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PERSPECTIVE
published: 02 June 2017
doi: 10.3389/fmicb.2017.00996
Edited by:
Sabah Bidawid,
Health Canada, Canada
Reviewed by:
Young Min Kwon,
University of Arkansas, United States
Sheng Chen,
Hong Kong Polytechnic University,
Hong Kong
*Correspondence:
Roger C. Levesque
rclevesq@ibis.ulaval.ca
Lawrence Goodridge
lawrence.goodridge@mcgill.ca
†These authors have contributed
equally to this work.
Specialty section:
This article was submitted to
Food Microbiology,
a section of the journal
Frontiers in Microbiology
Received: 29 March 2017
Accepted: 17 May 2017
Published: 02 June 2017
Citation:
Emond-Rheault J-G , Jeukens J,
Freschi L, Kukavica-Ibrulj I, Boyle B,
Dupont M-J, Colavecchio A,
Barrere V, Cadieux B, Arya G,
Bekal S, Berry C, Burnett E,
Cavestri C, Chapin TK, Crouse A,
Daigle F, Danyluk MD, Delaquis P,
Dewar K, Doualla-Bell F, Fliss I,
Fong K, Fournier E, Franz E,
Garduno R, Gill A, Gruenheid S,
Harris L, Huang CB, Huang H,
Johnson R, Joly Y, Kerhoas M,
Kong N, Lapointe G, Larivière L,
Loignon S, Malo D, Moineau S,
Mottawea W, Mukhopadhyay K,
Nadon C, Nash J, Ngueng Feze I,
Ogunremi D, Perets A, Pilar AV,
Reimer AR, Robertson J, Rohde J,
Sanderson KE, Song L, Stephan R,
Tamber S, Thomassin P, Tremblay D,
Usongo V, Vincent C, Wang S,
Weadge JT, Wiedmann M,
Wijnands L, Wilson ED, Wittum T,
Yoshida C, Youfsi K, Zhu L,
Weimer BC, Goodridge L and
Levesque RC (2017) A Syst-OMICS
Approach to Ensuring Food Safety
and Reducing the Economic Burden
of Salmonellosis.
Front. Microbiol. 8:996.
doi: 10.3389/fmicb.2017.00996
A Syst-OMICS Approach to Ensuring
Food Safety and Reducing the
Economic Burden of Salmonellosis
Jean-Guillaume Emond-Rheault1†, Julie Jeukens1†, Luca Freschi1†,
Irena Kukavica-Ibrulj1, Brian Boyle1, Marie-Josée Dupont1, Anna Colavecchio2,
Virginie Barrere2, Brigitte Cadieux2, Gitanjali Arya3, Sadjia Bekal4, Chrystal Berry3,
Elton Burnett2, Camille Cavestri5, Travis K. Chapin6, Alanna Crouse2, France Daigle7,
Michelle D. Danyluk6, Pascal Delaquis8, Ken Dewar2,9, Florence Doualla-Bell4,
Ismail Fliss5, Karen Fong10 , Eric Fournier4, Eelco Franz11 , Rafael Garduno12,
Alexander Gill13 , Samantha Gruenheid2, Linda Harris14, Carol B. Huang15,
Hongsheng Huang16 , Roger Johnson3, Yann Joly2, Maud Kerhoas7, Nguyet Kong15,
Gisèle Lapointe17 , Line Larivière2, Stéphanie Loignon5, Danielle Malo2, Sylvain Moineau5,
Walid Mottawea2,18, Kakali Mukhopadhyay2, Céline Nadon3, John Nash3,
Ida Ngueng Feze2, Dele Ogunremi16 , Ann Perets3, Ana V. Pilar2, Aleisha R. Reimer3,
James Robertson3, John Rohde19 , Kenneth E. Sanderson20, Lingqiao Song2,
Roger Stephan21 , Sandeep Tamber13, Paul Thomassin2, Denise Tremblay5,
Valentine Usongo4, Caroline Vincent4, Siyun Wang10 , Joel T. Weadge22,
Martin Wiedmann23 , Lucas Wijnands11, Emily D. Wilson22 , Thomas Wittum24 ,
Catherine Yoshida3, Khadija Youfsi4, Lei Zhu2, Bart C. Weimer15 , Lawrence Goodridge2*
and Roger C. Levesque1*
1Institute for Integrative and Systems Biology, Université Laval, Québec City, QC, Canada, 2McGill University, Montréal, QC,
Canada, 3National Microbiology Laboratory, Public Health Agency of Canada, Ottawa, ON, Canada, 4Laboratoire de Santé
Publique du Québec, Sainte-Anne-de-Bellevue, QC, Canada, 5Université Laval, Québec City, QC, Canada, 6Institute of
Food and Agricultural Sciences, University of Florida, Gainesville, FL, United States, 7Département de Microbiologie,
Infectiologie et Immunologie, Université de Montréal, Montréal, QC, Canada, 8Agriculture and Agri-Food Canada,
Summerland, BC, Canada, 9Génome Québec Innovation Center, Montréal, QC, Canada, 10 Food Safety Engineering, Faculty
of Land and Food Systems, University of British Columbia, Vancouver, BC, Canada, 11 National Institute for Public Health and
the Environment, Bilthoven, Netherlands, 12 Canadian Food Inspection Agency, Halifax, NS, Canada, 13 Bureau of Microbial
Hazards, Health Canada, Ottawa, ON, Canada, 14 UC Davis Food Science and Technology, Davis, CA, United States,
15 UC Davis School of Veterinary Medicine, Davis, CA, United States, 16 Canadian Food Inspection Agency, Ottawa, ON,
Canada, 17 Food Science, University of Guelph, Guelph, ON, Canada, 18 Department of Microbiology and Immunology,
Faculty of Pharmacy, Mansoura University, Mansoura, Egypt, 19 Department of Microbiology and Immunology, Dalhousie
University, Halifax, NS, Canada, 20 Department of Biological Sciences, University of Calgary, Calgary, AB, Canada, 21 Institute
for Food Safety and Hygiene, University of Zurich, Zurich, Switzerland, 22 Biological and Chemical Sciences, Wilfrid Laurier
University, Waterloo, ON, Canada, 23 Department of Food Science, Cornell University, Ithaca, NY, United States, 24 College of
Veterinary Medicine, The Ohio State University, Columbus, OH, United States
The Salmonella Syst-OMICS consortium is sequencing 4,500 Salmonella genomes and
building an analysis pipeline for the study of Salmonella genome evolution, antibiotic
resistance and virulence genes. Metadata, including phenotypic as well as genomic
data, for isolates of the collection are provided through the Salmonella Foodborne Syst-
OMICS database (SalFoS), at https://salfos.ibis.ulaval.ca/. Here, we present our strategy
and the analysis of the first 3,377 genomes. Our data will be used to draw potential
links between strains found in fresh produce, humans, animals and the environment.
The ultimate goals are to understand how Salmonella evolves over time, improve the
accuracy of diagnostic methods, develop control methods in the field, and identify
prognostic markers for evidence-based decisions in epidemiology and surveillance.
Keywords: Salmonella, foodborne pathogen, next-generation sequencing, bacterial genomics, phylogeny,
antibiotic resistance, database
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IMPORTANCE OF FOODBORNE
Salmonella AS A MODEL IN
LARGE-SCALE BACTERIAL GENOMICS
Salmonella enterica is a foodborne bacterial pathogen having at
least 2,600 serotypes (Gal-Mor et al., 2014)1that contaminates a
diversity of foods and is a leading cause of foodborne illnesses
and mortality globally. In fact, there are an estimated 93.3
million cases of gastroenteritis due to non-typhoidal Salmonella
infections each year, resulting in approximately 155,000 deaths
(Majowicz et al., 2010). In Canada, non-typhoidal salmonellosis
accounts for more than 88,000 cases of foodborne illness
each year, and has among the highest incidence rate of any
bacterial foodborne pathogen (Thomas et al., 2015). S. enterica is
responsible for more than 50% of fresh produce-borne outbreaks,
the highest number of foodborne outbreaks of any inspected
food commodity in North America (Kozak et al., 2013). Because
of its remarkable genomic diversity, Salmonella is found in
complex environmental and ecological niches and survives in
harsh environments for long periods (Podolak et al., 2010;Fatica
and Schneider, 2011). Several research groups have identified
relationships between some of the 2,557 S. enterica serotypes and
specific foods, which suggests, that some food commodities act
as reservoirs for particular serotypes (Kim, 2010;Jackson et al.,
2013;Nuesch-Inderbinen et al., 2015).
Salmonella outbreaks are monitored with support from
the PulseNet surveillance system in 86 countries2(Ribot
and Hise, 2016;Scharff et al., 2016). PulseNet Canada3is
a national surveillance system used to quickly identify and
respond to foodborne disease outbreaks, centralized at the
National Microbiology Laboratory in Winnipeg, MB, and
working in close collaboration with a network of federal and
provincial public health laboratories and epidemiologists. Still,
despite the availability of thousands of sequenced genomes,
knowledge of genome evolution integrated with transmission and
epidemiology is limited for produce-related outbreaks.
Studies of S. enterica population structure in humans,
animals, food and the environment are central to understand
the biodiversity, evolution, ecology and epidemiology of this
pathogen. However, studies describing the genetic structure
of Salmonella populations are commonly based on isolates
drawn overwhelmingly from clinical collections (Hoffmann
et al., 2014). This approach has resulted in a limited view
of Salmonella’s evolutionary history (D’costa et al., 2006;
Perry and Wright, 2014). In Salmonella as in many other
bacterial pathogens, there is limited knowledge on how genome
content, rearrangements and the complement of genes including
those acquired by horizontal gene transfer (HGT) contribute
to strain-specific phenotypes, including virulence (Casadevall,
2017). Various studies have sought to resolve the population
structure of Salmonella using complementary subtyping methods
including pulsed-field gel electrophoresis (PFGE), multiple
1https://www.cdc.gov/salmonella/reportspubs/salmonella-atlas/serotype-
snapshots.html
2http://www.pulsenetinternational.org/networks/usa/
3https://www.nml-lnm.gc.ca/Pulsenet/index- eng.htm
loci VNTR analysis (MLVA), 7-gene housekeeping schemes,
whole-genome multi-locus sequence typing (wgMLST) profiles,
pan- and core genome studies, and CRISPR analysis to define
molecular signatures, pathogen subtypes and the potential for
pathogenicity (Shariat and Dudley, 2014;Rouli et al., 2015;Liu
et al., 2016). Next-generation sequencing (NGS) coupled with
whole-genome comparison is well-positioned to become the gold
standard subtyping method, as it offers previously unmatched
resolution for phylogenetic analysis and rapid subtyping during
investigation of food contamination and outbreaks (Ashton et al.,
2016;Bekal et al., 2016).
THE Syst-OMICS Strategy
The application of genomics to infectious pathogens via
WGS is transforming the practice of Salmonella diagnostics,
epidemiology and surveillance. Genomic data are increasingly
used to understand infectious disease epidemiology (Didelot
et al., 2017). With rapidly falling costs and turnaround time,
microbial WGS and analysis is becoming a viable strategy to
identify the geographic origin of bacterial pathogens (Weedmark
et al., 2015;Hoffmann et al., 2016). The objective of the
Canadian-based international Syst-OMICS consortium is to
sequence a minimum of 4,500 genomes, include the data in the
Salmonella Foodborne Syst-OMICS database (SalFoS) at https:
//SalFoS.ibis.ulaval.ca/, share this information plus available
metadata with Canadian federal and provincial regulators and
the food industry, and develop pipelines to study these genomes.
Genomics data will support molecular epidemiology and source
attribution of outbreaks and has the potential for future genotypic
antimicrobial susceptibility testing, as well as the identification of
novel therapeutic targets and prognostic markers. Moreover, the
large-scale genomics and evolutionary biology tools developed
may lead to new strategies for countering not only Salmonella
infections, but other pathogens as well (Little et al., 2012).
The Syst-OMICS project is based upon a systems approach
(flowchart and screening method available in Supplementary
File 1). First, the genome diversity of 4,500 isolates will be assessed
using high-quality WGS, assembly, annotation and phylogeny.
This data will be used for in silico serotyping (Yoshida et al.,
2016), as well as analysis of virulence (Chen et al., 2012),
antibiotic resistance (Jia et al., 2016) and mobilome gene content
(Lanza et al., 2014). Based on this genomic data, a funnel-type
model will be applied such that 300 isolates will be selected
for in vitro high-throughput screening (HTS) in cell lines to
determine attachment, adhesion, invasion and replication of each
isolate (protocol adapted to 96-well plates from Forest et al.,
2007). From the results, isolates will be categorized as being
of high, medium, or low virulence. A limited number of those
isolates will then be selected for further screening in vivo using a
mouse model (Roy et al., 2007) and in vitro using gastrointestinal
fermenter models (Kheadr et al., 2010;Le Blay et al., 2012).
These data will identify isolates to represent the different levels
of virulence that will be used to develop novel diagnostic and
control tools. We propose to enhance food safety and lower
the economic burden of salmonellosis through a farm-to-table
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FIGURE 1 | Unrooted maximum likelihood tree of 3,377 Salmonella enterica genomes based on 196,774 SNPs using FastTree 2.1.9 (1000 bootstraps). The six
S. enterica subspecies names and specific epithets are indicated on the upper right tree. S. enterica subspecies enterica is split into two major lineages, clade A and
clade B, as proposed by Timme et al. (2013). The two S. bongori (V) isolates contained in SalFoS were not included in the phylogenetic tree because they
unnecessarily decrease resolution within the S. enterica subspecies. Number of genomes within each S. enterica subspecies are 3,235 enterica (2,648 in clade A
and 587 in clade B), 51 houtenae,32diarizonae,28salamae,17indica,8arizonae and 6 with unknown subspecies. Tree tips were colored based on the source of
each isolate. The number of isolates represented for each source category is shown between parentheses.
systematic approach to control Salmonella, with a focus on
new control methods in agricultural production, more specific
diagnostics and improved bacterial subtyping methods to support
investigation of foodborne outbreaks, as no single intervention is
likely to produce meaningful and lasting effects.
THE Salmonella FOODBORNE
Syst-OMICS DATABASE (SalFoS)
Salmonella Foodborne Syst-OMICS database is an online web
application that relies on a Mysql 5 database. It was designed not
only to store data for the Salmonella strain collection but also to
provide access to each isolate’s phenotypic, genomic, virulence,
serotype, mobilome and epidemiological data. Different levels
of access may be granted, but data modification is strictly
reserved to the curators. It includes isolate identification, host,
provider, date of isolation, geographical origin, phenotypic data,
DNA extraction details, NGS information and genome assembly
statistics. SalFoS currently contains NGS data and unpublished
draft genomes from produce, human, animal and environmental
isolates. Upon publication, draft genomes of SalFoS will become
available at NCBI and EnteroBase4.
4http://enterobase.warwick.ac.uk/
The SalFoS collection currently contains 2,498 entries for
Salmonella, as well as for Citrobacter,Hafnia and Proteus,
three genera often identified as false-positives by a number of
Salmonella detection schemes. It includes previously described
collections such as the unique Salmonella Genetic Stock Centre
strains, described at http://people.ucalgary.ca/~{}kesander/. This
collection was assembled with the aim of representing maximal
genomic diversity.
SEQUENCING 4,500 Salmonella
GENOMES: OBJECTIVES AND
STRATEGY
Our working hypothesis is that a very high-quality, large-scale
bacterial genome database available through a user-friendly
pipeline will have a major impact for epidemiology, diagnosis,
prevention and treatment. By generating a comprehensive
genome sequence database truly representative of the
foodborne Salmonella population, we will: (1) assemble a
large and representative strain collection, with associated
genome data, useful for antimicrobial testing, identification of
resistance markers, data mining for new therapeutic targets
and development of machine learning strategies; (2) develop
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FIGURE 2 | The resistome of 3,377 Salmonella strains. Gene, protein or variant presence were determined using the RGI-CARD (Mcarthur et al., 2013). Of 1,003
unique resistomes, only those present in at least five strains are shown; the histogram at the top represents absolute frequency. Other resistomes are condensed in
the “Rare genes” column. AMR genes and variants are grouped by antibiotic family or function. Genes encoding efflux pumps, which are generally conserved, have
been removed for figure clarity. Green: perfect match to a gene in the CARD, red: similar to a gene in the CARD, according to curated homology cut-offs, gray:
perfect match and/or similar to a gene in the CARD, black: no match in the CARD, ∗(wildcard) represents multiple forms (exact number between square brackets) of
the same gene or protein, †specific variants conferring resistance (protein variant models).
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platforms and pipelines to manage and analyze this information,
which will allow identification of prognostic markers, fast
epidemiological tracking and reduction of socio-economic
costs. We seek to develop user-friendly tools that will enable
epidemiologists, microbiologists, clinicians and others to
interpret genomic data, thus leading to informed decisions in
cases of food contamination and outbreaks. The contamination
of fresh produce by Salmonella will be addressed through
the development of natural solutions to control the presence
of Salmonella on fruits and vegetables as they are growing
in the fields. New tests will also be developed so that fresh
produce can be quickly and efficiently tested for the presence
of Salmonella before being sold to consumers. In the context
of outbreak investigation, the genomic data will be used to
assess high-quality SNPs and core/whole genome MLST for
their usefulness in genetic discrimination in addition to other
emerging methods such as CRISPR and prophage sequence
typing. As for outbreak investigation software, the National
Microbiology Laboratory-Public Health Agency of Canada
group has implemented the Integrated Rapid Infectious
Disease Analysis project (IRIDA)5and developed the SNVPhyl
phylogenomics pipeline that is in use by PulseNet Canada
for microbial genomic epidemiology (Petkau et al., 2016).
A complementary system called the Metagenomics Computation
and Analytics Workbench (MCAW) is being implemented as a
computing service for food safety (Edlund et al., 2016;Weimer
et al., 2016).
Sequencing for this project is performed on an Illumina MiSeq
instrument (at the Plateforme d’Analyses Génomiques of the
IBIS, Université Laval, Quebec City, QC, Canada), at a rate
of 120 genomes per week, using 300 bp paired-end libraries,
and with a median coverage of 45×. In order to perform core
genome phylogenetic analysis, the pan-genome, i.e., the complete
repertoire of genes of a species, is determined using a recently
developed software capable of handling high-quality NGS data
from thousands of genomes: Saturn V version 1.06(Jeukens
et al., 2017). Additional analyses focus on genes implicated in
virulence using comparative genomics predictions of confirmed
and predicted virulence factors (Yang et al., 2008), and resistome
identification based on the comprehensive antibiotic resistance
database (CARD) (Mcarthur et al., 2013;Jia et al., 2016). A set
of new reference Salmonella genomes representing maximal
genomic diversity among foodborne pathogens will then be
selected for PacBio Sequel sequencing to become fully assembled
and annotated as a single circular chromosome.
THE IBIS BIOINFORMATICS PIPELINE
FOR GENOME ASSEMBLY
When working with hundreds or thousands of genomes, analysis
software for assembly, annotation, statistics for quality control
and selection of additional reference genomes is required to
extract relevant information in an automated and reliable fashion
5http://dev.irida.ca/
6https://github.com/ejfresch/saturnV
with minimal human intervention. Ideally, this software should
be platform independent and able to analyze sequence data
directly without being tied to proprietary data formats. This
insures maximal flexibility and reduces lag time to a minimum.
We are currently using an integrated pipeline for de novo
assembly of microbial genomes based on the A5 pipeline (Tritt
et al., 2012). It was parallelized on a Silicon Graphics UV 300
using up to 120 cores to accommodate raw data from 120
genomes and provide assembly statistics as well as reference
genome alignment metrics in as little as 2 h. This automated
approach currently results in a median of 35 scaffolds per genome
(median N50 =462 kb).
PHYLOGENY OF Salmonella
Once isolates from a given outbreak are sequenced, patterns of
shared variations can be used to infer which isolates within the
outbreak are most closely related to each other (e.g., Didelot
et al., 2017). As a future strategy for the Syst-OMICS project, this
could be applied to partially sampled and on-going Salmonella
outbreaks. Here, as a first step in the study of S. enterica diversity
and epidemiology, we used 3,377 genomes; 1,627 were from
a collaboration with UC Davis (Bart C. Weimer), and 1,750
were part of SalFoS. All genomes with >100 scaffolds were
eliminated; this filter typically removes the vast majority of low
coverage (i.e., low quality) assemblies and mixed cultures. As
our assembly pipeline also includes alignment on a suite of
reference genomes, it is also possible to ensure that genomes used
belong to S. enterica. The core (conserved) genome was identified
with Saturn V, and consisted of 839 genes, which were used for
phylogenetic analysis. This number of core genes, which seems
small compared to other studies (2,882 core genes for 73 genomes
from 2 subspecies, Leekitcharoenphon et al., 2012), is due to both
the extensive diversity and the high number of genomes used.
As depicted in Figure 1, this population of S. enterica strains
could be divided into seven major groups. They correspond to
S. enterica subspecies enterica clades A and B and a collection
of branching subspecies previously defined as salamae, arizonae,
diarizonae, houtenae and indica. The significant number of
strains (3,377) included in our analysis and their wide-ranging
sources (including environmental, human, animal and food) is
essential to understand the diversity of Salmonella as a foodborne
pathogen and in defining levels of virulence. The remarkable
genomic diversity exhibited in Figure 1 is thought to enable the
colonization of a wide range of ecological niches. The Salmonella
Syst-OMICS consortium will provide fine-scale analysis of this
diversity via virulence factors, antibiotic resistance genes as well
as complete core and accessory genomes.
LINKING SalFoS WITH THE
COMPREHENSIVE ANTIBIOTIC
RESISTANCE DATABASE
The SalFoS database is intended to become an established
platform for searching and comparing multiple genome
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Emond-Rheault et al. The Salmonella Syst-OMICS Project
sequences for Salmonella isolates. The database will also
incorporate genome annotation and serotype prediction based on
SISTR (Yoshida et al., 2016). Close attention to the links between
specific genomic islands and patterns of SNPs in the core genome
will help identify diagnostic sequences and SNP combinations
for the development of new Salmonella subtyping methods with
the highest resolution to date. This will be done using de novo
island prediction with IslandViewer (Langille and Brinkman,
2009;Dhillon et al., 2015) as well as with gene presence-absence
from SaturnV.
As an additional feature, we routinely determine the resistome
of the genomes in SalFoS, i.e., the genes and variants likely
involved in antibiotic resistance. This is done using the Resistance
Gene Identifier (RGI) available with the CARD (Mcarthur et al.,
2013;Jia et al., 2016), at http://arpcard.mcmaster.ca/. Figure 2
summarizes the resistomes of 3,377 genomes. In fact, the original
dataset contained 1,003 unique resistomes, composed of various
combinations of 195 different genes and variants. Despite this
impressive diversity, the most striking feature shown in Figure 2
is that the two most frequently observed resistomes, which are
extremely similar, account for 23% of the strains. They are
therefore highly conserved and warrant further investigation.
These results will be exploited to study and understand the pool
of resistance genes present in Salmonella strains, with a focus on
strains found in fresh produce, to understand the links between
foodborne Salmonella and environmental strains with respect to
resistance genes.
LINKING GENOMIC AND CLINICAL DATA
It will be essential to match phenotypic, epidemiological and
available clinical Salmonella data (antibiotic resistance, virulence,
and anonymized clinical observations) to the genomic data
produced. We will categorize metadata in SalFoS so that isolates
can be sorted by phenotype, allowing rapid identification of
linked genomic signatures and the development of prognostic
approaches for diagnostic, epidemiology and surveillance.
We will develop tools to rapidly collate data for a given
strain type and produce a concise phenotypic and clinical
profile that provides users with an evidence-based decision-
making platform. The Canadian Food Inspection Agency,
Health Canada, Agriculture Canada, provincial public health
laboratories and the National Microbiology Laboratory-Public
Health Agency of Canada group are expected to be end-users of
the projects outcomes.
FUTURE GENOMIC AND BIOLOGICAL
STUDIES OF Salmonella
We will continuously improve SalFoS by adding Salmonella
strains, NGS data and analysis as well as experimental results.
Another aim of the Syst-OMICS consortium is to avoid
duplication of efforts in Salmonella genomics and enhance
interest from researchers having common goals. Additional
members are welcome to join in and expand on our original
Genome Canada project. We also intend to seek collaboration
with other groups to connect our database with those
developed for other Salmonella genomes. Finally, the Salmonella
Syst-OMICS project could be a model for other groups interested
in the bacterial genomics of infectious diseases, a strategy that
we are also pursuing for Pseudomonas aeruginosa (Freschi et al.,
2015).
AUTHOR CONTRIBUTIONS
J-GER, JJ, LF, IK-I and RL collected strains, performed the
analyses and drafted the manuscript. BB provided support for
sequencing and analysis. MD contributed to the development of
SalFoS. All other authors handled strains and collected metadata.
All authors revised the manuscript.
ACKNOWLEDGMENTS
We express our gratitude to members of the genomics analysis
and bioinformatics platforms at IBIS. We also acknowledge
Betty Wilkie, Ketna Mistry, Robert Holtslander and Shaun
Kenaghan from the NML Salmonella reference laboratory for
their assistance with serotyping. RL, LG, AG, ST, PD, DM,
SG, SB, FD, SW, SM, GL, INF, YJ, PT, CN, RG, JoR, JW are
funded by Genome Canada, provincial genome centers Génome
Québec and Genome BC, and the Ontario Ministry of Research
and Innovation. SM holds a Tier 1 Canada Research Chair in
Bacteriophages.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://journal.frontiersin.org/article/10.3389/fmicb.
2017.00996/full#supplementary-material
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fmicb-08-00996 May 31, 2017 Time: 15:54 # 8
Emond-Rheault et al. The Salmonella Syst-OMICS Project
Conflict of Interest Statement: The handling Editor declared a shared affiliation,
though no other collaboration, with the authors ST and AG, and the handling
Editor states that the process met the standards of a fair and objective review.
The other authors declare that the research was conducted in the absence of any
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of interest.
Copyright © 2017 Emond-Rheault, Jeukens, Freschi, Kukavica-Ibrulj, Boyle,Dupont,
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