The forensic analysis of foodborne bacterial pathogens in the age of whole-genome sequencing

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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A step-by-step beginner’s protocol for whole genome sequencing of human bacterial pathogens

Journal of Biological Methods ◽

10.14440/jbm.2019.276 ◽

2019 ◽

Vol 6 (1) ◽

pp. 110 ◽

Cited By ~ 3

Author(s):

Sanjay S. Gautam ◽

Rajendra KC ◽

Kelvin WC Leong ◽

Micheál Mac Aogáin ◽

Ronan F. O'Toole

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Bacterial Pathogens ◽

Whole Genome

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

Frontiers in Microbiology ◽

10.3389/fmicb.2021.795098 ◽

2021 ◽

Vol 12 ◽

Author(s):

Frederique Pasquali ◽

Daniel Remondini ◽

Emma Louise Snary ◽

Tine Hald ◽

Laurent Guillier

Keyword(s):

Risk Assessment ◽

Whole Genome Sequencing ◽

Genome Sequencing ◽

Bacterial Pathogens ◽

Whole Genome ◽

Source Attribution

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Hybridization capture and low-coverage SNP profiling for extended kinship analysis and forensic identification of historical remains

10.1101/2020.09.17.300715 ◽

2020 ◽

Author(s):

Erin M. Gorden ◽

Ellen M. Greytak ◽

Kimberly Sturk-Andreaggi ◽

Janet Cady ◽

Timothy P. McMahon ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Forensic Analysis ◽

Forensic Identification ◽

Degraded Dna ◽

Whole Genome ◽

Kinship Analysis ◽

Hybridization Capture ◽

Low Coverage ◽

Historical Remains

AbstractDNA-assisted identification of historical remains requires the genetic analysis of highly degraded DNA, along with a comparison to DNA from known relatives. This can be achieved by targeting single nucleotide polymorphisms (SNPs) using a hybridization capture and next-generation sequencing approach suitable for degraded skeletal samples. In the present study, two SNP capture panels were designed to target ∼25,000 (25K) and ∼95,000 (95K) autosomal SNPs, respectively, to enable distant kinship estimation (up to 4th degree relatives). Low-coverage SNP data were successfully recovered from 14 skeletal elements 75 years postmortem, with captured DNA having mean insert sizes ranging from 32-170 bp across the 14 samples. SNP comparison with DNA from known family references was performed in the Parabon Fχ Forensic Analysis Platform, which utilizes a likelihood approach for kinship prediction that was optimized for low-coverage sequencing data with DNA damage. The 25K and 95K panels produced 15,000 and 42,000 SNPs on average, respectively allowing for accurate kinship prediction in 17 and 19 of the 21 pairwise comparisons. Whole genome sequencing was not able to produce sufficient SNP data for accurate kinship prediction, demonstrating that hybridization capture is necessary for historical samples. This study provides the groundwork for the expansion of research involving compromised samples to include SNP hybridization capture.Author SummaryOur study evaluates ancient DNA techniques involving SNP capture and Next-Generation Sequencing for use in forensic identification. We utilized bone samples from 14 sets of previously identified historical remains aged 70 years postmortem for low-coverage SNP genotyping and extended kinship analysis. We performed whole genome sequencing and hybridization capture with two SNP panels, one targeting ∼25,000 SNPs and the other targeting ∼95,000 SNPs, to assess SNP recovery and accuracy in kinship estimation. A genotype likelihood approach was utilized for SNP profiling of degraded DNA characterized by cytosine deamination typical of ancient and historical specimens. Family reference samples from known relatives up to 4th degree were genotyped using a SNP microarray. We then utilized the Parabon Fχ Forensic Analysis Platform to perform pairwise comparisons of all bone and reference samples for kinship prediction. The results showed that both capture panels facilitated accurate kinship prediction in more than 80% of the tested relationships without producing false positive matches (or adventitious hits), which were commonly observed in the whole genome sequencing comparisons. We demonstrate that SNP capture can be an effective method for genotyping of historical remains for distant kinship analysis with known relatives, which will support humanitarian efforts and forensic identification.

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

protocols.io ◽

10.17504/protocols.io.4xxgxpn ◽

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 Analysis of Quorum Quenching Bacterial Strain Acinetobacter lactucae QL-1 Identifies the FadY Enzyme for Degradation of the Diffusible Signal Factor

International Journal of Molecular Sciences ◽

10.3390/ijms21186729 ◽

2020 ◽

Vol 21 (18) ◽

pp. 6729 ◽

Cited By ~ 1

Author(s):

Tian Ye ◽

Tian Zhou ◽

Xudan Xu ◽

Wenping Zhang ◽

Xinghui Fan ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Xanthomonas Campestris ◽

Molecular Mechanisms ◽

Bacterial Pathogens ◽

Quorum Quenching ◽

Site Directed Mutagenesis ◽

Whole Genome ◽

Sequencing Analysis ◽

Diffusible Signal Factor

The diffusible signal factor (DSF) is a fatty acid signal molecule and is widely conserved in various Gram-negative bacteria. DSF is involved in the regulation of pathogenic virulence in many bacterial pathogens, including Xanthomonas campestris pv. campestris (Xcc). Quorum quenching (QQ) is a potential approach for preventing and controlling DSF-mediated bacterial infections by the degradation of the DSF signal. Acinetobacter lactucae strain QL-1 possesses a superb DSF degradation ability and effectively attenuates Xcc virulence through QQ. However, the QQ mechanisms in strain QL-1 are still unknown. In the present study, whole-genome sequencing and comparative genomics analysis were conducted to identify the molecular mechanisms of QQ in strain QL-1. We found that the fadY gene of QL-1 is an ortholog of XccrpfB, a known DSF degradation gene, suggesting that strain QL-1 is capable of inactivating DSF by QQ enzymes. The results of site-directed mutagenesis indicated that fadY is required for strain QL-1 to degrade DSF. The determination of FadY activity in vitro revealed that the fatty acyl-CoA synthetase FadY had remarkable catalytic activity. Furthermore, the expression of fadY in transformed Xcc strain XC1 was investigated and shown to significantly attenuate bacterial pathogenicity on host plants, such as Chinese cabbage and radish. This is the first report demonstrating a DSF degradation enzyme from A. lactucae. Taken together, these findings shed light on the QQ mechanisms of A. lactucae strain QL-1, and provide useful enzymes and related genes for the biocontrol of infectious diseases caused by DSF-dependent bacterial pathogens.

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