Visualizing variation within Global Pneumococcal Sequence Clusters (GPSCs) and country population snapshots to contextualize pneumococcal isolates

Abstract

Knowledge of pneumococcal lineages, their geographic distribution and antibiotic resistance patterns, can give insights into global pneumococcal disease. We provide interactive bioinformatic outputs to explore such topics, aiming to increase dissemination of genomic insights to the wider community, without the need for specialist training. We prepared 12 country-specific phylogenetic snapshots, and international phylogenetic snapshots of 73 common Global Pneumococcal Sequence Clusters (GPSCs) previously defined using PopPUNK, and present them in Microreact. Gene presence and absence defined using Roary, and recombination profiles derived from Gubbins are presented in Phandango for each GPSC. Temporal phylogenetic signal was assessed for each GPSC using BactDating. We provide examples of how such resources can be used. In our example use of a country-specific phylogenetic snapshot we determined that serotype 14 was observed in nine unrelated genetic backgrounds in South Africa. The international phylogenetic snapshot of GPSC9, in which most serotype 14 isolates from South Africa were observed, highlights that there were three independent sub-clusters represented by South African serotype 14 isolates. We estimated from the GPSC9-dated tree that the sub-clusters were each established in South Africa during the 1980s. We show how recombination plots allowed the identification of a 20 kb recombination spanning the capsular polysaccharide locus within GPSC97. This was consistent with a switch from serotype 6A to 19A estimated to have occured in the 1990s from the GPSC97-dated tree. Plots of gene presence/absence of resistance genes ( tet , erm , cat ) across the GPSC23 phylogeny were consistent with acquisition of a composite transposon. We estimated from the GPSC23-dated tree that the acquisition occurred between 1953 and 1975. Finally, we demonstrate the assignment of GPSC31 to 17 externally generated pneumococcal serotype 1 assemblies from Utah via Pathogenwatch. Most of the Utah isolates clustered within GPSC31 in a USA-specific clade with the most recent common ancestor estimated between 1958 and 1981. The resources we have provided can be used to explore to data, test hypothesis and generate new hypotheses. The accessible assignment of GPSCs allows others to contextualize their own collections beyond the data presented here.

Producing polished prokaryotic pangenomes with the Panaroo pipeline

“…To demonstrate the utility of using the corrected pangenome graph to infer gene gain/loss rates and pangenome size, we used the FMG model to investigate 51 of the major Global Pneumococcal Sequence Clusters (GPSCs) for which reliable dated phylogenies could be constructed [42]. The major clades of the pneumococcus have distinct accessory gene profiles [43].…”

Genome-Wide Analysis of the Temporal Genetic Changes in Streptococcus pneumoniae Isolates of Genotype ST320 and Serotype 19A from South Korea

“…We analyzed the whole-genome sequences of 179 S. pneumoniae 19A-ST320 isolates including 153 isolates that belonged to GPSC1 [ 15 ].15 A phylogenetic tree based on the SNPs showed multichotomous branching with several obvious sub-clusters ( Figure 2 A). In the phylogenetic tree, the S. pneumoniae isolates from South Korea were not grouped into limited clusters among the isolates from several continents.…”

“…Our data suggested that the frequency of recombination might have been different between pneumococcal isolates even if they belonged to the same lineage. The introduction of foreign DNA from the environment into the chromosome via transformation and homologous recombination is known to contribute more towards genetic variation than mutations [ 15 ]. It has been estimated that nearly 90% of all genetic variations in S. pneumoniae have been introduced via recombination [ 6 ].…”

Sugar-Coated Killer: Serotype 3 Pneumococcal Disease

“…They aimed to study S. pneumoniae serotype, antibiotic resistance, and invasiveness in association with the overall genetic background of the isolates. They partitioned lineages into Global Pneumococcal Sequence Clusters (GPSCs) that were then associated with the features of interest listed above, as well as clonal complex (CC) and of course serotype ( Gladstone et al., 2019 ; Lo et al., 2019 ; Gladstone et al., 2020 ). A CC groups related bacterial strains through the identification of allele variations or sequence types (STs) within seven highly conserved housekeeping genes ( aroE , gki , gdh , xpt , spi , ddl , and recP ) ( Enright et al., 2001 ).…”

“…Genes encoding key virulence determinants such as NanA, PspA, and CbpA showed high levels of variability between the three clades. Variations in each of these genes has been shown to impact pneumococcal virulence in a variety of experimental conditions ( Camara et al., 1994 ; Enright et al., 2001 ; Ren et al., 2003 ; Shivshankar et al., 2009 ; Sanchez et al., 2010 ; Georgieva et al., 2018 ; Azarian et al., 2018 ; Gladstone et al., 2019 ; Lo et al., 2019 ; Sheppard et al., 2019 ; Gladstone et al., 2020 ). Several variations in genes responsible for increased drug resistance, such as Tet32, were also observed within clades Iβ and II, but not within Iα isolates ( Azarian et al., 2018 ; Groves et al., 2019 ).…”