The Meningococcal Antigen Typing System (MATS) was developed to identify meningococcus group B strains with a high likelihood of being covered by the 4CMenB vaccine, but is limited by the requirement for viable isolates from culture-confirmed cases. We examined if antigen genotyping could complement MATS in predicting strain coverage by the 4CMenB vaccine. From a panel of 3912 MATS-typed invasive meningococcal disease isolates collected in England and Wales in 2007-2008, 2014-2015 and 2015-2016, and in 16 other countries in 2000-2015, 3481 isolates were also characterized by antigen genotyping. Individual associations between antigen genotypes and MATS coverage for each 4CMenB component were used to define a genetic MATS (gMATS). gMATS estimates were compared with England and Wales human complement serum bactericidal assay (hSBA) data and vaccine effectiveness (VE) data from England. Overall, 81% of the strain panel had genetically predictable MATS coverage, with 92% accuracy and highly concordant results across national panels (Lin's accuracy coefficient, 0.98; root-mean-square deviation, 6%). England and Wales strain coverage estimates were 72-73% by genotyping (66-73% by MATS), underestimating hSBA values after four vaccine doses (88%) and VE after two doses (83%). The gMATS predicted strain coverage in other countries was 58-88%. gMATS can replace MATS in predicting 4CMenB strain coverage in four out of five cases, without requiring a cultivable isolate, and is open to further improvement. Both methods underestimated VE in England. Strain coverage predictions in other countries matched or exceeded England and Wales estimates.

Meningococcal (Neisseria meningitidis) serogroup B (MenB) strain antigens are diverse and a limited number of strains can be evaluated using the human serum bactericidal antibody (hSBA) assay. The genetic Meningococcal Antigen Typing System (gMATS) was developed to predict the likelihood of coverage for large numbers of isolates by the 4CMenB vaccine, which includes antigens Neisseria adhesin A (NadA), Neisserial Heparin-Binding Antigen (NHBA), factor H-binding protein (fHbp), and Porin A (PorA). In this study, we characterized by whole-genome analyses 284 invasive MenB isolates collected from 2010 to 2014 by the Argentinian National Laboratories Network (52-61 isolates per year). Strain coverage was estimated by gMATS on all isolates and by hSBA assay on 74 randomly selected isolates, representative of the whole panel. The four most common clonal complexes (CCs), accounting for 81.3% of isolates, were CC-865 (75 isolates, 26.4%), CC-32 (59, 20.8%), CC-35 (59, 20.8%), and CC-41/44 (38, 13.4%). Vaccine antigen genotyping showed diversity. The most prevalent variants/peptides were fHbp variant 2, NHBA peptides 24, 21, and 2, and PorA variable region 2 profiles 16-36 and 14. The nadA gene was present in 66 (23.2%) isolates. Estimated strain coverage by hSBA assay showed 78.4% of isolates were killed by pooled adolescent sera, and 51.4% and 64.9% (based on two different thresholds) were killed by pooled infant sera. Estimated coverage by gMATS (61.3%; prediction interval: 55.5%, 66.7%) was consistent with the infant hSBA assay results. Continued genomic surveillance is needed to evaluate the persistence of major MenB CCs in Argentina.

Background: Multi-locus sequence typing (MLST) is a standard typing technique used to associate a sequence type (ST) to a bacterial isolate. When the output of whole genome sequencing (WGS) of a sample is available the ST can be assigned directly processing the read-set. Current approaches employ reads mapping (SRST2) against the MLST loci, k-mer distribution (stringMLST), selective assembly (GRAbB) or whole genome assembly (BIGSdb) followed by BLASTn sequence query. Here we present STRAIN (ST Reduced Assembly IdentificatioN), an R package that implements a hybrid strategy between assembly and mapping of the reads to assign the ST to an isolate starting from its read-sets. Results: Analysis of 540 publicly accessible Illumina read sets showed STRAIN to be more accurate at correct allele assignment and new alleles identification compared to SRTS2, stringMLST and GRAbB. STRAIN assigned correctly 3666 out of 3780 alleles (capability to identify correct alleles 97%) and, when presented with samples containing new alleles, identified them in 3730 out of 3780 STs (capability to identify new alleles 98.7%) of the cases. On the same dataset the other tested tools achieved lower capability to identify correct alleles (from 28.5 to 96.9%) and lower capability to identify new alleles (from 1.1 to 97.1%). Conclusions: STRAIN is a new accurate method to assign the alleles and ST to an isolate by processing the raw reads output of WGS. STRAIN is also able to retrieve new allele sequences if present. Capability to identify correct and new STs/alleles, evaluated on a benchmark dataset, are higher than other existing methods. STRAIN is designed for single allele typing as well as MLST. Its implementation in R makes allele and ST assignment simple, direct and prompt to be integrated in wider pipeline of downstream bioinformatics analyses.