Dublin as the reference genome led to a loss in resolving power. In fact, sporadic isolates clustered with outbreak 1 isolates (Fig 2).

Fig 2. Minimum spanning phylogenetic tree of the core genome of 145 S. Heidelberg sequenced isolates generated using the distantly related reference S. Derby (SL477).

Fig 2

Open in a new tab

Isolates in the same circle have 0 hqSNVs and the size of each circle is proportional to the number of isolates in the circle.

The phylogenetic features revealed by the minimum spanning trees were nearly identical across all the 14 S. Heidelberg reference genomes (Table 4).

Table 4. Phylogenetic observations of the minimum spanning tree topology built from cgSNV analysis of 145 S. Heidelberg isolates and comparison of draft and closed genomes as references.

References selected from within the outbreak isolates References downloaded from NCBI Distantly related genome downloaded from NCBI
Minimum spanning tree features ID117795 ID128787 ID128902 ID134609 ID135140 SL486 JWQE01.1 SL476 CP003416.1 CP001144
Draft Closed Draft Closed Draft Closed Draft Closed Draft Closed Draft Draft Closed Closed Closed
Total # of nodes on the tree 92 92 92 92 92 92 93 93 93 93 92 93 93 93 86
Total # of nodes representing 1 isolate 79 79 79 79 79 79 80 80 80 80 78 79 80 80 72
Total # of nodes with more than one isolate 13 13 13 13 13 13 13 13 13 13 13 14 13 13 15
Total # of OB nodes 13 13 13 13 13 13 13 13 13 13 13 13 13 13 10
Total # of nodes with one isolate 9 9 9 9 9 9 9 9 9 9 9 9 9 9 6
Total # of OB nodes with more than one isolate 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
Total # of sporadic nodes 79 79 79 79 79 79 80 80 80 80 78 79 80 80 76
Total # of sporadic nodes linked to main OB clusters 10 10 10 10 10 10 10 10 10 10 10 10 10 10 10
Number of sites used to generate phylogeny 762 762 761 760 762 760 766 761 764 763 777 799 751 819 18155
Number of genomic positions used for reference mapping 4,046,580 4,037,652 4,046,584 4,037,362 4,047,586 4,037,102 4,049,343 4,037,849 4,046,484 4,037,788 4,008,004 4,048,063 3,925,643 4,036,335 3,681,444
% of genomic positions representing the genome 86,04% 86,68% 86,07% 86,7% 86,08% 86,69% 85% 86,72% 86% 86,72% 85,95% 86,12% 85,72% 86,67% 84,63%
Open in a new tab

The genetic distances observed between outbreak isolates as well as between outbreak and sporadic isolates were nearly identical across the 14 S. Heidelberg reference genomes. Using S. Dublin as the reference genome led to a significant reduction in genetic distances between sporadic and outbreak isolates with some sporadic isolates having 0 and 3 SNV difference with outbreak 1 and 3 respectively whereas the genetic distances between isolates in these outbreaks ranged from 0–3 SNVs (Table 5).

Table 5. Comparison of the number of high quality SNVs between 145 S. Heidelberg sporadic and outbreak isolates using a draft, closed and distantly related reference genomes.

Referencea Outbreak Outbreak 1 Outbreak 2 Outbreak 3 Outbreak 4 Sporadic
Draft Closed Draft Closed Draft Closed Draft Closed Draft Closed
ID117795 1 0–3 0–3 72–74 71–73 48–51 48–52 18–21 18–21 1–93 1–93
2 72–74 71–73 0 0 66–67 65–67 80–81 79–80 4–105 4–104
3 48–51 48–52 66–67 65–67 0–2 0–3 56–58 56–59 6–86 6–87
4 18–21 18–21 80–81 79–80 56–58 56–59 0–1 0–1 10–100 10–100
ID128787 1 0–3 0–3 71–73 71–73 47–50 47–50 17–20 17–20 1–92 1–92
2 71–73 71–73 0 0 66–67 66–67 80–81 80–81 4–105 4–105
3 47–50 47–50 66–67 66–67 0–2 0–2 56–58 56–58 6–86 6–86
4 17–20 17–20 80–81 80–81 56–58 56–58 0–1 0–1 10–100 10–100
ID128902 1 0–3 0–3 72–74 71–73 48–52 47–50 17–20 17–20 1–92 1–92
2 72–74 71–73 0 0 66–68 66–67 81–82 80–81 4–105 4–105
3 48–52 47–50 66–68 66–67 0–3 0–2 57–60 56–58 6–86 6–86
4 17–20 17–20 81–82 80–81 57–60 56–58 0–1 0–1 10–101 10–100
ID135140 1 0–3 0–3 72–74 71–73 48–52 47–51 17–20 17–20 1–93 1–92
2 72–74 71–73 0 0 66–68 66–68 81–82 80–81 4–105 4–105
3 48–52 47–51 66–68 66–68 0–3 0–3 58–60 58–59 6–87 6–87
4 17–20 17–20 81–82 80–81 58–60 58–59 0–1 0–1 10–100 10–100
ID135609 1 0–3 0–3 72–74 71–73 48–52 47–50 17–20 17–20 1–93 1–92
2 72–74 71–73 0 0 66–68 66–67 81–82 80–81 4–105 4–105
3 48–52 47–50 66–68 66–67 0–3 0–2 57–60 56–58 6–86 6–86
4 17–20 17–20 81–82 80–81 57–60 56–58 0–1 0–1 10–101 10–100
SL486 1 0–3 ND 71–73 ND 46–49 ND 17–20 ND 1–92 ND
2 71–73 0 65–66 80–81 4–105
3 46–49 65–66 0–2 55–57 6–85
4 17–20 80–81 55–57 0–1 13–100
JWQE01.1 1 0–3 ND 71–73 ND 46–49 ND 17–20 ND 1–91 ND
2 70–72 0 66–67 79–80 4–105
3 46–49 66–67 0–2 55–57 6–86
4 17–20 79–80 55–57 0–1 10–99
SL476 1 ND 0–3 ND 68–70 ND 46–49 ND 17–20 ND 1–92
2 68–70 0 65–66 80–81 4–105
3 46–49 65–66 0–2 55–57 6–85
4 17–20 80–81 55–57 0–1 13–100
CP003416.1 1 ND 0–3 ND 72–74 ND 47–50 ND 17–20 ND 1–92
2 72–74 0 67–68 81–82 4–106
3 47–50 67–68 0–2 56–58 6–86
4 17–20 81–82 56–58 0–1 10–100
CP001144 1 ND 0–2 ND 45–47 ND 28–31 ND 10–12 ND 0–60
2 45–47 0 39–40 49–49 2–67
3 28–31 39–40 0–2 32–34 3–53
4 10–12 49–49 32–34 0 7–62
Open in a new tab

aThe reference genomes were obtained from: S. Heidelberg outbreak isolates [ID117795, ID128787, ID128902, ID135140, and ID135609]; publicly-available S. Heidelberg references from NCBI [SL486, JWQE01.1, SL476, CP003416.1] and distantly-related S. Dublin reference from NCBI [CP001144].

ND, Not Determined.

Topological similarity of the phylogenetic trees

To confirm the similarities in tree topologies, we performed the RF test. The computed RF topological distances between the 14 trees ranged from 0 to 24 (Table 6).

Table 6. Robinson-Foulds topological distances between trees generated with 145 S. Heidelberg sequenced isolates using draft and closed genomes as references during cgSNV analysis.

Tree 2 Tree 3 Tree 4 Tree 5 Tree 6 Tree 7 Tree 8 Tree 9 Tree 10 Tree 11 Tree 12 Tree 13 Tree 14
Tree 1 0 20 20 20 20 20 20 20 20 7 11 15 11
Tree 2 20 20 20 20 20 20 20 10 7 11 15 11
Tree 3 0 0 0 24 0 24 24 13 11 15 11
Tree 4 0 0 24 0 24 24 13 11 15 11
Tree 5 0 24 0 24 24 13 11 15 11
Tree 6 24 0 24 24 13 11 15 11
Tree 7 24 0 0 13 15 19 15
Tree 8 24 24 13 11 15 11
Tree 9 0 13 15 19 15
Tree 10 13 15 19 15
Tree 11 4 8 4
Tree 12 4 0
Tree 13 4
Open in a new tab

Discussion

In this study we assessed the impact of the choice of the reference genome on the resolution clustering of S. Heidelberg outbreak and sporadic isolates using the cgSNV methodology. Our results revealed that using a draft or completely sequenced S. Heidelberg genomes as references did not affect the ability of the cgSNV method to distinguish between four epidemiologically well characterized S. Heidelberg outbreak isolates and to separate these isolates from sporadic or background strains. In fact, all outbreak and sporadic isolates were clustered on distinct branches (Fig 1A and 1B). On the contrary, using S. Dublin as reference choice resulted in a tree with less resolution (Fig 2). Sporadic isolates clustered together with outbreak 1 isolates and in addition, the genetic distances observed within outbreak as well as between outbreak and sporadic isolates was overall reduced when S. Dublin was used as reference choice as opposed to S. Heidelberg reference genomes (Figs 1 and 2, Table 5). This finding is in agreement with a recent study on Salmonella which found that using a distantly related genome as a choice of reference failed to cluster S. Enteritidis outbreak strains concordantly [18]. These observations emphasize the need to choose an appropriate reference genome during laboratory investigations of foodborne outbreaks involving reference based methods.

The loss in resolution observed in this study can be due to the following reasons: Firstly, the SNVPhyl pipeline uses only core genome SNVs to build the phylogeny implying that a reference genome with high similarity with the isolates under investigation would have a larger core genome from which more SNVs can be produced to build a phylogeny as opposed to a dissimilar reference such as S. Dublin. However, the smallest core genome among all the reference genomes used in this study was equivalent to 84.76% of the total genome. Secondly, another issue to consider is that as the reference genome grows more distant from the sequences under analysis, more variation would be observed between the core regions of the reference genome and all other sequences leading to a long branch separating the reference genome from the rest of the other isolates. This was indeed what we observed using S. Dublin as reference (Fig 2) since the majority of the 18155 positions used to construct the phylogeny were indeed variations between the reference (17666 positions) and the other isolates.

The RF values for the trees constructed using S. Heidelberg draft genome and its corresponding closed genome equivalent was zero with the exception of the reference genome pair ID134609 (tree 7 vs. tree 8) whereby the RF topological distance was 24. This difference could be attributed to misidentification of SNVs linked to the presence of repetitive regions in the draft genome that were not properly detected. By nature, draft genomes are not entirely genomically accurate and for this reason, it is possible that the SPAdes assembly for this isolate may have collapsed repetitive regions larger than the read/read pair size into a single contig. Aligning reads to repetitive regions is problematic and has been reported to lead to potential misidentification of SNVs [19]. Although we used the software MUMmer to identify and filter the repetitive regions of the reference genome, for a closed genome, this method will be much more successful due to its intact, completely mapped sequence than for a draft genome, which may erroneously possess several copies of unmapped repeat regions. These unmapped repeats can lead to repeat reads mapping to a single contig resulting in the inclusion of additional SNVs and subsequent alignment issues and false SNV calls. Interestingly in this reference pair, 766 sites were used to generate the phylogeny using the draft genome as reference as opposed to 761 sites for the closed genome counterpart. Despite the slight differences in topology between this reference pair as well as in other tree pairs, both the draft and closed genomes were still able to differentiate the outbreak from sporadic isolates in concordance with epidemiological data. In agreement with our observations, a recent study on Listeria monocytogenes also demonstrated that phylogenetic clustering based on SNV analysis using a de novo assembled draft genome selected from within the group was similar to the phylogeny using a closely related closed genome [6].

Despite the increasing accessibility of WGS-based technologies and decreasing costs, the implementation of WGS for routine surveillance and outbreak investigation may still present many challenges as sequencing a genome to completion remains a costly and time-consuming endeavour and for many public-health laboratories, this is not a viable option. [20]. The results of our study indicates that draft genomes can be relied upon as a suitable reference choice during laboratory investigations of foodborne outbreaks using the cgSNVphyl pipeline. In fact, a recent study evaluated the quality score of 32000 genomes located in public repositories and concluded that most of these genomes were of sufficient quality to perform analysis on and only 10% of draft genomes were of poor quality and unsuitable for downstream analysis [21]. In conclusion, our results provide strong evidence that the choice of the reference genome does not impact the ability of the cgSNV methodology to distinguish between S. Heidelberg isolates involved in foodborne outbreaks. Our results also demonstrate that using a distantly related genome as reference could lead to a loss in resolution during cgSNV analysis. Although wgMLST was recently recommended as the primary subtyping tool moving forward by PNC and other foodborne surveillance networks [22], it is important to note that the cgSNV approach still remains an important method in the PNC molecular tool box. In fact, this method was recently used by PNC in collaboration with provincial public health laboratories to identify the source of a multi-provincial outbreak of S. Enteritidis [23]. Despite the advantages provided by cg/wgMLST approaches such as curability and standardization, the development and validation of schemas for each organism still remains a daunting task both financially and technically for public health laboratories operating with limited resources. The implementation of the cgSNV methodology described here could be a viable alternative for monitoring S. Heidelberg. Whether this method applies to other Salmonella serovars remains to be determined.

Acknowledgments

We express our gratitude to members of the genomics analysis and bioinformatics platforms at the National Microbiology Laboratory and the Bacteriology Unit staff at the Laboratoire de Santé Publique du Québec. Lawrence Goodridge, Céline Nadon and Sadjia Bekal are funded by Genome Canada and by the Génome Québec provincial genome center.

Data Availability

All relevant data are within the paper.

Funding Statement

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.The National Microbiology Laboratory (NML), Centre for Food-borne, Environmental and Zoonotic Infectious Diseases (CFEZID), Public Health Agency of Canada, Provincial Public Health Microbiology Laboratories. 2014. National Enteric Surveillance Program (NESP): annual summary 2012. Public Health Agency of Canada, Ottawa, Ontario, Canada.
  • 2.Bekal S, Berry C, Reimer AR, Van Domselaar G, Beaudry G, Fournier E, et al. Usefulness of High-Quality Core Genome Single-Nucleotide Variant Analysis for Subtyping the Highly Clonal and the Most Prevalent Salmonella enterica Serovar Heidelberg Clone in the Context of outbreak investigations. J Clin Microbiol. 2016; 54:289–295. doi: 10.1128/JCM.02200-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Allard MW, Strain E, Melka D, Bunning K, Musser SM, Brown EW, et al. Practical Value of Food Pathogen Traceability through Building a Whole Genome Sequencing Network and Database. J Clin Microbiol. 2016; 54: 1975–1983. doi: 10.1128/JCM.00081-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Maiden MC, Rensburg VMJ, Bray JE, Earle SG, Ford SA, Jolley KA, et al. MLST revisited: the gene-by-gene approach to bacterial genomics. Nat Rev Microbiol. 2013;11:728–736. doi: 10.1038/nrmicro3093 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Moura A, Criscuolo A, Pouseele H, Maury MM, Leclercq A, Tarr C, et al. Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes. Nat Microbiol. 2016; 10/10 2:16185. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Kwong JC, Mercoulia K, Tomita T, Easton M, Li HY, Bulach DM, et al. Prospective Whole-Genome Sequencing Enhances National Surveillance of Listeria monocytogenes. J Clin Microbiol. 2016; 54:333–342. doi: 10.1128/JCM.02344-15 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Bertels F, Silander OK, Pachkov M, Rainey PB, Nimwegen VE. Automated reconstruction of whole-genome phylogenies from short-sequence reads. Mol Biol Evol. 2014; 31:1077–1088. doi: 10.1093/molbev/msu088 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: A new genome assembly algorithm and its application to single cell sequencing. J Comput Biol. 2012; 19:455–477. doi: 10.1089/cmb.2012.0021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Labbé G, Ziebell K, Bekal S, Macdonald KA, Parmley EJ, Agunos A, et al. Complete genome sequence of 17 Canadian isolates of Salmonella enterica subsp. enterica serovar Heidelberg from human, animal and food sources. Genome Announce. 2016; 15: pii: e00990-16. doi: 10.1128/genomeA.00990-16 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Petkau A, Mabon P, Sieffert C, Knox NC, Cabral J, Iskander M, et al. SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology. BioRixV. 2016;bioRixv. https://doi.org/10.1101/092940. [DOI] [PMC free article] [PubMed]
  • 11.Afgan E, Baker D, Beek VDM, Blankenberg D, Bouvier D, Cech M, et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucl Acids Res. 2016; 44: W3–W10. doi: 10.1093/nar/gkw343 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Olson ND, Lund SP, Colman RE, Foster JT, Sahl JW, Schupp JM, et al. Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Front Genet. 2015; 6:235 doi: 10.3389/fgene.2015.00235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Arndt D, Grant JR, Marcu A, Sajed T, Pon A, Liang Y, et al. PHASTER: a better, faster version of the PHAST phage search tool. Nucl Acids Res. 2016; 44:W16–21. doi: 10.1093/nar/gkw387 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Li H, Handsaker B, Wysoker A, Fennel T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–2079 doi: 10.1093/bioinformatics/btp352 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Francisco AP, Vaz C, Monteiro PT, Cristino MJ, Ramirez M, Carrico JA. PHYLOViZ: phylogenetic inference and data visualization for sequence based typing methods. BMC Bioinform. 2012; 13: 87. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Robinson DF, Foulds LR. Comparison of phylogenetic trees. Mathematical Biosciences. 1981; 53:131–147. doi: 10.1016/0025-5564(81)90043-2 [Google Scholar]
  • 17.Boc A, Diallo AB, Makarenkov V. T-REX: a web server for inferring, validating and visualizing phylogenetic trees and networks. Nucl Acids Res. 2012; 40:W573–W579. doi: 10.1093/nar/gks485 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Leekitcharoenphon P, Nielsen EM, Kaas RS, Lund O, Aarestrup FM. Evaluation of whole genome sequencing for outbreak detection of Salmonella enterica. PLoS One. 2014; 9:e87791. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Olson ND, Lund SP, Colman RE, Foster JT, Sahl JW, Schupp JM, et al. Best practices for evaluating single nucleotide variant calling methods for microbial genomics. Front Genet. 2015; 6:235 doi: 10.3389/fgene.2015.00235 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Land M, Hauser L, Jun SR, Nookaew I, Leuze MR, Ahn TH, et al. Insights from 20 years of bacterial genome sequencing. Funct Integr Genomics. 2015; 15:141–161. doi: 10.1007/s10142-015-0433-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Land M, Hyatt D, Jun SR, Kora G, Hauser L, Lukjancenko O, et al. Quality scores for 32,000 genomes. Stand Genomic Sci. 2014; 9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Nadon C, Van Walle I, Smidt GP, Campos J, Chinen I, Acevedo CJ, et al. PulseNet International: Vision for the implementation of whole genome sequencing (wgs) for global food-borne disease surveillance. Euro Surveill. 2017;22:30544 doi: 10.2807/1560-7917.ES.2017.22.23.30544 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Croxen MA, Macdonald KA, Walker M, deWith N, Zabek E, Peterson C, et al. Multi-provincial Salmonellosis outbreak related to newly hatched chicks and poults. A genomics perspective. PLoSCurr.2017;1 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

All relevant data are within the paper.

Articles from PLoS ONE are provided here courtesy of PLOS

RESOURCES