Editorial: Integrating Whole Genome Sequencing Into Source Attribution and Risk Assessment of Foodborne Bacterial Pathogens

Melioidosis, a disease of public health importance in Southeast Asia and northern Australia, is caused by the Gram-negative soil bacillusBurkholderia pseudomallei. Melioidosis is typically acquired through environmental exposure, and case clusters are rare, even in regions where the disease is endemic.B. pseudomalleiis classed as a tier 1 select agent by the Centers for Disease Control and Prevention; from a biodefense perspective, source attribution is vital in an outbreak scenario to rule out a deliberate release. Two cases of melioidosis within a 3-month period at a residence in rural northern Australia prompted an investigation to determine the source of exposure.B. pseudomalleiisolates from the property's groundwater supply matched the multilocus sequence type of the clinical isolates. Whole-genome sequencing confirmed the water supply as the probable source of infection in both cases, with the clinical isolates differing from the likely infecting environmental strain by just one single nucleotide polymorphism (SNP) each. For the first time, we report a phylogenetic analysis of genomewide insertion/deletion (indel) data, an approach conventionally viewed as problematic due to high mutation rates and homoplasy. Our whole-genome indel analysis was concordant with the SNP phylogeny, and these two combined data sets provided greater resolution and a better fit with our epidemiological chronology of events. Collectively, this investigation represents a highly accurate account of source attribution in a melioidosis outbreak and gives further insight into a frequently overlooked reservoir ofB. pseudomallei. Our methods and findings have important implications for outbreak source tracing of this bacterium and other highly recombinogenic pathogens.

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Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges

Frontiers in Microbiology ◽

10.3389/fmicb.2019.01107 ◽

2019 ◽

Vol 10 ◽

Cited By ~ 20

Author(s):

Lucie Collineau ◽

Patrick Boerlin ◽

Carolee A. Carson ◽

Brennan Chapman ◽

Aamir Fazil ◽

...

Keyword(s):

Risk Assessment ◽

Antimicrobial Resistance ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Quantitative Risk Assessment ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

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Correction for Quainoo et al., “Whole-Genome Sequencing of Bacterial Pathogens: the Future of Nosocomial Outbreak Analysis”

Clinical Microbiology Reviews ◽

10.1128/cmr.00082-17 ◽

2017 ◽

Vol 31 (1) ◽

Cited By ~ 1

Author(s):

Scott Quainoo ◽

Jordy P. M. Coolen ◽

Sacha A. F. T. van Hijum ◽

Martijn A. Huynen ◽

Willem J. G. Melchers ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Bacterial Pathogens ◽

Whole Genome ◽

Nosocomial Outbreak ◽

The Future

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A step-by-step beginner's protocol for whole genome sequencing of human bacterial pathogens v1 (protocols.io.4u4gwyw)

protocols.io ◽

10.17504/protocols.io.4u4gwyw ◽

2019 ◽

Author(s):

Sanjay Gautam ◽

Rajendra KC ◽

Kelvin WC ◽

Miche l ◽

Ronan F

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Bacterial Pathogens ◽

Whole Genome

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Whole-Genome Sequencing for Routine Pathogen Surveillance in Public Health: a Population Snapshot of InvasiveStaphylococcus aureusin Europe

mBio ◽

10.1128/mbio.00444-16 ◽

2016 ◽

Vol 7 (3) ◽

Cited By ~ 123

Author(s):

David M. Aanensen ◽

Edward J. Feil ◽

Matthew T. G. Holden ◽

Janina Dordel ◽

Corin A. Yeats ◽

...

Keyword(s):

Public Health ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Large Scale ◽

Bacterial Pathogens ◽

Epidemiological Surveillance ◽

Data Sets ◽

Whole Genome ◽

Bioinformatic Tools ◽

Road Map

ABSTRACTThe implementation of routine whole-genome sequencing (WGS) promises to transform our ability to monitor the emergence and spread of bacterial pathogens. Here we combined WGS data from 308 invasiveStaphylococcus aureusisolates corresponding to a pan-European population snapshot, with epidemiological and resistance data. Geospatial visualization of the data is made possible by a generic software tool designed for public health purposes that is available at the project URL (http://www.microreact.org/project/EkUvg9uY?tt=rc). Our analysis demonstrates that high-risk clones can be identified on the basis of population level properties such as clonal relatedness, abundance, and spatial structuring and by inferring virulence and resistance properties on the basis of gene content. We also show thatin silicopredictions of antibiotic resistance profiles are at least as reliable as phenotypic testing. We argue that this work provides a comprehensive road map illustrating the three vital components for future molecular epidemiological surveillance: (i) large-scale structured surveys, (ii) WGS, and (iii) community-oriented database infrastructure and analysis tools.IMPORTANCEThe spread of antibiotic-resistant bacteria is a public health emergency of global concern, threatening medical intervention at every level of health care delivery. Several recent studies have demonstrated the promise of routine whole-genome sequencing (WGS) of bacterial pathogens for epidemiological surveillance, outbreak detection, and infection control. However, as this technology becomes more widely adopted, the key challenges of generating representative national and international data sets and the development of bioinformatic tools to manage and interpret the data become increasingly pertinent. This study provides a road map for the integration of WGS data into routine pathogen surveillance. We emphasize the importance of large-scale routine surveys to provide the population context for more targeted or localized investigation and the development of open-access bioinformatic tools to provide the means to combine and compare independently generated data with publicly available data sets.

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SplitStrains, a tool to identify and separate mixed Mycobacterium tuberculosis infections from WGS data

10.1101/2021.02.07.21250981 ◽

2021 ◽

Author(s):

Einar Gabbasov ◽

Miguel Moreno-Molina ◽

Iñaki Comas ◽

Maxwell Libbrecht ◽

Leonid Chindelevitch

Keyword(s):

Public Health ◽

Mycobacterium Tuberculosis ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Bacterial Pathogens ◽

Superior Performance ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Multiple Strains

AbstractThe occurrence of multiple strains of a bacterial pathogen such as M. tuberculosis or C. difficile within a single human host, referred to as a mixed infection, has important implications for both healthcare and public health. However, methods for detecting it, and especially determining the proportion and identities of the underlying strains, from WGS (whole-genome sequencing) data, have been limited.In this paper we introduce SplitStrains, a novel method for addressing these challenges. Grounded in a rigorous statistical model, SplitStrains not only demonstrates superior performance in proportion estimation to other existing methods on both simulated as well as real M. tuberculosis data, but also successfully determines the identity of the underlying strains.We conclude that SplitStrains is a powerful addition to the existing toolkit of analytical methods for data coming from bacterial pathogens, and holds the promise of enabling previously inaccessible conclusions to be drawn in the realm of public health microbiology.Author summaryWhen multiple strains of a pathogenic organism are present in a patient, it may be necessary to not only detect this, but also to identify the individual strains. However, this problem has not yet been solved for bacterial pathogens processed via whole-genome sequencing. In this paper, we propose the SplitStrains algorithm for detecting multiple strains in a sample, identifying their proportions, and inferring their sequences, in the case of Mycobacterium tuberculosis. We test it on both simulated and real data, with encouraging results. We believe that our work opens new horizons in public health microbiology by allowing a more precise detection, identification and quantification of multiple infecting strains within a sample.

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