Whole genome characterization of Neisseria meningitidis serogroup W isolates, circulating in Moscow

Abstract

Introduction. The invasive meningococcal disease (meningitis and/or septicemia) is actual problem of public health in Russia. Neisseria meningitidis isolates are classified into serogroups, PorA/FetA VRs, sequence types and clonal complexes. The growth of the invasive forms of meningococcal infection caused by isolates with «W: P1.5,2: F1-1: ST-11 (cc11)» profile requires attention for extended genotyping because the discriminating ability of classical MLST and antigens typing does not allow to answer the question about genetic and antigenic features of the pathogens and their epidemic potential. Materials and Methods. Four N. meningitidis serogroup W isolates associated with invasive meningococcal disease in Moscow (Russia) were characterized by next-generation sequencing. Three isolates were sequenced directly from cerebrospinal fluid samples and one -as a bacterial culture. All isolates were characterized earlier and the data were published in the PubMLST data base (id38565, id38573, id50225 and id50241). Genomic DNA was sequenced on Illumina MiSeq instrument. Results and Discussion. Obtained sequences allowed us to characterize four meningococci isolates for more than 1400 loci from the core genome MLST scheme. We have analyzed the core genome MLST scheme information about surface-antigen coding sequences. Housekeeping genes sequences were used to determine eMLST profile, ribosomal protein genes and some antibiotic resistance associated genes. We have characterized some ribosomal protein genes and antibiotic resistance associated genes. Based on eMLST profiles we noticed that there are at list two clones of N. meningitidis serogroup W inside complex ST-11/ET-37 clonal complex circulating in Moscow during 2016. An eMLST profile of isolates id50225 and id50241 differs in 3 loci out of 20. Application of the approach based on next-generation sequencing in routine epidemiological surveillance dramatically increases the amount of data and genotyping discriminating ability.