Genomic investigation of a suspected outbreak of Legionella pneumophila ST82 reveals undetected heterogeneity by the present gold-standard methods, Denmark, July to November 2014

Schjørring, Susanne; Stegger, Marc; Kjelsø, Charlotte; Lilje, Berit; Bangsborg, Jette; Petersen, Randi Føns; David, Sophia; Uldum, Søren A.

doi:10.2807/1560-7917.es.2017.22.25.30558

Cited by 18 publications

(13 citation statements)

References 34 publications

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“…1B). There is a growing awareness of single-source polyphyletic Lpn-SG1 outbreaks (8,10,13,30,31). These data all point to the need for a different approach in order to use molecular epidemiology and genomics in support of Legionella outbreak investigations.…”

Section: Discussionmentioning

confidence: 99%

“…Increasingly, whole-genome sequencing (WGS) is being employed to investigate individual Legionella outbreaks, and the insights obtained from these high-resolution comparisons are challenging our expectations regarding common-source outbreaks, which usually are characterized by a single strain or genotype (5)(6)(7)(8)(9). It is becoming evident that outbreaks can be caused by multiple cocirculating L. pneumophila genotypes (5,10) and that L. pneumophila core genomes can be surprisingly conserved across space and time (8,(11)(12)(13).…”

mentioning

confidence: 99%

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A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

Buultjens

Chua

Baines

et al. 2017

Appl Environ Microbiol

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Public health agencies are increasingly relying on genomics during Legionnaires' disease investigations. However, the causative bacterium () has an unusual population structure, with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires' disease outbreaks can be caused by multiple 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 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 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 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 genetic diversity extremes and provide objective source attribution data for outbreak investigations. 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 , which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of 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 isolates to build statistical models that predict with high confidence the environmental source of clinical during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

Buultjens

Chua

Baines

et al. 2017

Appl Environ Microbiol

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“…Beside single outbreak investigations, WGS-based typing data also support comparison across studies [16]. Nevertheless, many recent reports on Lp using WGS have mainly focused on single outbreaks [5,8,12,17,18]. These studies also did not assess the complexity of environmental sources in great detail, whereby the sampling strategy of the environmental isolates and the diversity of strains in the sources remain unclear.…”

Section: Introductionmentioning

confidence: 89%

“…Infections with Lp are acquired via inhalation of contaminated aerosolised water [4]. Various environmental sources are known, such as showers [5,6], hot tubs, fountains, dishwashers [7], hot water tanks, larger plumbing systems [8] and air-conditioner cooling towers (ACCT) [9-12].…”

Section: Introductionmentioning

confidence: 99%

Air-conditioner cooling towers as complex reservoirs and continuous source of Legionella pneumophila infection evidenced by a genomic analysis study in 2017, Switzerland

et al. 2019

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IntroductionWater supply and air-conditioner cooling towers (ACCT) are potential sources of Legionella pneumophila infection in people. During outbreaks, traditional typing methods cannot sufficiently segregate L. pneumophila strains to reliably trace back transmissions to these artificial water systems. Moreover, because multiple L. pneumophila strains may be present within these systems, methods to adequately distinguish strains are needed. Whole genome sequencing (WGS) and core genome multilocus sequence typing (cgMLST), with their higher resolution are helpful in this respect. In summer 2017, the health administration of the city of Basel detected an increase of L. pneumophila infections compared with previous months, signalling an outbreak.AimWe aimed to identify L. pneumophila strains populating suspected environmental sources of the outbreak, and to assess the relations between these strains and clinical outbreak strains.MethodsAn epidemiological and WGS-based microbiological investigation was performed, involving isolates from the local water supply and two ACCTs (n = 60), clinical outbreak and non-outbreak related isolates from 2017 (n = 8) and historic isolates from 2003–2016 (n = 26).ResultsIn both ACCTs, multiple strains were found. Phylogenetic analysis of the ACCT isolates showed a diversity of a few hundred allelic differences in cgMLST. Furthermore, two isolates from one ACCT showed no allelic differences to three clinical isolates from 2017. Five clinical isolates collected in the Basel area in the last decade were also identical in cgMLST to recent isolates from the two ACCTs.ConclusionCurrent outbreak-related and historic isolates were linked to ACCTs, which form a complex environmental habitat where strains are conserved over years.

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“…Increasingly, whole genome sequencing (WGS) is being employed to investigate individual Legionella outbreaks and the insights obtained from these high-resolution comparisons are challenging our expectations regarding common-source outbreaks, which usually are characterized by a single strain or genotype (5-9). It is becoming evident that outbreaks can be caused by multiple co-circulating L. pneumophila genotypes (5, 10) and that L. pneumophila core genomes can be surprisingly conserved across space and time (8, 11-13).…”

Section: Introductionmentioning

confidence: 99%

A supervised statistical learning approach for accurateLegionella pneumophilasource attribution during outbreaks

Buultjens

Chua

Baines

et al. 2017

Preprint

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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 onL. 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, involving 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’ 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 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.

show abstract

Genomic investigation of a suspected outbreak of Legionella pneumophila ST82 reveals undetected heterogeneity by the present gold-standard methods, Denmark, July to November 2014

Cited by 18 publications

References 34 publications

A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

Air-conditioner cooling towers as complex reservoirs and continuous source of Legionella pneumophila infection evidenced by a genomic analysis study in 2017, Switzerland

A supervised statistical learning approach for accurateLegionella pneumophilasource attribution during outbreaks

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