Please use this identifier to cite or link to this item: https://ahro.austin.org.au/austinjspui/handle/1/35065
Title: High performance Legionella pneumophila source attribution using genomics-based machine learning classification.
Austin Authors: Buultjens, Andrew H;Vandelannoote, Koen;Mercoulia, Karolina;Ballard, Susan;Sloggett, Clare;Howden, Benjamin P ;Seemann, Torsten;Stinear, Timothy P
Affiliation: Department of Microbiology and Immunology, Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia.;Center for Pathogen Genomics, University of Melbourne, Melbourne, Victoria, Australia.
Bacterial Phylogenomics Group, Institut Pasteur du Cambodge, Phnom Penh, Cambodia.
Infectious Diseases
Center for Pathogen Genomics, University of Melbourne, Melbourne, Victoria, Australia.;Department of Microbiology and Immunology, Microbiology Diagnostic Unit, Doherty Institute for Infection and Immunity, University of Melbourne, Melbourne, Victoria, Australia.
Issue Date: 30-Jan-2024
Date: 2024
Publication information: Applied and Environmental Microbiology 2024-01-30
Abstract: 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.
URI: https://ahro.austin.org.au/austinjspui/handle/1/35065
DOI: 10.1128/aem.01292-23
ORCID: 0000-0002-5984-1328
0000-0003-0150-123X
Journal: Applied and Environmental Microbiology
Start page: e0129223
PubMed URL: 38289130
ISSN: 1098-5336
Type: Journal Article
Subjects: Legionella pneumophila
Legionnaires' disease
bacterial genomics
machine learning
outbreak control
public health
source attribution
Appears in Collections:Journal articles

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