Genomic Epidemiology: Whole-Genome-Sequencing–Powered Surveillance and Outbreak Investigation of Foodborne Bacterial Pathogens

Abstract Yukon Territory (YT) is a remote region in northern Canada with ongoing spread of tuberculosis (TB). To explore the utility of whole genome sequencing (WGS) for TB surveillance and monitoring in a setting with detailed contact tracing and interview data, we used a mixed-methods approach. Our analysis included all culture-confirmed cases in YT (2005–2014) and incorporated data from 24-locus Mycobacterial Interspersed Repetitive Units-Variable Number of Tandem Repeats (MIRU-VNTR) genotyping, WGS and contact tracing. We compared field-based (contact investigation (CI) data + MIRU-VNTR) and genomic-based (WGS + MIRU-VNTR + basic case data) investigations to identify the most likely source of each person's TB and assessed the knowledge, attitudes and practices of programme personnel around genotyping and genomics using online, multiple-choice surveys (n = 4) and an in-person group interview (n = 5). Field- and genomics-based approaches agreed for 26 of 32 (81%) cases on likely location of TB acquisition. There was less agreement in the identification of specific source cases (13/22 or 59% of cases). Single-locus MIRU-VNTR variants and limited genetic diversity complicated the analysis. Qualitative data indicated that participants viewed genomic epidemiology as a useful tool to streamline investigations, particularly in differentiating latent TB reactivation from the recent transmission. Based on this, genomic data could be used to enhance CIs, focus resources, target interventions and aid in TB programme evaluation.

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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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Agrobacterium spp. nosocomial outbreak assessment using rapid MALDI-TOF MS based typing, confirmed by whole genome sequencing

Antimicrobial Resistance and Infection Control ◽

10.1186/s13756-019-0619-y ◽

2019 ◽

Vol 8 (1) ◽

Author(s):

Carlo Casanova ◽

Elia Lo Priore ◽

Adrian Egli ◽

Helena M. B. Seth-Smith ◽

Lorenz Räber ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Point Sources ◽

University Hospital ◽

Outbreak Investigation ◽

Whole Genome ◽

Maldi Tof Ms ◽

Maldi Tof ◽

Epidemiological Characteristics ◽

Tof Ms

Abstract Background A number of episodes of nosocomial Agrobacterium spp. bacteremia (two cases per year) were observed at Bern University Hospital, Switzerland, from 2015 to 2017. This triggered an outbreak investigation. Methods Cases of Agrobacterium spp. bacteremias that occurred between August 2011 and February 2017 were investigated employing line lists, environmental sampling, rapid protein- (MALDI-TOF MS), and genome-based typing (pulsed field gel electrophoresis and whole genome sequencing) of the clinical isolates. Results We describe a total of eight bacteremia episodes due to A. radiobacter (n = 2), Agrobacterium genomovar G3 (n = 5) and A. pusense (n = 1). Two tight clusters were observed by WGS typing, representing the two A. radiobacter isolates (cluster I, isolated in 2015) and four of the Agrobacterium genomovar G3 isolates (cluster II, isolated in 2016 and 2017), suggesting two different point sources. The epidemiological investigations revealed two computer tomography (CT) rooms as common patient locations, which correlated with the two outbreak clusters. MALDI-TOF MS permitted faster evaluation of strain relatedness than DNA-based methods. High resolution WGS-based typing confirmed the MALDI-TOF MS clustering. Conclusions We report clinical and epidemiological characteristics of two outbreak clusters with Agrobacterium. spp. bacteremia likely acquired during CT contrast medium injection and highlight the use of MALDI-TOF MS as a rapid tool to assess relatedness of rare gram-negative pathogens in an outbreak investigation.

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