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

Abstract

Although rare in the U.S., outbreaks due to Neisseria meningitidis do occur. Rapid, early outbreak detection is important for timely public health response. In this study, we characterized U.S. meningococcal isolates (N = 201) from 15 epidemiologically defined outbreaks (2009–2015) along with temporally and geographically matched sporadic isolates using multilocus sequence typing, pulsed-field gel electrophoresis (PFGE), and six whole genome sequencing (WGS) based methods. Recombination-corrected maximum likelihood (ML) and Bayesian phylogenies were reconstructed to identify genetically related outbreak isolates. All WGS analysis methods showed high degree of agreement and distinguished isolates with similar or indistinguishable PFGE patterns, or the same strain genotype. Ten outbreaks were caused by a single strain; 5 were due to multiple strains. Five sporadic isolates were phylogenetically related to 2 outbreaks. Analysis of 9 outbreaks using timed phylogenies identified the possible origin and estimated the approximate time that the most recent common ancestor emerged for outbreaks analyzed. U.S. meningococcal outbreaks were caused by single- or multiple-strain introduction, with organizational outbreaks mainly caused by a clonal strain and community outbreaks by divergent strains. WGS can infer linkage of meningococcal cases when epidemiological links are uncertain. Accurate identification of outbreak-associated cases requires both WGS typing and epidemiological data.

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel

“…To evaluate BioHansel's SNP-detection accuracy, we compared its concordance with Snippy (Seemann, 2015), a traditional SNP-detection workflow. We chose to compare BioHansel with Snippy as it is popular, built for speed, and has demonstrated accuracy that compares favourably to other traditional SNP-detection workflows (Whaley et al, 2018).…”

“…The choice of typing approach depends on its intended application (for a review see Nadon et al, 2017), although in general, gene-by-gene approaches are popular for routine surveillance activities, whereas SNP-based approaches are used when extra discriminatory power is required, or for pathogens that are not amenable to gene-by-gene methods. Comparisons of gene-by-gene and SNP-based approaches show that these methods both produce concordant and complementary results Whaley et al, 2018;Jagadeesan et al, 2019). Regardless of the method used, the analysis of WGS data can be highly complex and can require high performance computing and specialized genomics expertise to carry out and to interpret the results (Carriço et al, 2018).…”

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

“…In the majority of the studies, subtyping of N. meningitidis is being carried out using a wellestablished cgMLST scheme, developed by Jolley et al (2018). Recently, a number of works have also applied assembly-, kmerand mapping-based SNP approaches in some cases combined with recombination detection tools, for characterization of N. meningitidis (Figure 1) (Lamelas et al, 2014(Lamelas et al, , 2017Mustapha et al, 2015;Sater et al, 2015;Stefanelli et al, 2016;Bårnes et al, 2017;Diallo et al, 2017;Tzeng et al, 2017;Hao et al, 2018;Whaley et al, 2018). However, it is not always that clear which method is the best to use for a given research question Whaley et al, 2018).…”

“…Recently, a number of works have also applied assembly-, kmerand mapping-based SNP approaches in some cases combined with recombination detection tools, for characterization of N. meningitidis (Figure 1) (Lamelas et al, 2014(Lamelas et al, , 2017Mustapha et al, 2015;Sater et al, 2015;Stefanelli et al, 2016;Bårnes et al, 2017;Diallo et al, 2017;Tzeng et al, 2017;Hao et al, 2018;Whaley et al, 2018). However, it is not always that clear which method is the best to use for a given research question Whaley et al, 2018). Several studies demonstrated a high degree of agreement between the different SNP-based approaches and cgMLST on distinguishing epidemiologically linked isolates (Bårnes et al, 2017;Diallo et al, 2017;Whaley et al, 2018), but no detailed evaluation has been carried out describing the differences in performance and output of cgMLST and SNP-based workflows, and how they are affected by the use of tools that detect and mask recombinant regions.…”

“…However, it is not always that clear which method is the best to use for a given research question Whaley et al, 2018). Several studies demonstrated a high degree of agreement between the different SNP-based approaches and cgMLST on distinguishing epidemiologically linked isolates (Bårnes et al, 2017;Diallo et al, 2017;Whaley et al, 2018), but no detailed evaluation has been carried out describing the differences in performance and output of cgMLST and SNP-based workflows, and how they are affected by the use of tools that detect and mask recombinant regions. Given the exceptionally high rates of homologous gene transfer and structural variation, the variety of methods which have been used to subtype this organism, and the lack of a benchmarking analysis, N. meningitidis makes an interesting proof of concept to (a) evaluate the technical performance of the individual subtyping workflows, and recombination filtering tools and (b) to compare the output obtained with the different data analysis approaches.…”