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The EpiQuant framework for computing the epidemiological
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concordance of microbial subtyping data
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Benjamin M. Hetman,a,b* Steven K. Mutschall,b James E. Thomas,a Victor P. J. Gannonb,
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Clifford G. Clarkc, Frank Pollarid, Eduardo N. Taboadab#
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Department of Biological Sciences, University of Lethbridge, Lethbridge, Alberta,
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Canadaa; National Microbiology Laboratory at Lethbridge, Public Health Agency of
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Canada, Lethbridge, Alberta, Canadab; National Microbiology Laboratory at Winnipeg,
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Public Health Agency of Canada, Winnipeg, Manitoba, Canadac; Centre for Foodborne,
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Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada,
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Guelph, Ontario, Canadad
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Running title: Computing epidemiological concordance of typing data
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#Address correspondence to Eduardo N. Taboada,
[email protected]
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*Present address: Department of Population Medicine, Ontario Veterinary College,
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University of Guelph, Guelph, Ontario, Canada
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Abstract
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A fundamental assumption in the use and interpretation of microbial subtyping
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results for public health investigations is that isolates that appear to be related based on
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molecular subtyping data are expected to share commonalities with respect to their
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origin, history and distribution. Critically, no approach currently exists for systematically
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assessing the underlying epidemiology of subtyping results. Our aim was to develop a
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method for directly quantifying the similarity between bacterial isolates using basic
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sampling metadata and to develop a framework for computing the epidemiologic
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concordance of microbial typing results.
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We have developed an analytical model that summarizes the similarity of
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bacterial isolates using basic parameters typically provided in sampling records using a
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novel framework (EpiQuant) developed in the R environment for statistical computing.
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We have applied the EpiQuant framework to a dataset comprising 654 isolates of the
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enteric pathogen Campylobacter jejuni from Canadian surveillance data in order to
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examine the epidemiological concordance of clusters obtained using two leading C. jejuni
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subtyping methods.
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The EpiQuant framework can be used to directly quantify the similarity of
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bacterial isolates based on basic sample metadata. These results can then be used to
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assess the concordance between microbial epidemiologic and molecular data, facilitating
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the objective assessment of subtyping method performance, paving the way for the
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improved application of molecular subtyping data in investigations of infectious disease.
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Keywords
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Campylobacter jejuni; molecular typing; genome sequencing; molecular epidemiology;
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epidemiological concordance.
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Introduction The analysis of pathogens through the application of techniques adapted from
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molecular biology has become an essential part of many modern epidemiological
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investigations (i.e. ‘molecular epidemiology’) targeted at the prevention and control of
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infectious disease and improving our understanding of how infectious disease agents
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circulate between/within natural reservoirs and affected populations (1, 2). Molecular
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subtyping of bacteria allows for the differentiation between closely related isolates from
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the same species, and can be instrumental in determining if an isolate forms part of an
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epidemiologically linked cluster. However, an ongoing challenge in molecular
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epidemiology has been the effective interpretation of subtyping data. While subtyping
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results connect isolates into groups related by molecular or phenotypic criteria (i.e.
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clusters), it is not generally known to what extent these clusters correspond to the
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underlying epidemiology of the pathogen.
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Assessment of the epidemiological relevance of isolates sharing a molecular
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subtype has typically been carried out manually, based on the aims of the analysis.
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Clusters of genetic or phenotypically related isolates are produced using one or more
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molecular subtyping methods, and relevant epidemiological attributes, such as
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membership to an outbreak group, are superimposed and subjected to interpretation on a
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cluster-by-cluster basis, with additional context such as subtype reproducibility, subtype
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prevalence, and subtype variability in the organism also considered (3–5). While this
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general approach represents a pragmatic solution to the need for interpretation criteria
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based on epidemiological relevance, it lacks the systematic rigor required to
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comprehensively assess subtyping results and their concordance with underlying
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characteristics related to the ecology and epidemiology of the bacterial isolates in
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question. In light of the important role of molecular typing in public health investigations,
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it becomes necessary to develop analytical approaches to systematically assess this
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relationship.
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In this study, we present a model for computing the similarity between bacterial
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isolates based on attributes commonly documented within isolate sampling records (e.g.
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source, time and geography of sampling) and the development of a framework for
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assessing the concordance between the ‘epidemiologic signal’ of bacterial isolates and
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their subtyping data. We assess the utility of this framework on a dataset of 654 isolates
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of the important zoonotic pathogen Campylobacter jejuni sampled from across Canada
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and demonstrate how the model can be used to: a) quantify the epidemiological similarity
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between C. jejuni isolates; b) assess the relative ability of subtyping methods to cluster
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isolates into cohesive epidemiologically -linked groups; and c) identify subtype clusters
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with significantly increased specificity to the underlying epidemiology of bacterial
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isolates, facilitating targeted epidemiological investigations.
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Materials and Methods
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1. Description of the EpiQuant model for computing the epidemiologic distance (∆Ɛ)
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The geography of a sample from which a bacterial isolate was recovered, the time
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or date of sampling, and the source of a sample (i.e. the specific reservoir or vehicle)
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represent three common metadata descriptors that can be used for broadly describing the
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ecologic epidemiology (i.e. the “ecological address”) of a bacterial isolate. In our model,
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the “epidemiologic type” (Ɛ) of a bacterial isolate is described by its position in a three-
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dimensional space defined by geospatial (g), temporal (t), and source (s) variables and is
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thus expressed by the vector in Equation 1. =( , , )
(1)
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A calculation of the “epidemiological distance” between any two isolates can then be
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defined by a combination of these three distances. A formula expressing the Euclidean
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distance between the respective vectors is therefore represented by: (2)
(∆ ) + (∆ ) + (∆ )
Δ = 95
where Δg, Δt, and Δs represent the pairwise geospatial, temporal and source distances
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between the sampling parameters of two isolates, and γ, τ, and σ represent adjustable
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coefficients for assigning relative contributions to each component based on a priori
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considerations of data granularity, reliability or importance. Substituting derivations for
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Δg, Δt, and Δs into Equation 2 yields our final model for summarizing the
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epidemiological distance between any two bacterial isolates:
∆ =
((log
) )+
log
(
−
)
+
6
1−
1
( ,
)
(3)
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where (distab) is the physical distance, in kilometres, between sampling locations for each
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isolate; (x, y) represent the sampling time of each pair of isolates, rounded to the nearest
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day; and f(vi, ui) is a function for comparing sampling sources in a conceptual model
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describing the transmission of C. jejuni (Figure 1) using a set of epidemiologic attributes
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and the scoring rubric used to compare them (Figure 2). Finally, the “epidemiologic
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similarity” between two isolates is defined as 1 – the epidemiological distance (i.e. 1 –
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∆Ɛ). A detailed rationale and derivation of the various components in the complete model
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is presented in supplementary file S1.
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2. Strain selection for assessing the EpiQuant model The majority (n = 490) of Campylobacter jejuni isolates included in this study
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have been described previously (6, 7). These isolates were sampled from a wide range of
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agricultural, environmental, retail, and human clinical sources by the FoodNet Canada
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enteric disease surveillance network (formerly C-EnterNet) and analyzed using
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Comparative Genomic Fingerprinting (CGF) (6) and Multi-Locus Sequence Typing
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(MLST) (8). Additional C. jejuni isolates were added to this study so as to cover a wider
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range of geospatial, temporal, and source parameters. These included further isolates
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collected by FoodNet Canada (n = 42), as well as those collected as part of various
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sampling initiatives from Southern Alberta, British Columbia, Ontario, Quebec, and New
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Brunswick, Canada (n = 122). All additional isolates were selected from the Canadian
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Campylobacter Comparative Genomic Fingerprinting Database (C3GFdb) on the basis of
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their CGF fingerprint and sampling metadata. The C3GFdb is a pan-Canadian collection
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of over 22,000 Campylobacter isolates from human clinical, animal, and environmental
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sources analyzed by CGF.
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3. DNA extraction and whole-genome sequencing Whole-genome sequencing was performed on the isolates used to supplement our
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original dataset (n = 164) in order to derive in silico MLST profiles. Isolates were
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recovered from archival glycerol stocks (60% glycerol in phosphate-buffered saline
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stored at -80°C). Stocks were streaked for isolation onto modified cefoperazone charcoal
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deoxycholate agar (mCCDA, Oxoid CM0739, with selective supplement SR0155E) and
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monocultures were incubated for 24-48 hours in a tri-gas microaerobic environment
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(MAE, 10% CO2, 5% O2, 85% N2) at 42°C. Single colonies were selected and spread to
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blood agar plates (BBL Blood Agar base, BD 211037, 5% sheep blood) and incubated
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overnight in MAE prior to harvesting biomass. Genomic DNA extractions were
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performed using the QIAgen genomic tip 20G kit according to the manufacturer’s
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recommendations. Quantity and integrity of genomic DNA was assessed using the Quant-
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IT HS fluorometric assay (Life Technologies Q-33120) and gel electrophoresis on 0.8%
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agarose, respectively.
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Paired-end tagged libraries were prepared at the National Microbiology
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Laboratory (Winnipeg, Manitoba), and sequenced on the Illumina MiSeq platform using
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150bp reads. Approximately 30 isolates were pooled per run yielding, on average, 80-100
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fold coverage per isolate. Draft genome assemblies were assembled de-novo using the St.
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Petersburg Academy genome assembler (SPAdes version 3.5.0) (9) and selecting a k-mer
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length of 55, as this provided a consistent quality of assemblies across the dataset.
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4. In-silico typing of draft genome assemblies In order to derive molecular typing results from the WGS data, the “Microbial In
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silico Typing” (MIST) software was used (10). Developed by our group, MIST is an
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analytical typing engine that enables the user to simulate molecular subtyping results
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based on a series of user-defined sequence homology searches against draft genome
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sequence assemblies. For the generation of in silico MLST results, we subjected our
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collection of draft genome assemblies to sequence queries using MLST allelic sequences
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available from the BIGSdb server, hosted at the Campylobacter pubMLST website (11,
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12). Clonal complexes (CC) and sequence types (ST) were determined based on
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assignments from pubMLST. A small number of isolates (n=21) had novel alleles and
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were excluded from ST-based analyses.
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5. Applying the EpiQuant model framework to isolates of C. jejuni All calculations used in the analyses for this study were performed in the R
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environment for statistical computing (13) using a set of custom scripts available for
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download at www.github.com/hetmanb/EpiQuant_Typing_Analysis. Pairwise distance
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matrices for Δg, Δt, and Δs were combined as described in Equation (3) to yield a final
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epidemiologic distance (ΔƐ) matrix for all isolates used in the study. To facilitate
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exploration of the EpiQuant framework including various parameters used to calculate
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the ΔƐ statistic, an interactive web application was developed using the R Shiny web
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application framework for R (http://shiny.rstudio.com/), available for download at
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https://github.com/hetmanb/EpiQuant. A live demonstration of the site is also available at
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https://lfz.corefacility.ca/shiny/EpiQuant/.
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A two-dimensional neighbour-network was generated from a matrix of source
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distances using the ‘neighborNet’ function from the ‘phangorn’ package in R (14), and
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edited for visual clarity using the SplitsTree4 program (15). Heatmaps were generated in
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R using the heatmap.2 function from the package Gplots (16) and applying single-linkage
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clustering. The geospatial component of the dataset had partial data (i.e. defined at the
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level of province only) for 63 entries; we assessed these locations as a general provincial
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location based on Google Maps GPS data, (e.g. ‘Ontario, Canada’).
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6. Assessing the epidemiologic relevance of C. jejuni subtyping data We defined the “Epidemiologic Cluster Cohesion” (ECC) of subtyping clusters as
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the mean pairwise epidemiologic similarity for all isolates within a subtype cluster. The
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ECC statistic was used as a measure of the epidemiological concordance (i.e. the
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epidemiological relevance) of subtyping clusters, with a high ECC representing clusters
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with increased epidemiologic specificity (i.e. sampled from a similar time, location and
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source) and a low ECC representing groups of isolates sharing the same subtype despite
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varied epidemiologic profiles. Singleton clusters (e.g. clusters containing only one
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isolate) were not included in the ECC analysis but were used to compute the background
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ECC signal of non-clustered isolates to use as basis for comparison to the ECC of various
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subtyping clusters. Group comparisons for ECC values of isolates were performed in R
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using an analysis of variance (“aov”) with follow-up Tukey honest significant-difference
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testing (“TukeyHSD”), all tests were performed at the α = 0.05 level of significance. To
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identify outliers from a boxplot analysis of CGF subtyping data, we performed a typical
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Tukey outlier analysis, where subtype clusters with ECC > Q3 + (1.5*IQR) or ECC < Q1
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– (1.5*IQR) were determined to be statistical outliers (Q1, Q3 = first and third quartiles;
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Interquartile Range (IQR) = Q3 – Q1).
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Results
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1. Development of a model for computing source similarities using C. jejuni isolates
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from the Canadian CGF database. Sources for comparison were selected using
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available sampling information from the Canadian Campylobacter Comparative Genomic
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Fingerprinting Database (C3GFdb), a repository containing curated metadata on over
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22,000 Campylobacter isolates for which the granularity has been kept largely consistent,
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simplifying the process of identifying non-redundant sources (n=40) to test our method
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for computing source similarities.
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Developing a rubric for comparing Campylobacter sampling sources from the
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C3GFdb involved describing the epidemiological profile of each source using a series of
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attributes constructed from a conceptual framework that outlined major environments and
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interactions we believe to be important in the C. jejuni transmission chain (Figure 1).
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Each source was then assessed independently against these attributes and the distance
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between any two sources was computed by comparing their respective epidemiological
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profiles, with the pairwise source similarity based on the number of matching and
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partially matching epidemiological attributes as a proportion of the total number of
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attributes examined (n=25). An example of the rubric used to assess the unique source
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identifiers against epidemiologically relevant attributes can be seen in Figure 2.
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Pairwise comparison of the epidemiologic profiles derived for each source using
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our rubric resulted in a matrix summarizing the overall “source distance” between all
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sources used in this study. We constructed a Neighbour-Network splits graph (Figure 3)
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based on the source distances in order to confirm whether the resulting source matrix was
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congruent with our conceptual representation of Campylobacter transmission networks.
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Clustering results from the splits graph demonstrated significant agreement with those
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proposed in our original conceptual framework. For example, entries related to farm food
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animal sources – (A) food animals and (B) meat products and abattoir samples – grouped
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in the same area of the network and these grouped separately from farm-based
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companion animals (C) and a group comprised of domestic companion animals and wild
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animals straddling the urban/rural environment (D). A separate region of the network
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included groups directly related to environmental and human inputs (E-F). A group of
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farm-animal related environmental sources (G) were found to group midway between the
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environmental-water related sources in (E) and farm-animal sources in (A), consistent
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with the dual nature of the source input. While major groupings were readily identified
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by the splits graph in Figure 3, a considerable amount of reticulation, or ’splits’ were
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observed, and this is consistent with shared characteristics between sources not derived
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from the same principal headings (i.e. “Human,” “Animal,” or “Environmental”) used to
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construct the rubric in Figure 2.
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To further examine the effect of shared epidemiological attributes on the overall
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pairwise source comparison, we constructed an hierarchically clustered heatmap
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illustrating the similarity between all pairwise sources (Figure 4). This visualization
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yielded several epidemiologically relevant groupings consistent with those observed in
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Figure 3; at the same time, areas of similarity away from the 45° (i.e. “self vs. self”) axis
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of Figure 4 reflect epidemiological relationships that lie outside of the major groupings
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outlined in the splits graph analysis. An example of this can be seen within Cluster F,
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which is comprised of food animal sources from farm through to retail levels. A sub-
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group of farm-based poultry sources (i.e. goose, duck, chicken, turkey) within this cluster
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displays high secondary similarity to other on-farm food-animal sources (i.e. cow, pig,
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goat, sheep) and to poultry sources at the abattoir and retail levels. Results from the
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source model can also be seen to delineate between similar sources that differ at a small
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number of attributes based on differences in likely primary exposures to C. jejuni. For
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example, of the three human sources in Cluster C, the ‘Human_Urban’ source exhibits
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higher similarity towards animals with urban exposure (e.g. companion animals,
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raccoons, seagulls, deer) and retail food sources; ‘Human_Farm Workers’ demonstrates
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higher similarity towards on-farm food animals; and ‘Human_Abattoir Workers’ express
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strong similarity to abattoir and retail-based animal sources.
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2. Combining components to compute epidemiological distance (∆Ɛ ). An example of
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the total epidemiologic distance for all isolates in our dataset (n = 654) was derived from
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the application of our model for calculating ∆Ɛ (i.e. Equation 3) using sample metadata
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and is presented in Figure 5. In combining the “source distances” previously described
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with geographic positioning data (GPS) and collection dates using weighting ratios of
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50%, 30% and 20% for σ, τ, and γ coefficients, respectively, a pairwise matrix describing
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the total epidemiologic distance of all isolates from our dataset of 654 C. jejuni was
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created. The adjustable coefficients γ, τ, and σ are used for assigning weights to each
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component based on a priori epidemiological considerations. For example, a bacterial
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species known to be highly source-restricted may then require a higher value for σ to
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provide additional weight to the source relative to the geospatial and temporal variables,
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to account for the increased significance when observing a difference in the source.
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In general, the groups that resulted from clustering based on ∆Ɛ represented cohesive
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epidemiologic units comprised of bacterial isolates from similar source, temporal and
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geospatial cohorts.
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For example, Cluster 1 comprised 282 Human Clinical isolates of C. jejuni from
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Ontario, Canada, with further sub-clustering based on distances between sampling dates,
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ranging from January 2006 to November 2008. Within this cluster is a subset of 43
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human clinical isolates collected during a four-week period in the Summer of 2007
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(Figure 5A, highlighted in blue). These also include a set of 24 isolates that were
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confirmed epidemiologically to belong to an outbreak cluster. As seen in Figure 5B, the
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outbreak isolates shared identical temporal, location and sampling source data, and thus
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cluster together with an average epidemiologic similarity (1 - ∆ε) of 1. Isolates collected
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within the same municipality and a similar time-frame that were not part of the outbreak
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are shown to cluster separately, with epidemiologic similarities ranging from 0.87 to
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0.99. Cluster 2 included isolates derived from Raccoon sources in Ontario across a
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narrow sampling time (October 2011 – July 2012). Cluster 3 comprised farm-based food
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animal isolates sampled from various locations across Alberta, Canada in the years 2004-
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2006. Cluster 5 contained isolates sampled from animal sources at both the farm and
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retail levels with sub-clusters delimited by their source and sampling locations (e.g.
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“Chicken@Retail” samples from British Columbia, Canada, and “Cow@Farm” samples
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from Alberta, Canada) as well as the sampling dates, which ranged from 2009 to 2012.
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Cluster 7 included isolates sampled from environmental sources (e.g. “Water@Irrigation
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Ditch”) and this is consistent with results from the pairwise source analysis (Figures 2
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and 3), which suggests that environmental sources form a distinct group separate from
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animal and human sources. Cluster 8 comprised most of the food-animal related isolates
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in the dataset: all isolates contained within this cluster were derived from retail or farm
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animal sources, and encompass a close geographic range from within Ontario, Canada.
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As was observed with the pairwise source similarity matrix (Figure 4), a
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considerable amount of secondary similarity can be seen off the 45° axis due to partial
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similarity across some, but not all, components. For example, Clusters 1 (human) and 8
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(food-animal) share significant secondary similarity due to the shared geospatial and
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temporal components of the isolate subsets.
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3. Using ∆Ɛ to assess the epidemiologic concordance of subtyping methods. We
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wished to investigate the use of the epidemiological similarity (i.e. 1 - ∆Ɛ) between two
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isolates estimated by our framework, as a means to quantify the epidemiological
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concordance of subtyping methods. Using Multi-Locus Sequence Typing (MLST) and
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Comparative Genomic Fingerprinting (CGF) data from our collection of 654 C. jejuni
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isolates, we computed the Epidemiologic Cluster Cohesion (ECC), the average pairwise
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epidemiologic similarity for each subtyping cluster in the dataset and compared the ECC
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values obtained with each method. Furthermore, as CGF has been shown to have greater
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discriminatory power than MLST (6) and MLST data can be analyzed at two levels of
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resolution, Clonal Complex (CC) and Sequence Type (ST), these data were also used to
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investigate the epidemiologic concordance as a function of a method’s discriminatory
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power.
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When compared to the average ECC of isolates not belonging to clusters (0.471 ± 0.165), we observed that each subtyping method assembled isolates into clusters with
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higher average ECC (p < 0.001) and that higher resolution methods resulted in increasing
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overall ECC values. the lower resolution subtyping method (i.e. CC) assembled isolates
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into larger clusters with a lower overall ECC (0.486 ± 0.183) when compared to higher
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resolution methods (i.e. MLST and CGF), which generated several smaller clusters from
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the CC assignments and these had higher overall ECC values (ST: 0.505 ± 0.197; CGF:
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0.543 ± 0.223; p < 0.001), which is consistent with the increased epidemiological
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concordance of clusters obtained with the higher resolution methods. To illustrate this
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observation, isolates from the nine largest CC in our dataset (n=516) are presented in
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Figure 6, with each subplot illustrating a single CC and its splitting into several smaller
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ST and CGF subtyping clusters that tend to exhibit higher ECC than the original parent
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cluster.
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4. Adjusting ∆Ɛ parameters to identify subtyping clusters with differing
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epidemiological characteristics. To demonstrate the flexibility of the EpiQuant model
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for assessing the epidemiologic cohesion of subtyping clusters based on the differential
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weighting of geospatial, temporal, and source parameters, we computed ∆Ɛ for all
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isolates in the dataset based on two additional sets of inputs for γ, τ, and σ coefficients.
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The first iteration favoured relationships based on source relationships (e.g. 80% source,
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10% temporal and 10% geospatial weightings); and a second emphasised temporal
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associations (e.g. 10% source, 80% temporal, and 10% geospatial weightings). Combined
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with the original ∆Ɛ results from Figure 5, we then applied these data to compute the
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ECC of CGF subtypes in our dataset in an attempt to identify clusters that were highly
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source or temporally specific.
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Results from the ECC analysis of CGF subtyping data reveal differences in the
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distribution of ECC values observed for CGF clusters based on the input coefficients
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used (Figure 7A). The ECC distributions show that favouring temporal interactions
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results in a significantly lower average ECC value (0.458 ± 0.173) compared to those
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calculated with a higher emphasis on source relationships (0.640 ± 0.124; p < 0.001), or
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when the “balanced” coefficient set was used (0.564 ± 0.141; p = 0.003). This
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observation is consistent with the wide distribution of temporal signal in the dataset (i.e.
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sampling years from 2004 to 2012). No significant difference was observed between the
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overall ECC achieved when comparing “source” versus “balanced” approaches.
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Outliers were identified with significant source and temporal associations based
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on all three sets of coefficients. To confirm whether the ECC results were indeed
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reflecting highly biased temporal or source associations, we examined the metadata for
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each of these outlier subtypes (Table 1). For example, subtype “0082.001.001” was
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associated with a single source type (“Human_Urban”), yielding a high ECC value when
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assessed favouring the source component despite a temporal range spanning seven
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months. By contrast, subtypes “0609.011.003” and “0891.001.001” were identified as
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being highly specific temporally due to short sampling periods (8 and 17 days,
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respectively) and had high ECC values when assessed favouring the temporal component
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despite being associated with multiple sources. Subtypes “0592.006.003” and
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“0926.002.004” were identified as being both highly temporal and source specific based
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on high ECC values obtained under both sets of coefficients. The metadata for both of
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these clusters revealed a single sampling source collected within a narrow window in
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time (e.g. “Pig@Farm” with a 14-day sampling period and “Human_Urban” with a ten
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day time period, respectively).
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Discussion Molecular subtyping techniques have become an essential part of modern
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epidemiological investigations of infectious disease. Subtyping data have been used to
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identify outbreaks and their vehicles of transmission (17–23), to study the dynamics of
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pathogen circulation throughout natural reservoirs (24–26), and to assess the population
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structure of bacterial disease agents, identifying subgroups important to human health
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(27–29). A consistent feature in the evolution of the field of molecular epidemiology has
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been the continuing development and refinement of approaches for molecular typing. In
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general, the drive for novel methods has been motivated by the search for improvements
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in performance criteria such as discriminatory power and deployability (6, 30, 31), and by
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the mitigation of problems that can arise when adapting a given subtyping method to a
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particular pathogen of interest (32–34). Coupled with continuing technical advances in
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molecular biology, the search for approaches useful for distinguishing and classifying
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bacterial strains has led to the development of a large number of subtyping methods now
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available (35, 36).
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A significant challenge with the emergence and proliferation of new molecular
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typing methods has been the lack of systematic approaches for objectively assessing and
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comparing methods. In 2006, Carriço et al. described a framework for quantitatively
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assessing different typing systems using performance criteria such as discriminatory
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power and partition congruence (37). We have previously used this framework for
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comparing the performance of CGF, a novel method for C. jejuni subtyping developed in
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our group, to MLST, the leading method for C. jejuni subtyping (6) and for assessing
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both methods against the phylogenetic signal in whole-genome sequence data (38). This
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approach has been useful for assessing the concordance of methods against one another,
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which is particularly useful when comparing a novel method to a well-established ‘gold
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standard’. Critically, although subtyping data are used in the context of epidemiological
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investigations, the epidemiological concordance of subtyping results is an element that
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has escaped systematic examination.
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The ‘Tenover criteria’, which were introduced over two decades ago, have
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provided guidance on the interpretation of results generated using pulsed-field gel
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electrophoresis (PFGE) (39). It is generally acknowledged that subtyping data must be
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interpreted in the proper epidemiological context (i.e. epidemiological relevance) while
388
taking into consideration additional factors such as reproducibility of the method with a
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particular organism, genotypic variability of the organism being subtyped, the prevalence
390
of the pattern in question, and outbreak characteristics (3). To date, many studies have
391
been performed comparing the results of molecular typing with epidemiologic metadata
392
using manual methods: once genetic relationships between isolates are determined via
393
subtyping, epidemiological data are examined in an attempt to assess whether subtyping
394
clusters are consistent with the underlying epidemiology (26, 28, 40, 41). More recently,
395
visualizations based on mapping colour-coded epidemiological meta-data onto
396
dendrograms derived from sub-typing data have been used to facilitate this assessment
397
(4). While such approaches have been extremely successful for identifying subtyping
398
clusters related to particular epidemiologic considerations, a major disadvantage is that
399
they are qualitative and require significant manual interpretation, making them
400
impractical for the systematic examination of large datasets.
21
401
In this investigation, we have focussed on: a) establishing an approach for
402
summarizing the epidemiologic signal in sampling metadata from C. jejuni isolates; b)
403
developing a method for computing the epidemiological similarity between pairs of C.
404
jejuni isolates; and C) developing a framework for evaluating the epidemiological
405
concordance of subtyping data to compare the performance of two leading methods of C.
406
jejuni subtyping. As a high-priority foodborne pathogen primarily associated with
407
sporadic illness and a number of possible sources, C. jejuni poses significant challenges
408
to analyses based on descriptive epidemiological parameters alone (42). Moreover,
409
although temporal and geospatial data figure prominently in epidemiological
410
investigations of C. jejuni, sampling source is a parameter that has been shown to
411
contribute significantly to genotypic variation (43). As no means of measuring the
412
similarity of sampling sources currently exists, C. jejuni presents an excellent, if
413
complex, model organism for which to establish a model for source-source comparisons.
414
We first developed a conceptual framework incorporating major routes of
415
transmission for the spread of C. jejuni throughout various sources and vectors in the
416
farm-to-fork continuum. This exercise enabled us to identify basic attributes to be used
417
for computing similarity estimates between non-identical sampling sources using a
418
uniform set of epidemiologically relevant comparators; to our knowledge, this is an
419
approach that has no antecedent. Results from the splits graph analysis show general
420
agreement with the conceptual framework and serve to demonstrate the epidemiological
421
hierarchy achieved by the rubric despite these sources sharing many of the same
422
attributes. While our estimation of important attributes by no means encompasses the
423
entirety of Campylobacter epidemiology, an examination of pairwise similarity between
22
424
sources provided supporting evidence that we have managed to capture enough
425
information with our rubric to describe logical relationships between many
426
Campylobacter sampling sources, while maintaining secondary associations where
427
underlying epidemiologic similarity exists between sources that are less likely to interact
428
directly. As some of the attributes used in the current study have general applicability to
429
other organisms, this approach could be extended to other bacterial infectious disease
430
agents. However, this would require a careful examination of the transmission pathways
431
between reservoirs based on a review of the relevant literature and user knowledge.
432
A major aim of this study was the development of a method for computing an
433
estimate of the epidemiologic similarity between bacterial isolates based on common
434
descriptive metadata contained within sampling records. A unique quantitative summary
435
statistic (Ɛ) that comprised multiple layers of epidemiologic data (e.g. source, time and
436
geography of sampling) was used to estimate the epidemiologic similarity of isolates (1 -
437
ΔƐ) in a manner that is consistent, systematic, and scalable to entire databases. In this
438
study, we have used this approach to systematically examine a dataset comprised of 654
439
C. jejuni isolates and show that this approach can be used to derive pairwise
440
epidemiologic similarity estimates that are consistent with the underlying sampling meta-
441
data, generating similarity values that approach unity on isolates that share a common
442
source, location and date of sampling, as in the case of isolates from a confirmed
443
outbreak of campylobacteriosis.
444
We have also used this metric to compute the Epidemiologic Cluster Cohesion
445
(ECC), a reflection of the average epidemiologic similarity of isolates sharing a
446
molecular subtype.
23
447
Calculating the ECC provides an avenue for assessing the performance of a subtyping
448
method based on epidemiological concordance that can be performed independently of
449
other typing methods; our proposed approach also allows for the systematic examination
450
of the epidemiological relevance of individual clusters generated by any molecular typing
451
method.
452
A key driver in the development of novel molecular typing methods is higher
453
discriminatory power. By assigning isolates into smaller clusters, methods with higher
454
discriminatory power are expected to reduce the likelihood that non-epidemiologically
455
related isolates will share the same subtype, thus improving epidemiological
456
concordance. In previous work, we have shown that CGF provides higher discriminatory
457
power than MLST while maintaining high concordance to group memberships
458
established by the MLST method (6, 7). By subjecting our dataset of 654 C. jejuni
459
isolates to both MLST and CGF and comparing the ECC of clusters generated by
460
subtyping methods with increasing resolution (i.e. CC < ST < CGF), we aimed to test the
461
hypothesis that strain typing methods with higher resolution would separate isolates of C.
462
jejuni into clusters demonstrating higher epidemiological concordance. Our results
463
indicate the ability of CGF and ST to resolve large clusters produced using CC into
464
smaller, more refined clusters with greater epidemiologic concordance, as indicated by a
465
higher overall ECC. It is important to note that although clusters with ECC values
466
approaching unity might appear to be optimal, they necessarily represent groups of
467
isolates with singular temporal, geospatial, and source signal. In the context of infectious
468
disease epidemiology, however, ECC values that deviate significantly from unity are
24
469
expected due to transmission and survival of subtypes across a wide range of sampling
470
dates, locations, and sources.
471
An inherent strength of our model is the flexibility afforded in the inputs for γ, τ,
472
and σ, which can be used to modify the contribution accorded to geospatial, temporal and
473
source components, respectively. Adjusting the coefficients in the calculation of ∆Ɛ can
474
be used to limit the signal resulting from unreliable or incomplete data, but should also
475
allow for more targeted analyses, such as facilitating the identification of subtypes with
476
above-average source, temporal or geospatial associations. In our original analysis, we
477
combined source, temporal, and geospatial distances using a 50:30:20 ratio, respectively,
478
in order to achieve results that reflected all three components of the model, with non-
479
equal weighting of the coefficients to reflect the importance of source to C. jejuni
480
epidemiology and to reflect the decreased granularity of geospatial data in our dataset.
481
When the ECC was re-calculated with heavily adjusted σ, τ, and γ percentages favouring
482
source or temporal associations, the overall ECC decreased when temporal signal was
483
emphasized, consistent with the wide temporal range spanned by the isolates in the
484
dataset (2004-2012). An analysis of the outliers revealed certain CGF subtypes that
485
produced very high ECC when considering source, or temporal signal as the primary
486
metric for evaluation. Thus, by modifying the contribution to ∆Ɛ from the various
487
parameters in our model, it is possible to adjust the resulting ECC estimates. In a point-
488
source outbreak investigation, for example, it may be more suitable to negate the source
489
component of the model entirely, in favour of high temporal and geospatial similarities;
490
this would emphasize groups of isolates collected together in the same place and time,
491
potentially allowing for the identification of non-human sources of exposure sampled
25
492
during the time course of a confirmed outbreak. By contrast, adjusting the coefficients to
493
favour source or geospatial relationships could be better suited to performing source
494
attribution, or for the identification of pathogens endemic to particular geographic
495
regions, respectively.
496
Recently, technologies for evaluating the whole genome sequence (WGS) of
497
bacterial isolates have become widely available and it is likely that the increasing
498
adoption of WGS will result in the concomitant phasing out of molecular typing methods
499
in the near future. Analysis of WGS data offers unparalleled discriminatory power for
500
comparing bacterial isolates, while also providing a wide range of analytical options (e.g.
501
analysis of single nucleotide polymorphisms; gene-by-gene sequence-based typing; gene
502
content analysis) that facilitate in silico comparisons with legacy datasets (38, 44).
503
Furthermore, WGS-based analysis has become sufficiently cost-effective to allow an
504
increasing number of public health laboratories to focus their efforts on the generation of
505
WGS for isolates collected through routine surveillance (45) resulting in an explosive
506
growth in the number of isolates being analyzed and the concomitant phasing out of
507
molecular typing methods in the very near future. In this context, the potential utility of
508
the framework proposed here resides in the scalability of a scriptable, systematic
509
approach that allows for efficient and automatable computation of epidemiologic signal,
510
epidemiological similarity, and epidemiological concordance across very large datasets
511
and the flexibility to support different epidemiological applications.
512
By facilitating the direct comparison of genomic information of bacterial isolates
513
with their underlying epidemiology, our framework provides an epidemiological basis for
514
systematically assessing and interpreting the results obtained from both molecular and
26
515
WGS-based analyses, which will help improve the optimization of novel genomic
516
approaches in the emerging field of genomic epidemiology.
517 518 519
Conclusions In the rapidly evolving field of molecular epidemiology, improved measures for
520
assessing the genetic similarity of bacterial isolates need to be balanced with equally
521
improved measures for assessing strain epidemiology that allow for direct comparison
522
between the two. Here we have presented a simple model for the quantitative assessment
523
of similarities of human bacterial pathogens based on a comparison of their descriptive
524
sampling attributes. Using a test dataset of Canadian C. jejuni spanning a wide range of
525
sampling sources, times, and locations, we have demonstrated that deriving inter-strain
526
relationships based on basic epidemiological metadata results in highly structured groups
527
of isolates that conform to a natural, cogent organization. Moreover, by transforming a
528
set of descriptive qualifiers into a quantitative epidemiologic summary, we show that this
529
metric can be used towards assessing the epidemiological relevance of subtyping
530
methods as a means of systematically evaluating subtyping method performance.
531 532 533
Acknowledgments The authors wish to thank the Genomics Core Facility at the National
534
Microbiology Laboratory, Winnipeg for assistance with sequencing of C. jejuni isolates.
535
Funding for this project was provided through the Government of Canada’s Genomics
536
Research and Development Initiative. This work would not have been possible without
537
the collaboration of FoodNet Canada and its provincial public health partners and the
27
538
many contributors to the Canadian Campylobacter Comparative Genomic Fingerprinting
539
database (C3GFdb).
540
28
541
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35
701
Figure Legends:
702
Figure 1. Conceptual framework outlining major environments and interactions in
703
the C. jejuni transmission chain. The model incorporates all C. jejuni sampling sources
704
in the Canadian Campylobacter Comparative Genomic Fingerprinting Database
705
(C3GFdb) used in the analysis of source distances. Arrows indicate either unidirectional
706
or bidirectional flow of C. jejuni throughout the ‘farm-to-fork’ continuum. Sources not
707
located on one of the four “ecological islands” are considered transitory, and have high
708
exposure to multiple environments.
709 710
Figure 2. An example of the ‘Epidemiological Rubric’ used to assess source
711
comparisons. The epidemiological profile for each of 40 unique sources from the
712
C3GFdb was determined independently based on a set of 25 epidemiological attributes
713
derived from the conceptual framework presented in Figure 1. The character state of “1”
714
was used to indicate strong association, “*” partial association, and “0” little, or no
715
association to the attribute indicated in each column, where the status reflects the
716
perceived strength of the relationship based on the user's knowledge . The distance
717
between any two sources was computed by comparing their respective epidemiological
718
profiles, with the pairwise source similarity based on the number of matching and
719
partially matching epidemiological attributes as a proportion of the total number of
720
attributes examined (n=25).
721 722
Figure 3. Neighbour-network splits graph based on C. jejuni source distances. The
723
splits network was calculated based on the model for source distances (∆s) in the model.
36
724
Clusters A-G are highlighted to show clustering of highly similar sources into the same
725
regions of the graph. Distances were calculated in R using the phangorn package and a
726
splits graph was then plotted using the Splitstree software using the equal angle method.
727 728
Figure 4. All versus all heat map depicting pairwise similarities between C. jejuni
729
sources included in this study (n = 40). Source similarities were given by the formula (1
730
- ∆s). Darker shading indicates stronger similarity between sources. Groups A-F
731
represent memberships created at a clustering threshold of 50% (red line to the left of the
732
figure). Dotted black boxes are used as a visual aid in the analysis to highlight secondary
733
similarities observed off the “self vs. self” axis. The heatmap was created in R using
734
custom scripts and the heatmap.2 function from the gplots package.
735 736
Figure 5. Hierarchical clustering of C. jejuni isolates based on total epidemiological
737
distance (∆ε) computed using the EpiQuant framework. (A) Clustering of complete
738
set of isolates used in this study (n=654). Darker shading indicates higher similarity
739
between isolates based on the comparison of their sampling metadata (source, temporal,
740
geospatial) and the ∆Ɛ calculation outlined in Equation 3 using source, temporal, and
741
geospatial coefficients of 0.5, 0.3, and 0.2, respectively. A histogram displaying the
742
frequency of pairwise ∆Ɛ values observed, ranging from 0 (completely similar) to 1
743
(completely dissimilar), is shown (top-left). (B) Human clinical isolates collected in the
744
same municipality within a 4-week period, including those from a confirmed
745
campylobacteriosis outbreak (also highlighted in blue in panel A) are shown along with
746
their basic meta-data. The heatmap was generated in R using the heatmap.2 function from
37
747
the package ‘gplots’. Clustering of the resulting distances was done using the ‘single
748
linkage’ algorithm, and clusters were identified at the 50% threshold (dotted red line to
749
the left of the figure).
750 751 752
Figure 6. Comparison of Epidemiologic Cluster Cohesion (ECC) values for clusters
753
generated via MLST Clonal Complex (CC), MLST Sequence Type (ST) and
754
Comparative Genomic Fingerprint (CGF) for the isolates used in this study (n =
755
654). Individual facets of the plot contain the isolates from each of nine dominant MLST
756
Clonal Complexes in the dataset (indicated at the top of each box). Green Circles denote
757
“parent clusters” based on Clonal Complex (CC); membership in sub-clusters is also
758
shown (ST, purple circles; CGF, red circles). The relative membership size of each
759
cluster is indicated by the radius of each circle on the plot, and given by the values on the
760
x-axis. The average ECC of each cluster is given by its position along the y-axis. Sub-
761
clusters containing only one member, which are excluded from ECC analysis, were
762
excluded from the figure (ST, n=101; CGF, n=62). Plot was generated in R using the
763
ggplot2 package.
764 765
Figure 7. Epidemiological Cluster Cohesion analysis of CGF clusters using adjusted
766
source, temporal, and geospatial coefficients. (A) Boxplots of the total distribution of
767
ECC values for CGF clusters containing (n > 3) isolates when calculating the ∆ Ɛ using
768
different sets of source:temporal:geospatial coefficients including: 50:30:20 (i.e.
769
‘balanced’, left); 80:10:10 (i.e. ‘source emphasis’, middle); and 10:80:10 (i.e. ‘temporal
38
770
emphasis’, right). The box bounds the IQR divided by the median, and Tukey-style
771
whiskers extend to 1.5 IQR above and below each box. Outliers were identified using
772
Tukey’s method and are indicated as orange circles (source emphasis), or green triangles
773
(temporal emphasis) above the plots. (B) The distribution of ECC values for individual
774
CGF clusters. ECC values based on ‘source emphasis’ (dark grey circles), ‘balanced’
775
(light grey diamonds), and ‘temporal emphasis’ (light grey triangles) are shown. Orange
776
circles and green triangles represent outliers identified from the boxplot analysis in (A).
39
777
Tables
778
Table 1: Metadata summary of clusters identified as statistical outliers in the ECC
779
boxplot analysis from Figure 7 Outlier Type
Source Range
0082.001.001
Source
"Human_Urban"
0592.006.003
Source, Temporal
"Pig@Farm"
03/22/2005 to 04/05/2005
0609.011.003
Temporal
"Chicken@Retail;" "Cow@Farm"
06/04/2007 to 06/12/2007
0891.001.001
Temporal
"Chicken@Retail;" "Human_Urban;" "Pig@Retail"
07/06/2007 to 07/23/2007
0926.002.004
Source, Temporal
"Human_Urban"
06/26/2008 to 07/07/2008
Subtype
Temporal Range 01/29/2008 to 08/28/2008
780 781 782
Table 2: Typing statistics for methods calculated from current dataset of C. jejuni
783
isolates (n = 654). Adjusted Wallace
784 785
Method
No. Clusters
SID*
CGF
ST
CC
CGF
183
0.982
-
0.671
0.880
ST
179
0.950
0.233
-
0.999
CC
66
0.887
0.126
0.412
-
* Simpson’s Index of Diversity
40
