Whole genome sequencing of Neisseria meningitidis Y isolates collected in the Czech Republic in 1993-2018

Invasive meningococcal disease in South-Eastern European countries: Do we need to revise vaccination strategies?

Invasive meningococcal disease (IMD) is an acute life-threatening infection caused by the gram-negative bacterium, Neisseria meningitidis. Globally, there are approximately half a million cases of IMD each year, with incidence varying across geographical regions. Vaccination has proven to be successful against IMD, as part of controlling outbreaks, and when incorporated into national immunization programs. The South-Eastern Europe Meningococcal Advocacy Group (including representatives from Croatia, the Czech Republic, Greece, Hungary, Poland, Romania, Serbia, Slovenia and Ukraine) was formed in order to discuss the potential challenges of IMD faced in the region. The incidence of IMD across Europe has been relatively low over the past decade; of the countries that came together for the South-Eastern Meningococcal Advocacy Group, the notification rates were lower than the European average for some country. The age distribution of IMD cases was highest in infants and children, and most countries also had a further peak in adolescents and young adults. Across the nine included countries between 2010 and 2020, the largest contributors to IMD were serogroups B and C; however, each individual country had distinct patterns for serogroup distribution. Along with the variations in epidemiology of IMD between the included countries, vaccination policies also differ.

Whole genome analysis of Neisseria meningitidis isolates from invasive meningococcal disease collected in the Czech Republic over 28 years (1993-2020)

Invasive meningococcal disease belongs among the most dangerous infectious diseases in the world. Several polysaccharide conjugate vaccines against serogroups A, C, W and Y are available and two recombinant peptide vaccines against serogroup B (MenB vaccines) have been developed: MenB-4C (Bexsero) and MenB-fHbp (Trumenba). The aim of this study was to define the clonal composition of the Neisseria meningitidis population in the Czech Republic, to determine changes in this population over time and to estimate the theoretical coverage of isolates by MenB vaccines. This study presents the analysis of whole genome sequencing data of 369 Czech N. meningitidis isolates from invasive meningococcal disease covering 28 years. Serogroup B isolates (MenB) showed high heterogeneity and the most common clonal complexes were cc18, cc32, cc35, cc41/44, and cc269. Isolates of clonal complex cc11 were predominately serogroup C (MenC). The highest number of serogroup W isolates (MenW) belonged to clonal complex cc865, which we described as exclusive to the Czech Republic. Our study supports the theory that this cc865 subpopulation originated in the Czech Republic from MenB isolates by a capsule switching mechanism. A dominant clonal complex of serogroup Y isolates (MenY) was cc23, which formed two genetically quite distant subpopulations and which showed constant representation throughout the observed period. The theoretical coverage of isolates by two MenB vaccines was determined using the Meningococcal Deduced Vaccine Antigen Reactivity Index (MenDeVAR). Estimated Bexsero vaccine coverage was 70.6% (for MenB) and 62.2% (for MenC, W, Y). For Trumenba vaccine, estimated coverage was 74.6% (for MenB) and 65.7% (for MenC, W, Y). Our results demonstrated sufficient coverage of Czech heterogeneous population of N. meningitidis with MenB vaccines and, together with surveillance data on invasive meningococcal disease in the Czech Republic, were the basis for updating recommendations for vaccination against invasive meningococcal disease.

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.

Genome sequence assembly algorithms and misassembly identification methods

The sequence assembly algorithms have rapidly evolved with the vigorous growth of genome sequencing technology over the past two decades. Assembly mainly uses the iterative expansion of overlap relationships between sequences to construct the target genome. The assembly algorithms can be typically classified into several categories, such as the Greedy strategy, Overlap-Layout-Consensus (OLC) strategy, and de Bruijn graph (DBG) strategy. In particular, due to the rapid development of third-generation sequencing (TGS) technology, some prevalent assembly algorithms have been proposed to generate high-quality chromosome-level assemblies. However, due to the genome complexity, the length of short reads, and the high error rate of long reads, contigs produced by assembly may contain misassemblies adversely affecting downstream data analysis. Therefore, several read-based and reference-based methods for misassembly identification have been developed to improve assembly quality. This work primarily reviewed the development of DNA sequencing technologies and summarized sequencing data simulation methods, sequencing error correction methods, various mainstream sequence assembly algorithms, and misassembly identification methods. A large amount of computation makes the sequence assembly problem more challenging, and therefore, it is necessary to develop more efficient and accurate assembly algorithms and alternative algorithms.