Legionella pneumophila in Municipal Shower Systems in Stavanger, Norway; A Longitudinal Surveillance Study Using Whole Genome Sequencing in Risk Management

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.