Improvements to the Success of Outbreak Investigations of Legionnaires' Disease: 40 Years of Testing and Investigation in New York State

Genotypic and phenotypic profiling of 127 Legionella pneumophila strains: Insights into regional spread

Given the recent global surge in Legionnaires' disease cases, the monitoring of Legionella pneumophila becomes increasingly crucial. Epidemiological cases often stem from local outbreaks rather than widespread dissemination, emphasizing the need to study the characteristics of this pathogen at a local level. This study focuses on isolates of L. pneumophila in the Italian region of Friuli Venezia Giulia to assess specific genotype and phenotype distribution over time and space. To this end, a total of 127 L. pneumophila strains isolated between 2005 and 2017 within national surveillance programs were analysed. Rep-PCR, RAPD, and Sau-PCR were used for genotypic characterization, while phenotypic characterization was conducted through fatty acids analysis. RAPD and Sau-PCR effectively assessed genetic characteristics, identifying different profiles for the isolates and excluding the presence of clones. Although Sau-PCR is rarely used to analyse this pathogen, it emerged as the most discriminatory technique. Phenotypically, hierarchical cluster analysis categorized strains into three groups based on varying membrane fatty acid percentages. However, both phenotypic and genotypic analyses revealed a ubiquitous profile distribution at a regional level. These results suggest an absence of correlations between strain profiles, geographical location, and isolation time, indicating instead high variability and strain dissemination within this region.

A novel hybridization capture method for direct whole genome sequencing of clinical specimens to inform Legionnaires' disease investigations

The unprecedented precision and resolution of whole genome sequencing (WGS) can provide definitive identification of infectious agents for epidemiological outbreak tracking. WGS approaches, however, are frequently impeded by low pathogen DNA recovery from available primary specimens or unculturable samples. A cost-effective hybrid capture assay for Legionella pneumophila WGS analysis directly on primary specimens was developed. DNA from a diverse range of sputum and autopsy specimens PCR-positive for L. pneumophila serogroup 1 (LPSG1) was enriched with this method, and WGS was performed. All tested specimens were determined to be enriched for Legionella reads (up to 209,000-fold), significantly improving the discriminatory power to compare relatedness when no clinical isolate was available. We found the WGS data from some enriched specimens to differ by less than five single-nucleotide polymorphisms (SNPs) when compared to the WGS data of a matched culture isolate. This testing and analysis retrospectively provided previously unconfirmed links to environmental sources for clinical specimens of sputum and autopsy lung tissue. The latter provided the additional information needed to identify the source of these culture-negative cases associated with the South Bronx 2015 Legionnaires' disease (LD) investigation in New York City. This new method provides a proof of concept for future direct clinical specimen hybrid capture enrichment combined with WGS and bioinformatic analysis during outbreak investigations.IMPORTANCELegionnaires' disease (LD) is a severe and potentially fatal type of pneumonia primarily caused by inhalation of Legionella-contaminated aerosols from man-made water or cooling systems. LD remains extremely underdiagnosed as it is an uncommon form of pneumonia and relies on clinicians including it in the differential and requesting specialized testing. Additionally, it is challenging to obtain clinical lower respiratory specimens from cases with LD, and when available, culture requires specialized media and growth conditions, which are not available in all microbiology laboratories. In the current study, a method for Legionella pneumophila using hybrid capture by RNA baiting was developed, which allowed us to generate sufficient genome resolution from L. pneumophila serogroup 1 PCR-positive clinical specimens. This new approach offers an additional tool for surveillance of future LD outbreaks where isolation of Legionella is not possible and may help solve previously unanswered questions from past LD investigations.

High performance Legionella pneumophila source attribution using genomics-based machine learning classification

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.

Legionellosis on the rise: A scoping review of sporadic, community-acquired incidence in the United States

Over the past two decades, the incidence of legionellosis has been steadily increasing in the United States though there is noclear explanation for the main factors driving the increase. While legionellosis is the leading cause of waterborne outbreaks in the US, most cases are sporadic and acquired in community settings where the environmental source is never identified. This scoping review aimed to summarise the drivers of infections in the USA and determine the magnitude of impact each potential driver may have. A total of 1,738 titles were screened, and 18 articles were identified that met the inclusion criteria. Strong evidence was found for precipitation as a major driver, and both temperature and relative humidity were found to be moderate drivers of incidence. Increased testing and improved diagnostic methods were classified as moderate drivers, and the ageing U.S. population was a minor driver of increasing incidence. Racial and socioeconomic inequities and water and housing infrastructure were found to be potential factors explaining the increasing incidence though they were largely understudied in the context of non-outbreak cases. Understanding the complex relationships between environmental, infrastructure, and population factors driving legionellosis incidence is important to optimise mitigation strategies and public policy.

Investigation of a legionellosis outbreak in Sydney CBD - a brief report

OBJECTIVE

To identify and control a source of Legionella in Sydney CBD.

METHODS

Clinical, epidemiological, environmental and genomic techniques were employed to identify cases and the source of Legionella.

RESULTS

Eleven legionellosis cases were linked to Sydney CBD with a median age of 69 years. All were hospitalised and had risk factors for Legionella infection. Eight of 11 cases identified as male. Genomic analysis linked three cases to a contaminated cooling water source in Sydney CBD, with a further case infected with a similar strain to that found in Sydney CBD. Another case, although epidemiologically linked to Sydney CBD, was infected with a genomically different strain to that found in Sydney CBD. Six other cases had no viable sample for genomic analysis.

CONCLUSION/IMPLICATIONS FOR PUBLIC HEALTH

An outbreak of legionellosis is a serious public health threat that requires rapid investigation and environmental control. We were able to identify a source in Sydney CBD through the application of clinical, epidemiological, environmental and genomic techniques. Genomic analysis is a powerful tool that can be used to confirm the source location but requires close collaboration between clinicians, public health units and microbiologists to recover viable sputum cultures from cases diagnosed with legionellosis.