X

Quality Statement

Pacific Biosciences is committed to providing high-quality products that meet customer expectations and comply with regulations. We will achieve these goals by adhering to and maintaining an effective quality-management system designed to ensure product quality, performance, and safety.

X

Image Use Agreement

By downloading, copying, or making any use of the images located on this website (“Site”) you acknowledge that you have read and understand, and agree to, the terms of this Image Usage Agreement, as well as the terms provided on the Legal Notices webpage, which together govern your use of the images as provided below. If you do not agree to such terms, do not download, copy or use the images in any way, unless you have written permission signed by an authorized Pacific Biosciences representative.

Subject to the terms of this Agreement and the terms provided on the Legal Notices webpage (to the extent they do not conflict with the terms of this Agreement), you may use the images on the Site solely for (a) editorial use by press and/or industry analysts, (b) in connection with a normal, peer-reviewed, scientific publication, book or presentation, or the like. You may not alter or modify any image, in whole or in part, for any reason. You may not use any image in a manner that misrepresents the associated Pacific Biosciences product, service or technology or any associated characteristics, data, or properties thereof. You also may not use any image in a manner that denotes some representation or warranty (express, implied or statutory) from Pacific Biosciences of the product, service or technology. The rights granted by this Agreement are personal to you and are not transferable by you to another party.

You, and not Pacific Biosciences, are responsible for your use of the images. You acknowledge and agree that any misuse of the images or breach of this Agreement will cause Pacific Biosciences irreparable harm. Pacific Biosciences is either an owner or licensee of the image, and not an agent for the owner. You agree to give Pacific Biosciences a credit line as follows: "Courtesy of Pacific Biosciences of California, Inc., Menlo Park, CA, USA" and also include any other credits or acknowledgments noted by Pacific Biosciences. You must include any copyright notice originally included with the images on all copies.

IMAGES ARE PROVIDED BY Pacific Biosciences ON AN "AS-IS" BASIS. Pacific Biosciences DISCLAIMS ALL REPRESENTATIONS AND WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, NON-INFRINGEMENT, OWNERSHIP, MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. IN NO EVENT SHALL Pacific Biosciences BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, PUNITIVE, OR CONSEQUENTIAL DAMAGES OF ANY KIND WHATSOEVER WITH RESPECT TO THE IMAGES.

You agree that Pacific Biosciences may terminate your access to and use of the images located on the PacificBiosciences.com website at any time and without prior notice, if it considers you to have violated any of the terms of this Image Use Agreement. You agree to indemnify, defend and hold harmless Pacific Biosciences, its officers, directors, employees, agents, licensors, suppliers and any third party information providers to the Site from and against all losses, expenses, damages and costs, including reasonable attorneys' fees, resulting from any violation by you of the terms of this Image Use Agreement or Pacific Biosciences' termination of your access to or use of the Site. Termination will not affect Pacific Biosciences' rights or your obligations which accrued before the termination.

I have read and understand, and agree to, the Image Usage Agreement.

I disagree and would like to return to the Pacific Biosciences home page.

Pacific Biosciences
Contact:

Authors: Buultjens, Andrew H and Chua, Kyra Y L and Baines, Sarah L and Kwong, Jason and Gao, Wei and Cutcher, Zoe and Adcock, Stuart and Ballard, Susan and Schultz, Mark B and Tomita, Takehiro and Subasinghe, Nela and Carter, Glen P and Pidot, Sacha J and Franklin, Lucinda and Seemann, Torsten and Gonçalves Da Silva, Anders and Howden, Benjamin P and Stinear, Timothy P

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 on L. 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, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK. Copyright © 2017 American Society for Microbiology.

Journal: Applied and environmental microbiology
DOI: 10.1128/AEM.01482-17
Year: 2017

Read Publication

 

Stay
Current

Visit our blog »