A graph-based approach for the visualisation and analysis of bacterial pangenomes.

BACKGROUND: The advent of low cost, high throughput DNA sequencing has led to the availability of thousands of complete genome sequences for a wide variety of bacterial species. Examining and interpreting genetic variation on this scale represents a significant challenge to existing methods of data analysis and visualisation. RESULTS: Starting with the output of standard pangenome analysis tools, we describe the generation and analysis of interactive, 3D network graphs to explore the structure of bacterial populations, the distribution of genes across a population, and the syntenic order in which those genes occur, in the new open-source network analysis platform, Graphia. Both the analysis and the visualisation are scalable to datasets of thousands of genome sequences. CONCLUSIONS: We anticipate that the approaches presented here will be of great utility to the microbial research community, allowing faster, more intuitive, and flexible interaction with pangenome datasets, thereby enhancing interpretation of these complex data.

Epidemiological analysis of Legionnaires' disease in Scotland: a genomic study.

BACKGROUND: Legionella pneumophila is the main cause of a severe pneumonic illness known as Legionnaires' disease and is a global public health threat. Whole-genome sequencing (WGS) can be applied to trace environmental origins of L pneumophila infections, providing information to guide appropriate interventions. We aim to explore the evolutionary and epidemiological relationships in a 36-year Scottish L pneumophila reference isolate collection. METHODS: We investigated the genomic epidemiology of Legionnaires' disease over 36 years in Scotland, comparing genome sequences for all clinical L pneumophila isolates (1984-2020) with a sequence dataset of 3211 local and globally representative isolates. We used a stratified clustering approach to capture epidemiological relationships by core genome Multi-locus Sequence Typing, followed by high-resolution phylogenetic analysis of clusters to measure diversity and evolutionary relatedness in context with epidemiological metadata. FINDINGS: Clustering analysis showed that 111 (57·5 %) of 193 of L pneumophila infections in Scotland were caused by ten endemic lineages with a wide temporal and geographical distribution. Phylogenetic analysis of L pneumophila identified hospital-associated sublineages that had been detected in the hospital environment up to 19 years. Furthermore, 12 (30·0%) of 40 community-associated infections (excluding a single, large outbreak) that occurred over a 13 year period (from 2000 to 2013) were caused by a single widely distributed endemic clone (ST37), consistent with enhanced human pathogenicity. Finally, our analysis revealed clusters linked by national or international travel to distinct geographical regions, indicating several previously unrecognised travel links between closely related isolates (fewer than five single nucleotide polymorphisms) connected by geography. INTERPRETATION: Our analysis reveals the existence of previously undetected endemic clones of L pneumophila that existed for many years in hospital, community, and travel-associated environments. In light of these findings, we propose that cluster and outbreak definitions should be reconsidered, and propose WGS-based surveillance as a critical public health tool for real-time identification and mitigation of clinically important endemic clones. FUNDING: Chief Scientist Office, Biotechnology and Biological Sciences Research Council (UK), Medical Research Council Precision Medicine Doctoral Training Programme, Wellcome Trust, and Medical Research Council (UK).

Genome analysis of Legionella pneumophila ST23 from various countries reveals highly similar strains.

Legionella pneumophila serogroup 1 (Lp1) sequence type (ST) 23 is one of the most commonly detected STs in Italy where it currently causes all investigated outbreaks. ST23 has caused both epidemic and sporadic cases between 1995 and 2018 and was analysed at genomic level and compared with ST23 isolated in other countries to determine possible similarities and differences. A core genome multi-locus sequence typing (cgMLST), based on a previously described set of 1,521 core genes, and single-nucleotide polymorphisms (SNPs) approaches were applied to an ST23 collection including genomes from Italy, France, Denmark and Scotland. DNAs were automatically extracted, libraries prepared using NextEra library kit and MiSeq sequencing performed. Overall, 63 among clinical and environmental Italian Lp1 isolates and a further seven and 11 ST23 from Denmark and Scotland, respectively, were sequenced, and pangenome analysed. Both cgMLST and SNPs analyses showed very few loci and SNP variations in ST23 genomes. All the ST23 causing outbreaks and sporadic cases in Italy and elsewhere, were phylogenetically related independent of year, town or country of isolation. Distances among the ST23s were further shortened when SNPs due to horizontal gene transfers were removed. The Lp1 ST23 isolated in Italy have kept their monophyletic origin, but they are phylogenetically close also to ST23 from other countries. The ST23 are quite widespread in Italy, and a thorough epidemiological investigation is compelled to determine sources of infection when this ST is identified in both LD sporadic cases and outbreaks.

Population analysis of Legionella pneumophila reveals a basis for resistance to complement-mediated killing.

Legionella pneumophila is the most common cause of the severe respiratory infection known as Legionnaires' disease. However, the microorganism is typically a symbiont of free-living amoeba, and our understanding of the bacterial factors that determine human pathogenicity is limited. Here we carried out a population genomic study of 902 L. pneumophila isolates from human clinical and environmental samples to examine their genetic diversity, global distribution and the basis for human pathogenicity. We find that the capacity for human disease is representative of the breadth of species diversity although some clones are more commonly associated with clinical infections. We identified a single gene (lag-1) to be most strongly associated with clinical isolates. lag-1, which encodes an O-acetyltransferase for lipopolysaccharide modification, has been distributed horizontally across all major phylogenetic clades of L. pneumophila by frequent recent recombination events. The gene confers resistance to complement-mediated killing in human serum by inhibiting deposition of classical pathway molecules on the bacterial surface. Furthermore, acquisition of lag-1 inhibits complement-dependent phagocytosis by human neutrophils, and promoted survival in a mouse model of pulmonary legionellosis. Thus, our results reveal L. pneumophila genetic traits linked to disease and provide a molecular basis for resistance to complement-mediated killing.