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

From Investigating a Case of Cellulitis to Exploring Nosocomial Infection Control of ST1 Legionella pneumophila Using Genomic Approaches

Legionella pneumophila can cause a large panel of symptoms besides the classic pneumonia presentation. Here we present a case of fatal nosocomial cellulitis in an immunocompromised patient followed, a year later, by a second case of Legionnaires' disease in the same ward. While the first case was easily assumed as nosocomial based on the date of symptom onset, the second case required clear typing results to be assigned either as nosocomial and related to the same environmental source as the first case, or community acquired. To untangle this specific question, we applied core-genome multilocus typing (MLST), whole-genome single nucleotide polymorphism and whole-genome MLST methods to a collection of 36 Belgian and 41 international sequence-type 1 (ST1) isolates using both thresholds recommended in the literature and tailored threshold based on local epidemiological data. Based on the thresholds applied to cluster isolates together, the three methods gave different results and no firm conclusion about the nosocomial setting of the second case could been drawn. Our data highlight that despite promising results in the study of outbreaks and for large-scale epidemiological investigations, next-generation sequencing typing methods applied to ST1 outbreak investigation still need standardization regarding both wet-lab protocols and bioinformatics. A deeper evaluation of the L. pneumophila evolutionary clock is also required to increase our understanding of genomic differences between isolates sampled during a clinical infection and in the environment.

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.

Long-read sequencing for reliably calling the mompS allele in Legionella pneumophila sequence-based typing

Sequence-based typing (SBT) of Legionella pneumophila is a valuable tool in epidemiological studies and outbreak investigations of Legionnaires' disease. In the L. pneumophila SBT scheme, mompS2 is one of seven genes that determine the sequence type (ST). The Legionella genome typically contains two copies of mompS (mompS1 and mompS2). When they are non-identical it can be challenging to determine the mompS2 allele, and subsequently the ST, from Illumina short-reads. In our collection of 233 L. pneumophila genomes, there were 62 STs, 18 of which carried non-identical mompS copies. Using short-reads, the mompS2 allele was misassembled or untypeable in several STs. Genomes belonging to ST154 and ST574, which carried mompS1 allele 7 and mompS2 allele 15, were assigned an incorrect mompS2 allele and/or mompS gene copy number when short-read assembled. For other isolates, mainly those carrying non-identical mompS copies, short-read assemblers occasionally failed to resolve the structure of the mompS-region, also resulting in untypeability from the short-read data. In this study, we wanted to understand the challenges we observed with calling the mompS2 allele from short-reads, assess if other short-read methods were able to resolve the mompS-region, and investigate the possibility of using long-reads to obtain the mompS alleles, and thereby perform L. pneumophila SBT from long-reads only. We found that the choice of short-read assembler had a major impact on resolving the mompS-region and thus SBT from short-reads, but no method consistently solved the mompS2 allele. By using Oxford Nanopore Technology (ONT) sequencing together with Trycycler and Medaka for long-read assembly and polishing we were able to resolve the mompS copies and correctly identify the mompS2 allele, in accordance with Sanger sequencing/EQA results for all tested isolates (n=35). The remaining six genes of the SBT profile could also be determined from the ONT-only reads. The STs called from ONT-only assemblies were also consistent with hybrid-assemblies of Illumina and ONT reads. We therefore propose ONT sequencing as an alternative method to perform L. pneumophila SBT to overcome the mompS challenge observed with short-reads. To facilitate this, we have developed ONTmompS (https://github.com/marithetland/ONTmompS), an in silico approach to determine L. pneumophila ST from long-read or hybrid assemblies.

CLEC12A Binds to Legionella pneumophila but Has No Impact on the Host's Antibacterial Response

Legionella pneumophila is an intracellular pathogen that can cause severe pneumonia after the inhalation of contaminated aerosols and replication in alveolar macrophages. Several pattern recognition receptors (PRRs) have been identified that contribute to the recognition of L. pneumophila by the innate immune system. However, the function of the C-type lectin receptors (CLRs), which are mainly expressed by macrophages and other myeloid cells, remains largely unexplored. Here, we used a library of CLR-Fc fusion proteins to search for CLRs that can bind the bacterium and identified the specific binding of CLEC12A to L. pneumophila. Subsequent infection experiments in human and murine macrophages, however, did not provide evidence for a substantial role of CLEC12A in controlling innate immune responses to the bacterium. Consistently, antibacterial and inflammatory responses to Legionella lung infection were not significantly influenced by CLEC12A deficiency. Collectively, CLEC12A is able to bind to L. pneumophila-derived ligands but does not appear to play a major role in the innate defense against L. pneumophila.

Genomic characterization and assessment of pathogenic potential of Legionella spp. isolates from environmental monitoring

Several species in the genus Legionella are known to cause an acute pneumonia when the aerosols containing the bacteria from man-made water systems are inhaled. The disease is usually caused by Legionella pneumophila, but other species have been implicated in the infection. The disease is frequently manifested as an outbreak, which means several people are affected when exposed to the common source of Legionella contamination. Therefor environmental surveillance which includes isolation and identification of Legionella is performed routinely. However, usually no molecular or genome-based methods are employed in further characterization of the isolates during routine environmental monitoring. During several years of such monitoring, isolates from different geographical locations were collected and 39 of them were sequenced by hybrid de novo approach utilizing short and long sequencing reads. In addition, the isolates were typed by standard culture and MALDI-TOF method. The sequencing reads were assembled and annotated to produce high-quality genomes. By employing discriminatory genome typing, four potential new species in the Legionella genus were identified, which are yet to be biochemically and morphologically characterized. Moreover, functional annotations concerning virulence and antimicrobial resistance were performed on the sequenced genomes. The study contributes to the knowledge on little-known non-pneumophila species present in man-made water systems and establishes support for future genetic relatedness studies as well as understanding of their pathogenic potential.

Legionella pneumophila: The Journey from the Environment to the Blood

An outbreak of a potentially fatal form of pneumonia in 1976 and in the annual convention of the American Legion was the first time that Legionella spp. was identified. Thereafter, the term Legionnaires’ disease (LD) was established. The infection in humans is transmitted by the inhalation of aerosols that contain the microorganisms that belong to the Legionellaceae family and the genus Legionella. The genus Legionella contains genetically heterogeneous species and serogroups. The Legionella pneumophila serogroup 1 (Lp1) is the most often detected strain in outbreaks of LD. The pathogenesis of LD infection initiates with the attachment of the bacterial cells to the host cells, and subsequent intracellular replication. Following invasion, Legionella spp. activates its virulence mechanisms: generation of specific compartments of Legionella-containing vacuole (LCV), and expression of genes that encode a type IV secretion system (T4SS) for the translocation of proteins. The ability of L. pneumophila to transmigrate across the lung’s epithelium barrier leads to bacteremia, spread, and invasion of many organs with subsequent manifestations, complications, and septic shock. The clinical manifestations of LD depend on the bacterial load in the aerosol, the virulence factors, and the immune status of the patient. The infection has two distinct forms: the non- pneumatic form or Pontiac fever, which is a milder febrile flu-like illness, and LD, a more severe form, which includes pneumonia. In addition, the extrapulmonary involvement of LD can include heart, brain, abdomen, and joints.