Whole genome sequencing for investigations of meningococcal outbreaks in the United States: a retrospective analysis

Genetically distinct Hajj and South American-related strains of serogroup W Neisseria meningitidis causing invasive meningococcal disease in Ontario, Canada, January 1, 2015 to June 30, 2024

OBJECTIVES

To characterize the recent trends in serogroup W isolates from invasive meningococcal disease (IMD) cases (MenW) in Ontario, Canada since 2015.

METHODS

IMD case isolates in Ontario between January 1, 2015 and June 30, 2024 were examined by phenotypic and genetic methods for possession of vaccine antigen genes and clonal characteristics. MenW ST-11 clonal complex (CC) strains were compared against global MenW isolates by core-genome multi-locus sequence typing (cgMLST).

RESULTS

The percentage of culture-confirmed IMD caused by MenW in Ontario increased from 10 % in 2015-40.9 % in the first half of 2024, consisting entirely of strains belonging to the ST-11 CC. cgMLST comparison of the Ontario invasive MenW isolates versus international MenW ST-11CC strains showed that the Ontario isolates were related to those found globally, with a recent cluster of eight cases from one city due to a strain highly related to international Umrah outbreak strains. Most MenW IMD cases (60 %) occurred in individuals older than 40 years of age and the majority (83.3 %) predicted to express antigens covered by the 4CMenB vaccine.

CONCLUSIONS

Multiple different introductions of international MenW strains likely accounted for the recent shift towards invasive MenW disease in Ontario.

Statewide Outbreak of Neisseria meningitidis Serogroup Y, Sequence Type 1466 - Virginia, 2022-2024

Invasive meningococcal disease (IMD) is a severe illness that can have devastating effects; outbreaks are uncommon in the United States. Vaccination is the preferred control measure for IMD outbreaks when a defined population at risk (e.g., college students or persons experiencing homelessness) can be identified. In August 2022, the Virginia Department of Health (VDH) began investigating an IMD outbreak in Virginia's Eastern Health Planning Region, prompted by the detection of four confirmed cases within 8 weeks. Clinical isolates available from three cases were characterized as Neisseria meningitidis serogroup Y, sequence type 1466. A subsequent statewide investigation identified 36 genetically related cases, including seven deaths (case fatality rate = 19.4%) as of March 1, 2024. A majority of patients (63.9%) were in an age group (30-60 years) not generally considered at increased risk for IMD; 78.0% were non-Hispanic Black or African American. No common exposures, affiliations, or risk factors were identified, and a defined population could not be identified for vaccination. VDH recommended quadrivalent (serogroups A, C, W, and Y) meningococcal conjugate vaccination of a subset of close contacts of patients based on IMD risk factors and age range similar to that of patients with identified cases. IMD outbreaks might affect populations without established IMD risk factors. Lack of a well-defined population at risk might prompt exploration of novel control strategies, such as selective vaccination of close contacts.

Identifying factors that influence the use of pathogen genomics in Australia and New Zealand: a protocol

Introduction

Pathogen genomics, where whole genome sequencing technologies are used to produce complete genomic sequences of pathogens, is being increasingly used for infectious disease surveillance and outbreak response. Although proof-of-concept studies have highlighted the viability of using pathogen genomics in public health, few studies have investigated how end-users utilize pathogen genomics in public health. We describe a protocol for a study that aims to identify key factors that influence the use of pathogen genomics to inform public health responses against infectious diseases in Australia and New Zealand.

Methods

We will use qualitative comparative analysis (QCA), a case-oriented methodology that systematically compares and analyses multiple cases (or 'units of analysis'), to identify multiple pathways leading to the use of pathogen genomics results in public health actions. As part of the process, we will develop a rubric to identify and define the use of pathogen genomics and individual factors affecting this process. Simultaneously, we will identify cases where pathogen genomics has been used in public health across Australia and New Zealand. Data for these cases will be collected from document review of publicly available and confidential documents and semi-structured interviews with technicians and end-users and summarized in a case report. These case reports will form the basis for scoring each case on the extent of the use of pathogen genomics data and the presence or absence of specific factors such as the ease of extracting essential information from pathogen genomics reports and perceptions toward pathogen genomics. Using the scores, cases will be analyzed using QCA techniques to identify pathways leading to the use of pathogen genomics data. These pathways will be interpreted alongside the cases to provide rich explanations of the use of pathogen genomics in public health.

Discussion

This study will improve our understanding of the key factors that facilitate or hinder the use of pathogen genomics to inform public health authorities and end-users. These findings may inform ways to enhance the use of pathogen genomics data in public health.

Meningococcal disease in North America: Updates from the Global Meningococcal Initiative

This review summarizes the recent Global Meningococcal Initiative (GMI) regional meeting, which explored meningococcal disease in North America. Invasive meningococcal disease (IMD) cases are documented through both passive and active surveillance networks. IMD appears to be decreasing in many areas, such as the Dominican Republic (2016: 18 cases; 2021: 2 cases) and Panama (2008: 1 case/100,000; 2021: <0.1 cases/100,000); however, there is notable regional and temporal variation. Outbreaks persist in at-risk subpopulations, such as people experiencing homelessness in the US and migrants in Mexico. The recent emergence of β-lactamase-positive and ciprofloxacin-resistant meningococci in the US is a major concern. While vaccination practices vary across North America, vaccine uptake remains relatively high. Monovalent and multivalent conjugate vaccines (which many countries in North America primarily use) can provide herd protection. However, there is no evidence that group B vaccines reduce meningococcal carriage. The coronavirus pandemic illustrates that following public health crises, enhanced surveillance of disease epidemiology and catch-up vaccine schedules is key. Whole genome sequencing is a key epidemiological tool for identifying IMD strain emergence and the evaluation of vaccine strain coverage. The Global Roadmap on Defeating Meningitis by 2030 remains a focus of the GMI.

Whole Genome Analyses Accurately Identify Neisseria spp. and Limit Taxonomic Ambiguity

Genome sequencing facilitates the study of bacterial taxonomy and allows the re-evaluation of the taxonomic relationships between species. Here, we aimed to analyze the draft genomes of four commensal Neisseria clinical isolates from the semen of infertile Lebanese men. To determine the phylogenetic relationships among these strains and other Neisseria spp. and to confirm their identity at the genomic level, we compared the genomes of these four isolates with the complete genome sequences of Neisseria gonorrhoeae and Neisseria meningitidis and the draft genomes of Neisseria flavescens, Neisseria perflava, Neisseria mucosa, and Neisseria macacae that are available in the NCBI Genbank database. Our findings revealed that the WGS analysis accurately identified and corroborated the matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) species identities of the Neisseria isolates. The combination of three well-established genome-based taxonomic tools (in silico DNA-DNA Hybridization, Ortho Average Nucleotide identity, and pangenomic studies) proved to be relatively the best identification approach. Notably, we also discovered that some Neisseria strains that are deposited in databases contain many taxonomical errors. The latter is very important and must be addressed to prevent misdiagnosis and missing emerging etiologies. We also highlight the need for robust cut-offs to delineate the species using genomic tools.

Enhancing Meningococcal Genomic Surveillance in the Meningitis Belt Using High-Resolution Culture-Free Whole-Genome Sequencing

Rollout of meningococcal serogroup A conjugate vaccine in Africa started in 2010, aiming to eliminate meningitis outbreaks, in meningitis belt countries. Since then, studies have been conducted, primarily using isolates, to assess the vaccine impact on the distribution of meningococcal strains in the region. Here, we implemented an innovative, culture-free whole-genome sequencing approach on almost 400 clinical specimens collected between 2017 and 2019 from meningococcal meningitis cases in 6 African countries. About 50% of specimens provided high-quality whole-genome sequence data for comprehensive molecular profiling of the meningococcal pathogen. Three major clonal complexes were identified: CC11 associated with serogroup W, CC181 associated with serogroup X, and CC10217 associated with serogroup C, which continues to rise as a predominant clonal complex in the region. Genomic surveillance for meningococcal meningitis can be significantly improved using culture-free methods to increase data representativeness and monitor changes in epidemiological landscape, especially for countries with low culture rate.

Demographic Features of Invasive Meningococcal Disease in Taiwan, 1993 to 2020, and Genetic Characteristics of Neisseria meningitidis Isolates, 2003 to 2020

We present the demographic features of invasive meningococcal disease (IMD) in Taiwan between 1993 and 2020 and the genetic characteristics of Neisseria meningitidis isolates recovered from 2003 to 2020. IMD was rare in Taiwan between 1993 and 2020, with an annual incidence ranging from 0.009 to 0.204 per 100,000 people. The case fatality rate (CFR) declined from 18.1% for patients in 1993 to 2002 to 9.8% in 2003 to 2020. Infants less than 12 months were most susceptible to the disease. N. meningitidis serogroup B (NmB) was most predominant, responsible for 81.2% (134/165) of the IMD cases in 2003 to 2020. The majority of the isolates recovered from 2003 to 2020 belonged to 4 worldwide-spread hyperinvasive clonal complexes (cc), cc4821 (30.3%), cc32 (19.4%), cc41/44 (12.7%), cc23 (7.3%), and also a newly assigned clonal complex, cc3439 (10.3%). Core genome multilocus sequence typing (cgMLST) profile comparisons revealed that the cc4821 isolates with a T-to-I substitution at position 91 in gyrA were closely related to those originating from China. Of the 165 isolates, 20.0% and 53.3% were predicted to be covered by the Bexsero and Trumenba vaccines, respectively, whereas, 77.0% and 46.7% remained indeterminate. In conclusion, N. meningitidis isolates recovered in Taiwan between 2003 and 2020 were mostly highly diverse. Most IMD cases appeared sporadically and were caused by localized strains, although some patients were infected by recently introduced strains. cgMLST is a powerful tool for the rapid comparison of genetic relatedness among a large number of isolates. cgMLST profiling, based on 1,241 core genes, and strain tracking can be performed on the website of cgMLST@Taiwan (http://rdvd.cdc.gov.tw/cgMLST/). IMPORTANCE N. meningitidis can cause life-threatening invasive meningococcal disease (IMD), including meningitis and sepsis, resulting in a high CFR and long-term sequelae in survivors. Here, we report the demographic features of IMD in Taiwan over a 28-year period (1993 to 2020) and the genetic characteristics of N. meningitidis isolates recovered from patients with IMD over an 18-year period (2003 to 2020). We conducted a whole-genome sequence analysis to characterize the genetic features of the isolates and developed a cgMLST scheme for epidemiological investigation and strain tracking. The findings can be beneficial in understanding the epidemiology of IMD in Taiwan, the genetic characteristics of the bacterial strains, and the distribution of vaccine antigens for vaccine development and implementation.

An implementation science approach to evaluating pathogen whole genome sequencing in public health

BACKGROUND

Pathogen whole genome sequencing (WGS) is being incorporated into public health surveillance and disease control systems worldwide and has the potential to make significant contributions to infectious disease surveillance, outbreak investigation and infection prevention and control. However, to date, there are limited data regarding (i) the optimal models for integration of genomic data into epidemiological investigations and (ii) how to quantify and evaluate public health impacts resulting from genomic epidemiological investigations.

METHODS

We developed the Pathogen Genomics in Public HeAlth Surveillance Evaluation (PG-PHASE) Framework to guide examination of the use of WGS in public health surveillance and disease control. We illustrate the use of this framework with three pathogens as case studies: Listeria monocytogenes, Mycobacterium tuberculosis and SARS-CoV-2.

RESULTS

The framework utilises an adaptable whole-of-system approach towards understanding how interconnected elements in the public health application of pathogen genomics contribute to public health processes and outcomes. The three phases of the PG-PHASE Framework are designed to support understanding of WGS laboratory processes, analysis, reporting and data sharing, and how genomic data are utilised in public health practice across all stages, from the decision to send an isolate or sample for sequencing to the use of sequence data in public health surveillance, investigation and decision-making. Importantly, the phases can be used separately or in conjunction, depending on the need of the evaluator. Subsequent to conducting evaluation underpinned by the framework, avenues may be developed for strategic investment or interventions to improve utilisation of whole genome sequencing.

CONCLUSIONS

Comprehensive evaluation is critical to support health departments, public health laboratories and other stakeholders to successfully incorporate microbial genomics into public health practice. The PG-PHASE Framework aims to assist public health laboratories, health departments and authorities who are either considering transitioning to whole genome sequencing or intending to assess the integration of WGS in public health practice, including the capacity to detect and respond to outbreaks and associated costs, challenges and facilitators in the utilisation of microbial genomics and public health impacts.

Genomic Surveillance of a Globally Circulating Distinct Group W Clonal Complex 11 Meningococcal Variant, New Zealand, 2013-2018

Genomic surveillance is an essential part of effective disease control, enabling identification of emerging and expanding strains and monitoring of subsequent interventions. Whole-genome sequencing was used to analyze the genomic diversity of all Neisseria meningitidis isolates submitted to the New Zealand Meningococcal Reference Laboratory during 2013–2018. Of the 347 isolates submitted for whole-genome sequencing, we identified 68 sequence types belonging to 18 clonal complexes (CC). The predominant CC was CC41/44; next in predominance was CC11. Comparison of the 45 New Zealand group W CC11 isolates with worldwide representatives of group W CC11 isolates revealed that the original UK strain, the 2013 UK strain, and a distinctive variant (the 2015 strain) were causing invasive group W meningococcal disease in New Zealand. The 2015 strain also demonstrated increased resistance to penicillin and has been circulating in Canada and several countries in Europe, highlighting that close monitoring is needed to prevent future outbreaks around the world.

Using Neisseria meningitidis genomic diversity to inform outbreak strain identification

Meningococcal disease is a life-threatening illness caused by the human-restricted bacterium Neisseria meningitidis. Outbreaks in the USA involve at least two cases in an organization or community caused by the same serogroup within three months. Genome comparisons, including phylogenetic analysis and quantification of genome distances can provide confirmatory evidence of pathogen transmission during an outbreak. Interpreting genome distances depends on understanding their distribution both among isolates from outbreaks and among those not from outbreaks. Here, we identify outbreak strains based on phylogenetic relationships among 141 N. meningitidis isolates collected from 28 outbreaks in the USA during 2010-2017 and 1516 non-outbreak isolates collected through contemporaneous meningococcal surveillance. We show that genome distance thresholds based on the maximum SNPs and allele distances among isolates in the phylogenetically defined outbreak strains are sufficient to separate most pairs of non-outbreak isolates into separate strains. Non-outbreak isolate pairs that could not be distinguished from each other based on genetic distances were concentrated in the clonal complexes CC11, CC103, and CC32. Within each of these clonal complexes, phylodynamic analysis identified a group of isolates with extremely low diversity, collected over several years and multiple states. Clusters of isolates with low genetic diversity could indicate increased pathogen transmission, potentially resulting in local outbreaks or nationwide clonal expansions.

Establishment and Evaluation of a Core Genome Multilocus Sequence Typing Scheme for Whole-Genome Sequence-Based Typing of Pseudomonas aeruginosa

The environmental bacterium Pseudomonas aeruginosa, particularly multidrug-resistant clones, is often associated with nosocomial infections and outbreaks. Today, core genome multilocus sequence typing (cgMLST) is frequently applied to delineate sporadic cases from nosocomial transmissions. However, until recently, no cgMLST scheme for a standardized typing of P. aeruginosa was available. To establish a novel cgMLST scheme for P. aeruginosa, we initially determined the breadth of the P. aeruginosa population based on MLST data with a Bayesian approach (BAPS). Using genomic data of representative isolates for the whole population and all 12 serogroups, we extracted target genes and further refined them using a random data set of 1,000 P. aeruginosa genomes. Subsequently, we investigated reproducibility and discriminatory ability with repeatedly sequenced isolates and isolates from well-defined outbreak scenarios, respectively, and compared clustering applying two recently published cgMLST schemes. BAPS generated seven P. aeruginosa groups. To cover these and all serogroups, 15 reference strains were used to determine genes common in all strains. After refinement with the data set of 1,000 genomes, the cgMLST scheme consisted of 3,867 target genes, which are representative of the P. aeruginosa population and highly reproducible using biological replicates. We finally evaluated the scheme by reanalyzing two published outbreaks where the authors used single-nucleotide polymorphism (SNP) typing. In both cases, cgMLST was concordant with the previous SNP results and the results of the two other cgMLST schemes. In conclusion, the highly reproducible novel P. aeruginosa cgMLST scheme facilitates outbreak investigations due to the publicly available cgMLST nomenclature.

Web-Based Genome Analysis of Bacterial Meningitis Pathogens for Public Health Applications Using the Bacterial Meningitis Genomic Analysis Platform (BMGAP)

Effective laboratory-based surveillance and public health response to bacterial meningitis depends on timely characterization of bacterial meningitis pathogens. Traditionally, characterizing bacterial meningitis pathogens such as Neisseria meningitidis (Nm) and Haemophilus influenzae (Hi) required several biochemical and molecular tests. Whole genome sequencing (WGS) has enabled the development of pipelines capable of characterizing the given pathogen with equivalent results to many of the traditional tests. Here, we present the Bacterial Meningitis Genomic Analysis Platform (BMGAP): a secure, web-accessible informatics platform that facilitates automated analysis of WGS data in public health laboratories. BMGAP is a pipeline comprised of several components, including both widely used, open-source third-party software and customized analysis modules for the specific target pathogens. BMGAP performs de novo draft genome assembly and identifies the bacterial species by whole-genome comparisons against a curated reference collection of 17 focal species including Nm, Hi, and other closely related species. Genomes identified as Nm or Hi undergo multi-locus sequence typing (MLST) and capsule characterization. Further typing information is captured from Nm genomes, such as peptides for the vaccine antigens FHbp, NadA, and NhbA. Assembled genomes are retained in the BMGAP database, serving as a repository for genomic comparisons. BMGAP's species identification and capsule characterization modules were validated using PCR and slide agglutination from 446 bacterial invasive isolates (273 Nm from nine different serogroups, 150 Hi from seven different serotypes, and 23 from nine other species) collected from 2017 to 2019 through surveillance programs. Among the validation isolates, BMGAP correctly identified the species for all 440 isolates (100% sensitivity and specificity) and accurately characterized all Nm serogroups (99% sensitivity and 98% specificity) and Hi serotypes (100% sensitivity and specificity). BMGAP provides an automated, multi-species analysis pipeline that can be extended to include additional analysis modules as needed. This provides easy-to-interpret and validated Nm and Hi genome analysis capacity to public health laboratories and collaborators. As the BMGAP database accumulates more genomic data, it grows as a valuable resource for rapid comparative genomic analyses during outbreak investigations.

Full Molecular Typing of Neisseria meningitidis Directly from Clinical Specimens for Outbreak Investigation

Neisseria meningitidis is a leading cause of bacterial meningitis and sepsis worldwide and an occasional cause of meningococcal urethritis. When isolates are unavailable for surveillance or outbreak investigations, molecular characterization of pathogens needs to be performed directly from clinical specimens, such as cerebrospinal fluid (CSF), blood, or urine. However, genome sequencing of specimens is challenging because of low bacterial and high human DNA abundances. We developed selective whole-genome amplification (SWGA), an isothermal multiple-displacement amplification-based method, to efficiently enrich, sequence, and de novo assemble N. meningitidis DNA from clinical specimens with low bacterial loads. SWGA was validated with 12 CSF specimens from invasive meningococcal disease cases and 12 urine specimens from meningococcal urethritis cases. SWGA increased the mean proportion of N. meningitidis reads by 2 to 3 orders of magnitude, enabling identification of at least 90% of the 1,605 N. meningitidis core genome loci for 50% of the specimens. The validated method was used to investigate two meningitis outbreaks recently reported in Togo and Burkina Faso. Twenty-seven specimens with low bacterial loads were processed by SWGA before sequencing, and 12 of 27 were successfully assembled to obtain the full molecular typing and vaccine antigen profile of the N. meningitidis pathogen, thus enabling thorough characterization of outbreaks. This method is particularly important for enhancing molecular surveillance in regions with low culture rates. SWGA produces enough reads for phylogenetic and allelic analysis at a low cost. More importantly, the procedure can be extended to enrich other important human bacterial pathogens.

Toward a Global Genomic Epidemiology of Meningococcal Disease

Whole-genome sequencing (WGS) is invaluable for studying the epidemiology of meningococcal disease. Here we provide a perspective on the use of WGS for meningococcal molecular surveillance and outbreak investigation, where it helps to characterize pathogens, predict pathogen traits, identify emerging pathogens, and investigate pathogen transmission during outbreaks. Standardization of WGS workflows has facilitated their implementation by clinical and public health laboratories (PHLs), but further development is required for metagenomic shotgun sequencing and targeted sequencing to be widely available for culture-free characterization of bacterial meningitis pathogens. Internet-accessible servers are being established to support bioinformatics analysis, data management, and data sharing among PHLs. However, establishing WGS capacity requires investments in laboratory infrastructure and technical knowledge, which is particularly challenging in resource-limited regions, including the African meningitis belt. Strategic WGS implementation is necessary to monitor the molecular epidemiology of meningococcal disease in these regions and construct a global view of meningococcal disease epidemiology.

Detailed Evaluation of Data Analysis Tools for Subtyping of Bacterial Isolates Based on Whole Genome Sequencing: Neisseria meningitidis as a Proof of Concept

Whole genome sequencing is increasingly recognized as the most informative approach for characterization of bacterial isolates. Success of the routine use of this technology in public health laboratories depends on the availability of well-characterized and verified data analysis methods. However, multiple subtyping workflows are now often being used for a single organism, and differences between them are not always well described. Moreover, methodologies for comparison of subtyping workflows, and assessment of their performance are only beginning to emerge. Current work focuses on the detailed comparison of WGS-based subtyping workflows and evaluation of their suitability for the organism and the research context in question. We evaluated the performance of pipelines used for subtyping of Neisseria meningitidis, including the currently widely applied cgMLST approach and different SNP-based methods. In addition, the impact of the use of different tools for detection and filtering of recombinant regions and of different reference genomes were tested. Our benchmarking analysis included both assessment of technical performance of the pipelines and functional comparison of the generated genetic distance matrices and phylogenetic trees. It was carried out using replicate sequencing datasets of high- and low-coverage, consisting mainly of isolates belonging to the clonal complex 269. We demonstrated that cgMLST and some of the SNP-based subtyping workflows showed very good performance characteristics and highly similar genetic distance matrices and phylogenetic trees with isolates belonging to the same clonal complex. However, only two of the tested workflows demonstrated reproducible results for a group of more closely related isolates. Additionally, results of the SNP-based subtyping workflows were to some level dependent on the reference genome used. Interestingly, the use of recombination-filtering software generally reduced the similarity between the gene-by-gene and SNP-based methodologies for subtyping of N. meningitidis. Our study, where N. meningitidis was taken as an example, clearly highlights the need for more benchmarking comparative studies to eventually contribute to a justified use of a specific WGS data analysis workflow within an international public health laboratory context.

A likelihood ratio-based approach for improved source attribution in microbiological forensic investigations

A common objective in microbial forensic investigations is to identify the origin of a recovered pathogenic bacterium by DNA sequencing. However, there is currently no consensus about how degrees of belief in such origin hypotheses should be quantified, interpreted, and communicated to wider audiences. To fill this gap, we have developed a concept based on calculating probabilistic evidential values for microbial forensic hypotheses. The likelihood-ratio method underpinning this concept is widely used in other forensic fields, such as human DNA matching, where results are readily interpretable and have been successfully communicated in juridical hearings. The concept was applied to two case scenarios of interest in microbial forensics: (1) identifying source cultures among series of very similar cultures generated by parallel serial passage of the Tier 1 pathogen Francisella tularensis, and (2) finding the production facilities of strains isolated in a real disease outbreak caused by the human pathogen Listeria monocytogenes. Evidence values for the studied hypotheses were computed based on signatures derived from whole genome sequencing data, including deep-sequenced low-frequency variants and structural variants such as duplications and deletions acquired during serial passages. In the F. tularensis case study, we were able to correctly assign fictive evidence samples to the correct culture batches of origin on the basis of structural variant data. By setting up relevant hypotheses and using data on cultivated batch sources to define the reference populations under each hypothesis, evidential values could be calculated. The results show that extremely similar strains can be separated on the basis of amplified mutational patterns identified by high-throughput sequencing. In the L. monocytogenes scenario, analyses of whole genome sequence data conclusively assigned the clinical samples to specific sources of origin, and conclusions were formulated to facilitate communication of the findings. Taken together, these findings demonstrate the potential of using bacterial whole genome sequencing data, including data on both low frequency SNP signatures and structural variants, to calculate evidence values that facilitate interpretation and communication of the results. The concept could be applied in diverse scenarios, including both epidemiological and forensic source tracking of bacterial infectious disease outbreaks.

Pulsed-field gel electrophoresis (PFGE): A review of the "gold standard" for bacteria typing and current alternatives

Pulsed-field gel electrophoresis (PFGE) is considered the "gold standard" for bacteria typing. The method involves enzyme restriction of bacteria DNA, separation of the restricted DNA bands using a pulsed-field electrophoresis chamber, followed by clonal assignment of bacteria based on PFGE banding patterns. Various PFGE protocols have been developed for typing different bacteria, leading it to be one of the most widely used methods for phylogenetic studies, food safety surveillance, infection control and outbreak investigations. On the other hand, as PFGE is lengthy and labourious, several PCR-based typing methods can be used as alternatives for research purposes. Recently, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and whole genome sequencing (WGS) have also been proposed for bacteria typing. In fact, as WGS provides more information, such as antimicrobial resistance and virulence of the tested bacteria in comparison to PFGE, more and more laboratories are currently transitioning from PFGE to WGS for bacteria typing. Nevertheless, PFGE will remain an affordable and relevant technique for small laboratories and hospitals in years to come.