A Primer on Infectious Disease Bacterial Genomics

Crossing the streams: improving data quality and integration across the One Health genomics continuum with data standards and implementation strategies

The Canadian Genomics Research and Development Initiative for Antimicrobial Resistance (GRDI-AMR) uses a genomics-based approach to understand how health care, food production and the environment contribute to the development of antimicrobial resistance. Integrating genomics contextual data streams across the One Health continuum is challenging because of the diversity in data scope, content and structure. To better enable data harmonization for analyses, a contextual data standard was developed. However, development of standards does not guarantee their use. Implementation strategies are critical for putting standards into practice. This work focuses on the development of implementation strategies to better operationalize data standards across the Canadian federal genomics ecosystem. Results include improved understanding of complex data models that can create challenges for existing systems. Technical implementation strategies included spreadsheet-based solutions, new exchange formats, and direct standards integration into new databases. Data curation exercises highlighted common data collection and sharing issues, which informed improved practices and evaluation procedures. These new practices are contributing to improved data quality and sharing within the GRDI-AMR consortium as evidenced by publicly available datasets. The implementation strategies and lessons learned described in this work are generalizable for other standards and can be applied more broadly within other initiatives.

Use of a taxon-specific reference database for accurate metagenomics-based pathogen detection of Listeria monocytogenes in turkey deli meat and spinach

BACKGROUND

The reliability of culture-independent pathogen detection in foods using metagenomics is contingent on the quality and composition of the reference database. The inclusion of microbial sequences from a diverse representation of taxonomies in universal reference databases is recommended to maximize classification precision for pathogen detection. However, these sizable databases have high memory requirements that may be out of reach for some users. In this study, we aimed to assess the performance of a foodborne pathogen (FBP)-specific reference database (taxon-specific) relative to a universal reference database (taxon-agnostic). We tested our FBP-specific reference database's performance for detecting Listeria monocytogenes in two complex food matrices-ready-to-eat (RTE) turkey deli meat and prepackaged spinach-using three popular read-based DNA-to-DNA metagenomic classifiers: Centrifuge, Kraken 2 and KrakenUniq.

RESULTS

In silico host sequence removal led to substantially fewer false positive (FP) classifications and higher classification precision in RTE turkey deli meat datasets using the FBP-specific reference database. No considerable improvement in classification precision was observed following host filtering for prepackaged spinach datasets and was likely a consequence of a higher microbe-to-host sequence ratio. All datasets classified with Centrifuge using the FBP-specific reference database had the lowest classification precision compared to Kraken 2 or KrakenUniq. When a confidence-scoring threshold was applied, a nearly equivalent precision to the universal reference database was achieved for Kraken 2 and KrakenUniq. Recall was high for both reference databases across all datasets and classifiers. Substantially fewer computational resources were required for metagenomics-based detection of L. monocytogenes using the FBP-specific reference database, especially when combined with Kraken 2.

CONCLUSIONS

A universal (taxon-agnostic) reference database is not essential for accurate and reliable metagenomics-based pathogen detection of L. monocytogenes in complex food matrices. Equivalent classification performance can be achieved using a taxon-specific reference database when the appropriate quality control measures, classification software, and analysis parameters are applied. This approach is less computationally demanding and more attainable for the broader scientific and food safety communities.

Bioinformatic analyzes and validation of cystathionine gamma-lyase as a prognostic biomarker and related to immune infiltrates in hepatocellular carcinoma

Background

The role of cystathionine γ-lyase (CTH) in the prognosis and immune invasion of hepatocellular carcinoma (HCC) remains poorly understood.

Methods

In this study, the clinical data of patients with HCC were analyzed, and the expression level of CTH was compared between HCC and normal tissues using the R package and various databases.

Results

We found that CTH expression was significantly decreased in HCC compared with normal tissues, and its expression was associated with various clinicopathological factors, including tumor stage, gender, tumor status, residual tumor, histologic stage, race, alpha-fetoprotein (AFP), albumin, drinking, and smoking. Our results suggest that CTH might be a protective factor for the survival of patients with HCC. Further functional analysis revealed that high CTH expression was enriched in the Reactome signaling by interleukins and the Reactome neutrophil degranulation. Moreover, CTH expression was closely correlated with a variety of immune cells, including a negative correlation with the CD56 (bright) NK cells and follicular helper T cell (TFH), while a positive correlation with Th17 cells and central memory T cell (Tcm). High expression of CTH in immune cells predicted a better prognosis of HCC. Our findings further indicated Pyridoxal phosphate, l-cysteine, Carboxymethylthio-3-(3-chlorophenyl)-1,2,4-oxadiazol, 2-[(3-Hydroxy-2-Methyl-5-Phosphonooxymethyl-Pyridin-4-Ylmethyl)-Imino]-5-phosphono-pent-3-enoic acid and L-2-amino-3-butynoic acid as potential target candidate medications for HCC treatment based on CTH.

Conclusion

Our study suggests that CTH can serve as a biomarker to predict the prognosis and immune infiltration of HCC.

Genomic Investigation of Carbapenem-Resistant Klebsiella pneumonia Colonization in an Intensive Care Unit in South Africa

The study investigated carbapenemase-producing Klebsiella pneumoniae (CPKP) isolates of patients in an intensive care unit (ICU) in a public hospital in the KwaZulu-Natal province, South Africa using whole-genome sequencing (WGS). Ninety-seven rectal swabs, collected from all consenting adult patients (n = 31) on days 1, 3, and 7 and then weekly, were screened for carbapenemase-production using Chrome-ID selective media. Antibiotic susceptibility was determined for the fourteen positive CPKP isolates obtained using the VITEK 2 automated system. All isolates (100%) were resistant to ertapenem and meropenem, and 71.4% (n = 10) were resistant to imipenem. All CPKP isolates were subjected to ERIC/PCR, and a sub-sample of isolates was selected for WGS based on their antibiograms and clonality. All sequenced isolates harbored the bla_OXA-181 carbapenemase (100%) and co-carried other β-lactamase genes such as bla_OXA-1, bla_CTX-M-15, bla_TEM-1B, and bla_SHV-1. IncF, IncX3, and Col plasmid replicons groups and class I integrons (ln191 and ln27) were detected. All isolates belonged to the same sequence type ST307 and capsular serotypes (K102, O2v2). All the isolates carried the same virulence repertoire, reflecting the epidemiological relationship between isolates. bla_OXA-181 was located on a multi-replicon plasmid similar to that of E. coli p010_B-OXA181, and isolates were aligned with several South African and international clades, demonstrating horizontal and vertical transboundary distribution. The findings suggest that bla_OXA-181 producing K. pneumoniae is endemic in this ICU, colonizing the patients. CRE screening and enhanced infection prevention and control measures are urgently required.

Molecular Characterization of Multidrug-Resistant Yersinia enterocolitica From Foodborne Outbreaks in Sweden

The foodborne pathogen Yersinia enterocolitica causes gastrointestinal infections worldwide. In the spring of 2019, the Swedish Public Health Agency and Statens Serum Institut in Denmark independently identified an outbreak caused by Yersinia enterocolitica 4/O:3 that after sequence comparison turned out to be a cross-border outbreak. A trace-back investigation suggested shipments of fresh prewashed spinach from Italy as a common source for the outbreak. Here, we determined the genome sequences of five Y. enterocolitica clinical isolates during the Swedish outbreak using a combination of Illumina HiSeq short-read and Nanopore Technologies’ MinION long-read whole-genome sequencing. WGS results showed that all clinical strains have a fully assembled chromosome of approximately 4.6 Mbp in size and a 72-kbp virulence plasmid; one of the strains was carrying an additional 5.7-kbp plasmid, pYE-tet. All strains showed a high pathogen probability score (87.5%) with associated genes for virulence, all of which are closely related to an earlier clinical strain Y11 from Germany. In addition, we identified a chromosomally encoded multidrug-resistance cassette carrying resistance genes against chloramphenicol (catA1), streptomycin (aadA1), sulfonamides (sul1), and a mercury resistance module. This chromosomally encoded Tn2670 transposon has previously been reported associated with IncFII plasmids in Enterobacteriaceae: a Shigella flexneri clinical isolate from Japan in 1950s, a Klebsiella pneumoniae outbreak from Australia in 1997, and Salmonella enterica serovar Typhimurium. Interestingly, we identified an additional 5.7-kbp plasmid with tetB (encoding an ABC transporter), Rep, and its own ORI and ORIt sites, sharing high homology with small tetB-Rep plasmids from Pasteurellaceae. This is the first time that Tn2670 and Pasteurellaceae plasmids have been reported in Y. enterocolitica. Taken together, our study showed that the Swedish Y. enterocolitica outbreak strains acquired multi-antibiotic and metal-resistance genes through horizontal gene transfer, suggesting a potential reservoir of intraspecies dissemination of multidrug-resistance genes among foodborne pathogens. This study also highlights the concern of food-chain contamination of prewashed vegetables as a perpetual hazard against public health.

Genomic Investigation into the Virulome, Pathogenicity, Stress Response Factors, Clonal Lineages, and Phylogenetic Relationship of Escherichia coli Strains Isolated from Meat Sources in Ghana

Escherichia coli are among the most common foodborne pathogens associated with infections reported from meat sources. This study investigated the virulome, pathogenicity, stress response factors, clonal lineages, and the phylogenomic relationship of E. coli isolated from different meat sources in Ghana using whole-genome sequencing. Isolates were screened from five meat sources (beef, chevon, guinea fowl, local chicken, and mutton) and five areas (Aboabo, Central market, Nyorni, Victory cinema, and Tishegu) based in the Tamale Metropolis, Ghana. Following microbial identification, the E. coli strains were subjected to whole-genome sequencing. Comparative visualisation analyses showed different DNA synteny of the strains. The isolates consisted of diverse sequence types (STs) with the most common being ST155 (n = 3/14). Based Upon Related Sequence Types (eBURST) analyses of the study sequence types identified four similar clones, five single-locus variants, and two satellite clones (more distantly) with global curated E. coli STs. All the isolates possessed at least one restriction-modification (R-M) and CRISPR defence system. Further analysis revealed conserved stress response mechanisms (detoxification, osmotic, oxidative, and periplasmic stress) in the strains. Estimation of pathogenicity predicted a higher average probability score (P_score ≈ 0.937), supporting their pathogenic potential to humans. Diverse virulence genes that were clonal-specific were identified. Phylogenomic tree analyses coupled with metadata insights depicted the high genetic diversity of the E. coli isolates with no correlation with their meat sources and areas. The findings of this bioinformatic analyses further our understanding of E. coli in meat sources and are broadly relevant to the design of contamination control strategies in meat retail settings in Ghana.

Molecular Epidemiology of Carbapenemases in Enterobacteriales from Humans, Animals, Food and the Environment

The Enterobacteriales order consists of seven families including Enterobacteriaceae, Erwiniaceae, Pectobacteriaceae, Yersiniaceae, Hafniaceae, Morganellaceae, and Budviciaceae and 60 genera encompassing over 250 species. The Enterobacteriaceae is currently considered as the most taxonomically diverse among all seven recognized families. The emergence of carbapenem resistance (CR) in Enterobacteriaceae caused by hydrolytic enzymes called carbapenemases has become a major concern worldwide. Carbapenem-resistant Enterobacteriaceae (CRE) isolates have been reported not only in nosocomial and community-acquired pathogens but also in food-producing animals, companion animals, and the environment. The reported carbapenemases in Enterobacteriaceae from different sources belong to the Ambler class A (bla_KPC), class B (bla_IMP, bla_VIM, bla_NDM), and class D (bla_OXA-48) β-lactamases. The carbapenem encoding genes are often located on plasmids or associated with various mobile genetic elements (MGEs) like transposons and integrons, which contribute significantly to their spread. These genes are most of the time associated with other antimicrobial resistance genes such as other β-lactamases, as well as aminoglycosides and fluoroquinolones resistance genes leading to multidrug resistance phenotypes. Control strategies to prevent infections due to CRE and their dissemination in human, animal and food have become necessary. Several factors involved in the emergence of CRE have been described. This review mainly focuses on the molecular epidemiology of carbapenemases in members of Enterobacteriaceae family from humans, animals, food and the environment.

A complex approach to a complex problem: the use of whole-genome sequencing in monitoring avian-pathogenic Escherichia coli – a review

Infections associated with Escherichia coli are responsible for immense losses in poultry production; moreover, poultry products may serve as a source of pathogenic and/or resistant strains for humans. As early as during the first hours of life, commercially hatched chickens are colonized with potentially pathogenic E. coli from the environment of hatcheries. The source of contamination has not been quite elucidated and the possibility of vertical spread of several avian pathogenic E. coli (APEC) lineages has been suggested, making the hatcheries an important node where cross-contamination of chicken of different origin can take place. The recent technological progress makes the method of whole-genome sequencing (WGS) widely accessible, allowing high-throughput analysis of a large amount of isolates. Whole-genome sequencing offers an opportunity to trace APEC and extended-spectrum/plasmid-encoded AmpC beta-lactamases-producing E. coli (ESBL/pAmpC-E.coli) along the poultry processing chain and to recognize the potential pathways of “epidemicˮ sequence types. Data from WGS may be used in monitoring antimicrobial resistance, comparative pathogenomic studies describing new virulence traits and their role in pathogenesis and, above all, epidemiologic monitoring of clonal outbreaks and description of different transmission routes and their significance. This review attempts to outline the complexity of poultry-associated E. coli issues and the possibility to employ WGS in elucidating them.

Performance and Application of 16S rRNA Gene Cycle Sequencing for Routine Identification of Bacteria in the Clinical Microbiology Laboratory

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

Deconvoluting the diversity of within-host pathogen strains in a multi-locus sequence typing framework

BACKGROUND

Bacterial pathogens exhibit an impressive amount of genomic diversity. This diversity can be informative of evolutionary adaptations, host-pathogen interactions, and disease transmission patterns. However, capturing this diversity directly from biological samples is challenging.

RESULTS

We introduce a framework for understanding the within-host diversity of a pathogen using multi-locus sequence types (MLST) from whole-genome sequencing (WGS) data. Our approach consists of two stages. First we process each sample individually by assigning it, for each locus in the MLST scheme, a set of alleles and a proportion for each allele. Next, we associate to each sample a set of strain types using the alleles and the strain proportions obtained in the first step. We achieve this by using the smallest possible number of previously unobserved strains across all samples, while using those unobserved strains which are as close to the observed ones as possible, at the same time respecting the allele proportions as closely as possible. We solve both problems using mixed integer linear programming (MILP). Our method performs accurately on simulated data and generates results on a real data set of Borrelia burgdorferi genomes suggesting a high level of diversity for this pathogen.

CONCLUSIONS

Our approach can apply to any bacterial pathogen with an MLST scheme, even though we developed it with Borrelia burgdorferi, the etiological agent of Lyme disease, in mind. Our work paves the way for robust strain typing in the presence of within-host heterogeneity, overcoming an essential challenge currently not addressed by any existing methodology for pathogen genomics.

Application of different DNA extraction procedures, library preparation protocols and sequencing platforms: impact on sequencing results

In this study three DNA extraction procedures, two library preparation protocols and two sequencing platforms were applied to analyse six bacterial cultures and their corresponding DNA obtained as part of a proficiency test. The impact of each variable on sequencing results was assessed using the following parameters: reads quality, assembly and alignment statistics; number of single nucleotide polymorphisms (SNPs), detected applying assembly- and alignment-based strategies; antimicrobial resistance genes (ARGs), identified on de novo assemblies of all sequenced genomes. The investigated nucleic acid extraction procedures, library preparation kits and sequencing platforms do not significantly affect de novo assembly statistics and number of SNPs and ARGs. The only exception was observed for two duplicates, which were associated to one PCR-based library preparation kit. Results from this comparative study can support researchers in the choice toward the available pre-sequencing and sequencing options, and might suggest further comparisons to be performed.

Genomic analysis of a multidrug-resistant clinical Providencia rettgeri (PR002) strain with the novel integron ln1483 and an A/C plasmid replicon

Whole-genome sequence analysis was performed on a multidrug-resistant Providencia rettgeri PR002 clinical strain isolated from the urine of a hospitalized patient in Pretoria, South Africa, in 2013. The resistome, mobilome, pathogenicity island(s), as well as virulence and heavy-metal resistance genes of the isolate, were characterized using whole-genome sequencing and bioinformatic analysis. PR002 had a genome assembly size of 4,832,624 bp with a GC content of 40.7%, an A/C₂ plasmid replicase gene, four integrons/gene cassettes, 17 resistance genes, and several virulence and heavy metal resistance genes, confirming PR002 as a human pathogen. A novel integron, In1483, harboring the gene bla_OXA-2 , was identified, with other uncharacterized class 1 integrons harboring aacA4cr and dfrA1. Aac(3')-IIa and bla_SCO-1 , as well as bla_PER-7 , sul2, and tet(B), were found bracketed by composite Tn3 transposons, and IS91, IS91, and IS4 family insertion sequences, respectively. PR002 was resistant to all antibiotics tested except amikacin, carbapenems, cefotaxime-clavulanate, ceftazidime-clavulanate, cefoxitin, and fosfomycin. PR002 was closely related to PR1 (USA), PRET_2032 (SPAIN), DSM_1131, and NCTC7477 clinical P. rettgeri strains, but not close enough to suggest it was imported into South Africa from other countries. Multidrug resistance in P. rettgeri is rare, particularly in clinical settings, making this case an important incident requiring urgent attention. This is also the first report of an A/C plasmid in P. rettgeri. The array, multiplicity, and diversity of resistance and virulence genes in this strain are concerning, necessitating stringent infection control, antibiotic stewardship, and periodic resistance surveillance/monitoring policies to preempt further horizontal and vertical spread of these resistance genes.

Operational models and criteria for incorporating microbial whole genome sequencing in hospital microbiology - A systematic literature review

BACKGROUND

Microbial whole genome sequencing (WGS) has many advantages over standard microbiological methods. However, it is not yet widely implemented in routine hospital diagnostics due to notable challenges.

OBJECTIVES

The aim was to extract managerial, financial and clinical criteria supporting the decision to implement WGS in routine diagnostic microbiology, across different operational models of implementation in the hospital setting.

METHODS

This was a systematic review of literature identified through PubMed and Web of Science. English literature studies discussing the applications of microbial WGS without limitation on publication date were eligible. A narrative approach for categorization and synthesis of the sources identified was adopted.

RESULTS

A total of 98 sources were included. Four main alternative operational models for incorporating WGS in clinical microbiology laboratories were identified: full in-house sequencing and analysis, full outsourcing of sequencing and analysis and two hybrid models combining in-house/outsourcing of the sequencing and analysis components. Six main criteria (and multiple related sub-criteria) for WGS implementation emerged from our review and included cost (e.g. the availability of resources for capital and operational investment); manpower (e.g. the ability to provide training programmes or recruit trained personnel), laboratory infrastructure (e.g. the availability of supplies and consumables or sequencing platforms), bioinformatics requirements (e.g. the availability of valid analysis tools); computational infrastructure (e.g. the availability of storage space or data safety arrangements); and quality control (e.g. the existence of standardized procedures).

CONCLUSIONS

The decision to incorporate WGS in routine diagnostics involves multiple, sometimes competing, criteria and sub-criteria. Mapping these criteria systematically is an essential stage in developing policies for adoption of this technology, e.g. using a multicriteria decision tool. Future research that will prioritize criteria and sub-criteria that were identified in our review in the context of operational models will inform decision-making at clinical and managerial levels with respect to effective implementation of WGS for routine use. Beyond WGS, similar decision-making challenges are expected with respect to future integration of clinical metagenomics.

Shared genome analyses of notable listeriosis outbreaks, highlighting the critical importance of epidemiological evidence, input datasets and interpretation criteria

The persuasiveness of genomic evidence has pressured scientific agencies to supplement or replace well-established methodologies to inform public health and food safety decision-making. This study of 52 epidemiologically defined Listeria monocytogenes isolates, collected between 1981 and 2011, including nine outbreaks, was undertaken (1) to characterize their phylogenetic relationship at finished genome-level resolution, (2) to elucidate the underlying genetic diversity within an endemic subtype, CC8, and (3) to re-evaluate the genetic relationship and epidemiology of a CC8-delimited outbreak in Canada in 2008. Genomes representing Canadian Listeria outbreaks between 1981 and 2010 were closed and manually annotated. Single nucleotide variants (SNVs) and horizontally acquired traits were used to generate phylogenomic models. Phylogenomic relationships were congruent with classical subtyping and epidemiology, except for CC8 outbreaks, wherein the distribution of SNV and prophages revealed multiple co-evolving lineages. Chronophyletic reconstruction of CC8 evolution indicates that prophage-related genetic changes among CC8 strains manifest as PFGE subtype reversions, obscuring the relationship between CC8 isolates, and complicating the public health interpretation of subtyping data, even at maximum genome resolution. The size of the shared genome interrogated did not change the genetic relationship measured between highly related isolates near the tips of the phylogenetic tree, illustrating the robustness of these approaches for routine public health applications where the focus is recent ancestry. The possibility exists for temporally and epidemiologically distinct events to appear related even at maximum genome resolution, highlighting the continued importance of epidemiological evidence.

Evaluation of whole-genome sequencing for outbreak detection of Verotoxigenic Escherichia coli O157:H7 from the Canadian perspective

BACKGROUND

Rapid and accurate identification of Verotoxigenic Escherichia coli (VTEC) O157:H7 is dependent on well-established, standardized and highly discriminatory typing methods. Currently, conventional subtyping tests for foodborne bacterial pathogen surveillance are rapidly being replaced with whole-genome sequencing (WGS) in public health laboratories. The capacity of WGS to revolutionize global foodborne disease surveillance has positioned this tool to become the new gold standard; however, to ensure evidence standards for public health decision making can still be achieved, the performance of WGS must be thoroughly validated against current gold standard methods prior to implementation. Here we aim to verify the performance of WGS in comparison to pulsed-field gel electrophoresis (PFGE) and multiple-locus variable-number tandem repeat analysis (MLVA) for eight retrospective outbreaks of VTEC O157:H7 from the Canadian perspective. Since real-time implementation and routine use of WGS in public health laboratories is highly reliant on standardized data analysis tools, we also provide a comparative analysis of two popular methodologies for WGS analyses; an in-house developed single nucleotide variant phylogenomics (SNVPhyl) pipeline and the BioNumerics whole genome multilocus sequence typing (wgMLST) tool. To provide a useful and consistent starting point for examining laboratory-based surveillance data for VTEC O157:H7 in Canada, we also aim to describe the number of genetic differences observed among outbreak-associated isolates.

RESULTS

WGS provided enhanced resolution over traditional subtyping methods, and accurately distinguished outbreak-related isolates from non-outbreak related isolates with high epidemiological concordance. WGS also illuminated potential linkages between sporadic cases of illness and contaminated food, and isolates spanning multiple years. The topologies generated by SNVPhyl and wgMLST were highly congruent with strong statistical support. Few genetic differences were observed among outbreak-related isolates (≤5 SNVs/ < 10 wgMLST alleles) unless the outbreak was suspected to be multi-strain.

CONCLUSIONS

This study validates the superiority of WGS and indicates the BioNumerics wgMLST schema is suitable for surveillance and cluster detection of VTEC O157:H7. These findings will provide a useful and consistent starting point for examining WGS data for prospective laboratory-based surveillance of VTEC O157:H7, but however, the data will continue to be interpreted according to context and in combination with epidemiological and food safety evidence to inform public-health decision making in Canada.

chewBBACA: A complete suite for gene-by-gene schema creation and strain identification

Gene-by-gene approaches are becoming increasingly popular in bacterial genomic epidemiology and outbreak detection. However, there is a lack of open-source scalable software for schema definition and allele calling for these methodologies. The chewBBACA suite was designed to assist users in the creation and evaluation of novel whole-genome or core-genome gene-by-gene typing schemas and subsequent allele calling in bacterial strains of interest. chewBBACA performs the schema creation and allele calls on complete or draft genomes resulting from de novo assemblers. The chewBBACA software uses Python 3.4 or higher and can run on a laptop or in high performance clusters making it useful for both small laboratories and large reference centers. ChewBBACA is available at https://github.com/B-UMMI/chewBBACA.

New Insights into Clostridium difficile (CD) Infection in Latin America: Novel Description of Toxigenic Profiles of Diarrhea-Associated to CD in Bogotá, Colombia

Clostridium difficile (CD) produces antibiotic associated diarrhea and leads to a broad range of diseases. The source of CD infection (CDI) acquisition and toxigenic profile are factors determining the impact of CD. This study aimed at detecting healthcare facility onset- (HCFO) and community-onset (CO) CDI and describing their toxigenic profiles in Bogotá, Colombia. A total of 217 fecal samples from patients suffering diarrhea were simultaneously submitted to two CDI detection strategies: (i) in vitro culture using selective chromogenic medium (SCM; chromID, bioMérieux), followed verification by colony screening (VCS), and (ii) molecular detection targeting constitutive genes, using two conventional PCR tests (conv.PCR) (conv.16S y conv.gdh) and a quantitative test (qPCR.16s). The CD toxigenic profile identified by any molecular test was described using 6 tests independently for describing PaLoc and CdtLoc organization. High overall CDI frequencies were found by both SCM (52.1%) and conv.PCR (45.6% for conv.16S and 42.4% for conv.gdh), compared to reductions of up to half the frequency by VCS (27.2%) or qPCR.16S (22.6%). Infection frequencies were higher for SCM and conv.16S regarding HCFO but greater for CO concerning conv.gdh, such differences being statistically significant. Heterogeneous toxigenic profiles were found, including amplification with lok1/3 primers simultaneously with other PaLoc markers (tcdA, tcdB or tcdC). These findings correspond the first report regarding the differential detection of CDI using in vitro culture and molecular detection tests in Colombia, the circulation of CD having heterogeneous toxigenic profiles and molecular arrays which could affect the impact of CDI epidemiology.

MentaLiST - A fast MLST caller for large MLST schemes

MLST (multi-locus sequence typing) is a classic technique for genotyping bacteria, widely applied for pathogen outbreak surveillance. Traditionally, MLST is based on identifying sequence types from a small number of housekeeping genes. With the increasing availability of whole-genome sequencing data, MLST methods have evolved towards larger typing schemes, based on a few hundred genes [core genome MLST (cgMLST)] to a few thousand genes [whole genome MLST (wgMLST)]. Such large-scale MLST schemes have been shown to provide a finer resolution and are increasingly used in various contexts such as hospital outbreaks or foodborne pathogen outbreaks. This methodological shift raises new computational challenges, especially given the large size of the schemes involved. Very few available MLST callers are currently capable of dealing with large MLST schemes. We introduce MentaLiST, a new MLST caller, based on a k-mer voting algorithm and written in the Julia language, specifically designed and implemented to handle large typing schemes. We test it on real and simulated data to show that MentaLiST is faster than any other available MLST caller while providing the same or better accuracy, and is capable of dealing with MLST schemes with up to thousands of genes while requiring limited computational resources. MentaLiST source code and easy installation instructions using a Conda package are available at https://github.com/WGS-TB/MentaLiST.

A primer on microbial bioinformatics for nonbioinformaticians

BACKGROUND

Presently, the bottleneck in the deployment of high-throughput sequencing technology is the ability to analyse the increasing amount of data produced in a fit-for-purpose manner. The field of microbial bioinformatics is thriving and quickly adapting to technological changes, which creates difficulties for nonbioinformaticians in following the complexity and increasingly obscure jargon of this field.

AIMS

This review is directed towards nonbioinformaticians who wish to gain understanding of the overall microbial bioinformatic processes, from raw data obtained from sequencers to final outputs.

SOURCES

The software and analytical strategies reviewed are based on the personal experience of the authors.

CONTENT

The bioinformatic processes of transforming raw reads to actionable information in a clinical and epidemiologic context is explained. We review the advantages and limitations of two major strategies currently applied: read mapping, which is the comparison with a predefined reference genome, and de novo assembly, which is the unguided assembly of the raw data. Finally, we discuss the main analytical methodologies and the most frequently used freely available software and its application in the context of bacterial infectious disease management.

IMPLICATIONS

High-throughput sequencing technologies are overhauling outbreak investigation and epidemiologic surveillance while creating new challenges due to the amount and complexity of data generated. The continuously evolving field of microbial bioinformatics is required for stakeholders to fully harness the power of these new technologies.

Context Is Everything: Harmonization of Critical Food Microbiology Descriptors and Metadata for Improved Food Safety and Surveillance

Globalization of food networks increases opportunities for the spread of foodborne pathogens beyond borders and jurisdictions. High resolution whole-genome sequencing (WGS) subtyping of pathogens promises to vastly improve our ability to track and control foodborne disease, but to do so it must be combined with epidemiological, clinical, laboratory and other health care data (called “contextual data”) to be meaningfully interpreted for regulatory and health interventions, outbreak investigation, and risk assessment. However, current multi-jurisdictional pathogen surveillance and investigation efforts are complicated by time-consuming data re-entry, curation and integration of contextual information owing to a lack of interoperable standards and inconsistent reporting. A solution to these challenges is the use of ‘ontologies’ - hierarchies of well-defined and standardized vocabularies interconnected by logical relationships. Terms are specified by universal IDs enabling integration into highly regulated areas and multi-sector sharing (e.g., food and water microbiology with the veterinary sector). Institution-specific terms can be mapped to a given standard at different levels of granularity, maximizing comparability of contextual information according to jurisdictional policies. Fit-for-purpose ontologies provide contextual information with the auditability required for food safety laboratory accreditation. Our research efforts include the development of a Genomic Epidemiology Ontology (GenEpiO), and Food Ontology (FoodOn) that harmonize important laboratory, clinical and epidemiological data fields, as well as existing food resources. These efforts are supported by a global consortium of researchers and stakeholders worldwide. Since foodborne diseases do not respect international borders, uptake of such vocabularies will be crucial for multi-jurisdictional interpretation of WGS results and data sharing.

SNVPhyl: a single nucleotide variant phylogenomics pipeline for microbial genomic epidemiology

The recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research laboratory and into the front lines of surveillance and outbreak response requires user-friendly, reproducible and scalable pipelines that have been well validated. Single Nucleotide Variant Phylogenomics (SNVPhyl) is a bioinformatics pipeline for identifying high-quality single-nucleotide variants (SNVs) and constructing a whole-genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity, and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds or in the presence of contamination. SNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

Food Safety in the Age of Next Generation Sequencing, Bioinformatics, and Open Data Access

Public health labs and food regulatory agencies globally are embracing whole genome sequencing (WGS) as a revolutionary new method that is positioned to replace numerous existing diagnostic and microbial typing technologies with a single new target: the microbial draft genome. The ability to cheaply generate large amounts of microbial genome sequence data, combined with emerging policies of food regulatory and public health institutions making their microbial sequences increasingly available and public, has served to open up the field to the general scientific community. This open data access policy shift has resulted in a proliferation of data being deposited into sequence repositories and of novel bioinformatics software designed to analyze these vast datasets. There also has been a more recent drive for improved data sharing to achieve more effective global surveillance, public health and food safety. Such developments have heightened the need for enhanced analytical systems in order to process and interpret this new type of data in a timely fashion. In this review we outline the emergence of genomics, bioinformatics and open data in the context of food safety. We also survey major efforts to translate genomics and bioinformatics technologies out of the research lab and into routine use in modern food safety labs. We conclude by discussing the challenges and opportunities that remain, including those expected to play a major role in the future of food safety science.

Plasmid Classification in an Era of Whole-Genome Sequencing: Application in Studies of Antibiotic Resistance Epidemiology

Plasmids are extra-chromosomal genetic elements ubiquitous in bacteria, and commonly transmissible between host cells. Their genomes include variable repertoires of 'accessory genes,' such as antibiotic resistance genes, as well as 'backbone' loci which are largely conserved within plasmid families, and often involved in key plasmid-specific functions (e.g., replication, stable inheritance, mobility). Classifying plasmids into different types according to their phylogenetic relatedness provides insight into the epidemiology of plasmid-mediated antibiotic resistance. Current typing schemes exploit backbone loci associated with replication (replicon typing), or plasmid mobility (MOB typing). Conventional PCR-based methods for plasmid typing remain widely used. With the emergence of whole-genome sequencing (WGS), large datasets can be analyzed using in silico plasmid typing methods. However, short reads from popular high-throughput sequencers can be challenging to assemble, so complete plasmid sequences may not be accurately reconstructed. Therefore, localizing resistance genes to specific plasmids may be difficult, limiting epidemiological insight. Long-read sequencing will become increasingly popular as costs decline, especially when resolving accurate plasmid structures is the primary goal. This review discusses the application of plasmid classification in WGS-based studies of antibiotic resistance epidemiology; novel in silico plasmid analysis tools are highlighted. Due to the diverse and plastic nature of plasmid genomes, current typing schemes do not classify all plasmids, and identifying conserved, phylogenetically concordant genes for subtyping and phylogenetics is challenging. Analyzing plasmids as nodes in a network that represents gene-sharing relationships between plasmids provides a complementary way to assess plasmid diversity, and allows inferences about horizontal gene transfer to be made.