Hierarchical genomic analysis of carried and invasive serogroup A Neisseria meningitidis during the 2011 epidemic in Chad

Abstract

BackgroundSerogroup A Neisseria meningitidis (NmA) was the cause of the 2011 meningitis epidemics in Chad. This bacterium, often carried asymptomatically, is considered to be an “accidental pathogen”; however, the transition from carriage to disease phenotype remains poorly understood. This study examined the role genetic diversity might play in this transition by comparing genomes from geographically and temporally matched invasive and carried NmA isolates.ResultsAll 23 NmA isolates belonged to the ST-5 clonal complex (cc5). Ribosomal MLST comparison with other publically available NmA:cc5 showed that isolates were closely related, although those from Chad formed two distinct branches and did not cluster with other NmA, based on their MLST profile, geographical and temporal location. Whole genome MLST (wgMLST) comparison identified 242 variable genes among all Chadian isolates and clustered them into three distinct phylogenetic groups (Clusters 1, 2, and 3): no systematic clustering by disease or carriage source was observed. There was a significant difference (p = 0.0070) between the mean age of the individuals from which isolates from Cluster 1 and Cluster 2 were obtained, irrespective of whether the person was a case or a carrier.ConclusionsWhole genome sequencing provided high-resolution characterization of the genetic diversity of these closely related NmA isolates. The invasive meningococcal isolates obtained during the epidemic were not homogeneous; rather, a variety of closely related but distinct clones were circulating in the human population with some clones preferentially colonizing specific age groups, reflecting a potential age-related niche adaptation. Systematic genetic differences were not identified between carriage and disease isolates consistent with invasive meningococcal disease being a multi-factorial event resulting from changes in host-pathogen interactions along with the bacterium.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3789-0) contains supplementary material, which is available to authorized users.

Narrative review of methods and findings of recent studies on the carriage of meningococci and other Neisseria species in the African Meningitis Belt

“…Few studies of whole genome sequencing of meningococcal carriage isolates obtained in the African meningitis belt have been reported. A study of Nm A isolates, including both carriage and invasive isolates, collected during an epidemic in Chad demonstrated differences between closely related strains that were indistinguishable by MLST analysis . The bacterial clusters identified were linked to age group but did not differ between carriage and invasive disease isolates.…”

“…Twenty papers were identified which described meningococcal carriage surveys conducted in nine African meningitis belt countries and reported since 2007 [4][5][6][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. Sixteen of these surveys were conducted prior to the deployment of the group A meningococcal conjugate vaccine (MenAfriVac â ).…”

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

“…The kSNP pipeline, allowing SNP discovery based on k-mer analysis (Gardner et al, 2015), has been applied for subtyping of N. meningitidis in various studies (Stefanelli et al, 2016;Diallo et al, 2017;Whaley et al, 2018). In this work, kSNP 3.0 was tested with assembled data (kSNPCon, from kSNP Contigs) and trimmed reads (kSNPRead, from kSNP Reads) as input.…”

“…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).…”

“…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.…”