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.
Highlights
Meningococcal disease is a serious and life-threatening bacterial infection, caused by Neisseria meningitidis (Nm), with a case fatality ratio of 10–15%; 11–19% of survivors experience long-term sequelae[1]
Deletions and frameshifts were observed in porA and fetA among outbreak isolates; these changes have been previously reported to occur in NmB and NmC isolates[38]
We focused on one recombination-adjusted method (SNIPPY) and a method that does not account for recombination, and compared them with pulse field gel electrophoresis (PFGE). Core genome MLST (cgMLST) is widely used for typing meningococcal isolates, and uses de novo assembly data
Summary
Meningococcal disease is a serious and life-threatening bacterial infection, caused by Neisseria meningitidis (Nm), with a case fatality ratio of 10–15%; 11–19% of survivors experience long-term sequelae[1]. While Sanger sequencing-based typing using MLST genes and genes coding for outer membrane proteins is useful to define bacterial molecular epidemiology and population structure, it does not provide sufficient discriminatory power to differentiate outbreak strains from sporadic strains or strains with high genetic similarity. The rapid turnaround time and availability of automated WGS analysis tools make it possible to use this technology for outbreak investigation and surveillance of infectious disease[27,28,29]. WGS can distinguish strains based on differences in single nucleotide polymorphisms (SNPs) or multilocus genes across the whole genome, an essential feature needed in Nm outbreak investigations when strains show high genetic similarity. This study aimed to re-characterize NmB and NmC outbreaks in the United States during 2009–2015 using WGS-based methods and to develop an optimal WGS-based typing scheme for Nm outbreak investigations
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