Dataset Information

Population Genomics of Legionella longbeachae and Hidden Complexities of Infection Source Attribution.

ABSTRACT: Legionella longbeachae is the primary cause of legionellosis in Australasia and Southeast Asia and an emerging pathogen in Europe and the United States; however, our understanding of the population diversity of L. longbeachae from patient and environmental sources is limited. We analyzed the genomes of 64 L. longbeachae isolates, of which 29 were from a cluster of legionellosis cases linked to commercial growing media in Scotland in 2013 and 35 were non-outbreak-associated isolates from Scotland and other countries. We identified extensive genetic diversity across the L. longbeachae species, associated with intraspecies and interspecies gene flow, and a wide geographic distribution of closely related genotypes. Of note, we observed a highly diverse pool of L. longbeachae genotypes within compost samples that precluded the genetic establishment of an infection source. These data represent a view of the genomic diversity of L. longbeachae that will inform strategies for investigating future outbreaks.

SUBMITTER: Bacigalupe R

PROVIDER: S-EPMC5403047 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Population Genomics of Legionella longbeachae and Hidden Complexities of Infection Source Attribution.

Bacigalupe Rodrigo R Lindsay Diane D Edwards Giles G Fitzgerald J Ross JR

Emerging infectious diseases 20170501 5

Legionella longbeachae is the primary cause of legionellosis in Australasia and Southeast Asia and an emerging pathogen in Europe and the United States; however, our understanding of the population diversity of L. longbeachae from patient and environmental sources is limited. We analyzed the genomes of 64 L. longbeachae isolates, of which 29 were from a cluster of legionellosis cases linked to commercial growing media in Scotland in 2013 and 35 were non-outbreak-associated isolates from Scotland ...[more]

PMID: 28418314

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Project description:Fundamental to effective Legionnaires' disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent, Legionella pneumophila. Genomics has revolutionized pathogen surveillance, but L. pneumophila has a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here, we present a powerful machine learning approach that assigns the geographical source of Legionnaires' disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534 L. pneumophila genome sequences, including 149 genomes linked to 20 previously reported Legionnaires' disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmental L. pneumophila genomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance-agreement with epidemiological information. Our multivariate statistical learning approach maximizes the use of genomic variation data and is thus well-suited for supporting Legionnaires' disease outbreak investigations.IMPORTANCEIdentifying the sources of Legionnaires' disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila, the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires' disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires' disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.

Project description:Public health agencies are increasingly relying on genomics during Legionnaires' disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure, with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires' disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia, between 1994 and 2014. This collection included one of the largest reported Legionnaires' disease outbreaks, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK.

Dataset Information

Population Genomics of Legionella longbeachae and Hidden Complexities of Infection Source Attribution.

Publications

Population Genomics of Legionella longbeachae and Hidden Complexities of Infection Source Attribution.

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