Peter Kruczkiewicz

Development and validation of a comparative genomic fingerprinting method for high-resolution genotyping of Campylobacter jejuni.

ABSTRACT Campylobacter spp. are a leading cause of bacterial gastroenteritis worldwide. The need for molecular subtyping methods with enhanced discrimination in the context of surveillance- and outbreak-based epidemiologic investigations of Campylobacter spp. is critical to our understanding of sources and routes of transmission and the development of mitigation strategies to reduce the incidence of campylobacteriosis. We describe the development and validation of a rapid and high-resolution comparative genomic fingerprinting (CGF) method for C. jejuni. A total of 412 isolates from agricultural, environmental, retail, and human clinical sources obtained from the Canadian national integrated enteric pathogen surveillance program (C-EnterNet) were analyzed using a 40-gene assay (CGF40) and multilocus sequence typing (MLST). The significantly higher Simpsons index of diversity (ID) obtained with CGF40 (ID = 0.994) suggests that it has a higher discriminatory power than MLST at both the level of clonal complex (ID = 0.873) and sequence type (ID = 0.935). High Wallace coefficients obtained when CGF40 was used as the primary typing method suggest that CGF and MLST are highly concordant, and we show that isolates with identical MLST profiles are comprised of isolates with distinct but highly similar CGF profiles. The high concordance with MLST coupled with the ability to discriminate between closely related isolates suggests that CFG40 is useful in differentiating highly prevalent sequence types, such as ST21 and ST45. CGF40 is a high-resolution comparative genomics-based method for C. jejuni subtyping with high discriminatory power that is also rapid, low cost, and easily deployable for routine epidemiologic surveillance and outbreak investigations.

Evaluation of MALDI-TOF mass spectroscopy methods for determination of Escherichia coli pathotypes

It is rapidly becoming apparent that many E. coli pathotypes cause a considerable burden of human disease. Surveillance of these organisms is difficult because there are few or no simple, rapid methods for detecting and differentiating the different pathotypes. MALDI-TOF mass spectroscopy has recently been rapidly and enthusiastically adopted by many clinical laboratories as a diagnostic method because of its high throughput, relatively low cost, and adaptability to the laboratory workflow. To determine whether the method could be adapted for E. coli pathotype differentiation the Bruker Biotyper methodology and a second methodology adapted from the scientific literature were tested on isolates representing eight distinct pathotypes and two other groups of E. coli. A total of 136 isolates was used for this study. Results confirmed that the Bruker Biotyper methodology that included extraction of proteins from bacterial cells was capable of identifying E. coli isolates from all pathotypes to the species level and, furthermore, that the Bruker extraction and MALDI-TOF MS with the evaluation criteria developed in this work was effective for differentiating most pathotypes.

The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies

For nearly 100 years serotyping has been the gold standard for the identification of Salmonella serovars. Despite the increasing adoption of DNA-based subtyping approaches, serotype information remains a cornerstone in food safety and public health activities aimed at reducing the burden of salmonellosis. At the same time, recent advances in whole-genome sequencing (WGS) promise to revolutionize our ability to perform advanced pathogen characterization in support of improved source attribution and outbreak analysis. We present the Salmonella In Silico Typing Resource (SISTR), a bioinformatics platform for rapidly performing simultaneous in silico analyses for several leading subtyping methods on draft Salmonella genome assemblies. In addition to performing serovar prediction by genoserotyping, this resource integrates sequence-based typing analyses for: Multi-Locus Sequence Typing (MLST), ribosomal MLST (rMLST), and core genome MLST (cgMLST). We show how phylogenetic context from cgMLST analysis can supplement the genoserotyping analysis and increase the accuracy of in silico serovar prediction to over 94.6% on a dataset comprised of 4,188 finished genomes and WGS draft assemblies. In addition to allowing analysis of user-uploaded whole-genome assemblies, the SISTR platform incorporates a database comprising over 4,000 publicly available genomes, allowing users to place their isolates in a broader phylogenetic and epidemiological context. The resource incorporates several metadata driven visualizations to examine the phylogenetic, geospatial and temporal distribution of genome-sequenced isolates. As sequencing of Salmonella isolates at public health laboratories around the world becomes increasingly common, rapid in silico analysis of minimally processed draft genome assemblies provides a powerful approach for molecular epidemiology in support of public health investigations. Moreover, this type of integrated analysis using multiple sequence-based methods of sub-typing allows for continuity with historical serotyping data as we transition towards the increasing adoption of genomic analyses in epidemiology. The SISTR platform is freely available on the web at https://lfz.corefacility.ca/sistr-app/.

A framework for assessing the concordance of molecular typing methods and the true strain phylogeny of Campylobacter jejuni and C. coli using draft genome sequence data

Tracking of sources of sporadic cases of campylobacteriosis remains challenging, as commonly used molecular typing methods have limited ability to unambiguously link genetically related strains. Genomics has become increasingly prominent in the public health response to enteric pathogens as methods enable characterization of pathogens at an unprecedented level of resolution. However, the cost of sequencing and expertise required for bioinformatic analyses remains prohibitive, and these comprehensive analyses are limited to a few priority strains. Although several molecular typing methods are currently widely used for epidemiological analysis of campylobacters, it is not clear how accurately these methods reflect true strain relationships. To address this, we have developed a framework and associated computational tools to rapidly analyze draft genome sequence data for the assessment of molecular typing methods against a “gold standard” based on the phylogenetic analysis of highly conserved core (HCC) genes with high sequence quality. We analyzed 104 publicly available whole genome sequences (WGS) of C. jejuni and C. coli. In addition to in silico determination of multi-locus sequence typing (MLST), flaA, and porA type, as well as comparative genomic fingerprinting (CGF) type, we inferred a “reference” phylogeny based on 389 HCC genes. Molecular typing data were compared to the reference phylogeny for concordance using the adjusted Wallace coefficient (AWC) with confidence intervals. Although MLST targets the sequence variability in core genes and CGF targets insertions/deletions of accessory genes, both methods are based on multi-locus analysis and provided better estimates of true phylogeny than methods based on single loci (porA, flaA). A more comprehensive WGS dataset including additional genetically related strains, both epidemiologically linked and unlinked, will be necessary to more comprehensively assess the performance of subtyping methods for outbreak investigations and surveillance activities. Analyses of the strengths and weaknesses of widely used typing methodologies in inferring true strain relationships will provide guidance in the interpretation of this data for epidemiological purposes.

The Validation and Implications of Using Whole Genome Sequencing as a Replacement for Traditional Serotyping for a National Salmonella Reference Laboratory

Salmonella serotyping remains the gold-standard tool for the classification of Salmonella isolates and forms the basis of Canada’s national surveillance program for this priority foodborne pathogen. Public health officials have been increasingly looking toward whole genome sequencing (WGS) to provide a large set of data from which all the relevant information about an isolate can be mined. However, rigorous validation and careful consideration of potential implications in the replacement of traditional surveillance methodologies with WGS data analysis tools is needed. Two in silico tools for Salmonella serotyping have been developed, the Salmonella in silico Typing Resource (SISTR) and SeqSero, while seven gene MLST for serovar prediction can be adapted for in silico analysis. All three analysis methods were assessed and compared to traditional serotyping techniques using a set of 813 verified clinical and laboratory isolates, including 492 Canadian clinical isolates and 321 isolates of human and non-human sources. Successful results were obtained for 94.8, 88.2, and 88.3% of the isolates tested using SISTR, SeqSero, and MLST, respectively, indicating all would be suitable for maintaining historical records, surveillance systems, and communication structures currently in place and the choice of the platform used will ultimately depend on the users need. Results also pointed to the need to reframe serotyping in the genomic era as a test to understand the genes that are carried by an isolate, one which is not necessarily congruent with what is antigenically expressed. The adoption of WGS for serotyping will provide the simultaneous collection of information that can be used by multiple programs within the current surveillance paradigm; however, this does not negate the importance of the various programs or the role of serotyping going forward.

Development of a comparative genomic fingerprinting assay for rapid and high resolution genotyping of Arcobacter butzleri

BackgroundMolecular typing methods are critical for epidemiological investigations, facilitating disease outbreak detection and source identification. Study of the epidemiology of the emerging human pathogen Arcobacter butzleri is currently hampered by the lack of a subtyping method that is easily deployable in the context of routine epidemiological surveillance. In this study we describe a comparative genomic fingerprinting (CGF) method for high-resolution and high-throughput subtyping of A. butzleri. Comparative analysis of the genome sequences of eleven A. butzleri strains, including eight strains newly sequenced as part of this project, was employed to identify accessory genes suitable for generating unique genetic fingerprints for high-resolution subtyping based on gene presence or absence within a strain.ResultsA set of eighty-three accessory genes was used to examine the population structure of a dataset comprised of isolates from various sources, including human and non-human animals, sewage, and river water (n=156). A streamlined assay (CGF40) based on a subset of 40 genes was subsequently developed through marker optimization. High levels of profile diversity (121 distinct profiles) were observed among the 156 isolates in the dataset, and a high Simpson’s Index of Diversity (ID) observed (ID > 0.969) indicate that the CGF40 assay possesses high discriminatory power. At the same time, our observation that 115 isolates in this dataset could be assigned to 29 clades with a profile similarity of 90% or greater indicates that the method can be used to identify clades comprised of genetically similar isolates.ConclusionsThe CGF40 assay described herein combines high resolution and repeatability with high throughput for the rapid characterization of A. butzleri strains. This assay will facilitate the study of the population structure and epidemiology of A. butzleri.

Comparative Detection and Quantification of Arcobacter butzleri in Stools from Diarrheic and Nondiarrheic People in Southwestern Alberta, Canada

ABSTRACT Arcobacter butzleri has been linked to enteric disease in humans, but its pathogenicity and epidemiology remain poorly understood. The lack of suitable detection methods is a major limitation. Using comparative genome analysis, we developed PCR primers for direct detection and quantification of A. butzleri DNA in microbiologically complex matrices. These primers, along with existing molecular and culture-based methods, were used to detect A. butzleri and enteric pathogens in stools of diarrheic and nondiarrheic people (n = 1,596) living in southwestern Alberta, Canada, from May to November 2008. In addition, quantitative PCR was used to compare A. butzleri densities in diarrheic and nondiarrheic stools. Arcobacter butzleri was detected more often by PCR (59.6%) than by isolation methods (0.8%). Comparison by PCR-based detection found no difference in the prevalence of A. butzleri between diarrheic (56.7%) and nondiarrheic (45.5%) individuals. Rates of detection in diarrheic stools peaked in June (71.1%) and October (68.7%), but there was no statistically significant correlation between the presence of A. butzleri and patient age, sex, or place of habitation. Densities of A. butzleri DNA in diarrheic stools (1.6 ± 0.59 log10 copies mg−1) were higher (P = 0.007) than in nondiarrheic stools (1.3 ± 0.63 log10 copies mg−1). Of the 892 diarrheic samples that were positive for A. butzleri, 74.1% were not positive for other bacterial and/or viral pathogens. The current study supports previous work suggesting that A. butzleri pathogenicity is strain specific and/or dependent on other factors, such as the level of host resistance.

SuperPhy: predictive genomics for the bacterial pathogen Escherichia coli

BackgroundPredictive genomics is the translation of raw genome sequence data into a phenotypic assessment of the organism. For bacterial pathogens, these phenotypes can range from environmental survivability, to the severity of human disease. Significant progress has been made in the development of generic tools for genomic analyses that are broadly applicable to all microorganisms; however, a fundamental missing component is the ability to analyze genomic data in the context of organism-specific phenotypic knowledge, which has been accumulated from decades of research and can provide a meaningful interpretation of genome sequence data.ResultsIn this study, we present SuperPhy, an online predictive genomics platform (http://lfz.corefacility.ca/superphy/) for Escherichia coli. The platform integrates the analytical tools and genome sequence data for all publicly available E. coli genomes and facilitates the upload of new genome sequences from users under public or private settings. SuperPhy provides real-time analyses of thousands of genome sequences with results that are understandable and useful to a wide community, including those in the fields of clinical medicine, epidemiology, ecology, and evolution. SuperPhy includes identification of: 1) virulence and antimicrobial resistance determinants 2) statistical associations between genotypes, biomarkers, geospatial distribution, host, source, and phylogenetic clade; 3) the identification of biomarkers for groups of genomes on the based presence/absence of specific genomic regions and single-nucleotide polymorphisms and 4) in silico Shiga-toxin subtype.ConclusionsSuperPhy is a predictive genomics platform that attempts to provide an essential link between the vast amounts of genome information currently being generated and phenotypic knowledge in an organism-specific context.

A Genome-Wide Association Study to Identify Diagnostic Markers for Human Pathogenic Campylobacter jejuni Strains

Campylobacter jejuni is a leading human enteric pathogen worldwide and despite an improved understanding of its biology, ecology, and epidemiology, limited tools exist for identifying strains that are likely to cause disease. In the current study, we used subtyping data in a database representing over 24,000 isolates collected through various surveillance projects in Canada to identify 166 representative genomes from prevalent C. jejuni subtypes for whole genome sequencing. The sequence data was used in a genome-wide association study (GWAS) aimed at identifying accessory gene markers associated with clinically related C. jejuni subtypes. Prospective markers (n = 28) were then validated against a large number (n = 3,902) of clinically associated and non-clinically associated genomes from a variety of sources. A total of 25 genes, including six sets of genetically linked genes, were identified as robust putative diagnostic markers for clinically related C. jejuni subtypes. Although some of the genes identified in this study have been previously shown to play a role in important processes such as iron acquisition and vitamin B5 biosynthesis, others have unknown function or are unique to the current study and warrant further investigation. As few as four of these markers could be used in combination to detect up to 90% of clinically associated isolates in the validation dataset, and such markers could form the basis for a screening assay to rapidly identify strains that pose an increased risk to public health. The results of the current study are consistent with the notion that specific groups of C. jejuni strains of interest are defined by the presence of specific accessory genes.

Rapid Identification of Stable Clusters in Bacterial Populations Using the Adjusted Wallace Coefficient

Whole-genome sequencing (WGS) of microbial pathogens has become an essential part of modern epidemiological investigations. Although WGS data can be analyzed using a number of different approaches, such as traditional phylogenetic methods, a critical requirement for global systems for pathogen surveillance is the development of approaches for transforming sequence data into WGS-based subtypes, which creates a nomenclature that describes their higher-order relationships to one another. To this end, subtype similarity thresholds are needed to define clusters of subtypes representing lineages of interest. WGS-based subtyping presents a challenge since both the addition of novel genome sequences and small adjustments in similarity thresholds can have a dramatic impact on cluster composition and stability. We present the Neighbourhood Adjusted Wallace Coefficient (nAWC), a method for evaluating cluster stability based on computing cluster concordance between neighbouring similarity thresholds. The nAWC can be used to identify areas in in which distance thresholds produce robust clusters. Using datasets from Salmonella enterica and Campylobacter jejuni, representing strongly and weakly clonal bacterial species respectively, we show that clusters generated using such thresholds are both stable and reflect basic units in their overall population structure. Our results suggest that the nAWC could be useful for defining robust clusters compatible with nomenclatures for global WGS-based surveillance networks, which require stable clusters to be defined that both harness the discriminatory power of WGS data while allowing for long-term tracking of strains of interest.