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. Appl Environ Microbiol. 2017 Aug 18;: Authors: Buultjens AH, Chua KYL, Baines SL, Kwong J, Gao W, Cutcher Z, Adcock S, Ballard S, Schultz MB, Tomita T, Subasinghe N, Carter GP, Pidot SJ, Franklin L, Seemann T, Gonçalves Da Silva A, Howden BP, Stinear TP Abstract 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 to...
Source: Applied and Environmental Microbiology - Category: Microbiology Authors: Tags: Appl Environ Microbiol Source Type: research