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A Schema for Digitized Surface Swab Site Metadata in Open-Source DNA Sequence Databases. mSystems 2023; 8:e0128422. [PMID: 36847566 PMCID: PMC10134794 DOI: 10.1128/msystems.01284-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
Large, open-source DNA sequence databases have been generated, in part, through the collection of microbial pathogens by swabbing surfaces in built environments. Analyzing these data in aggregate through public health surveillance requires digitization of the complex, domain-specific metadata that are associated with the swab site locations. However, the swab site location information is currently collected in a single, free-text, "isolation source", field-promoting generation of poorly detailed descriptions with various word order, granularity, and linguistic errors, making automation difficult and reducing machine-actionability. We assessed 1,498 free-text swab site descriptions that were generated during routine foodborne pathogen surveillance. The lexicon of free-text metadata was evaluated to determine the informational facets and the quantity of unique terms used by data collectors. Open Biological Ontologies (OBO) Foundry libraries were used to develop hierarchical vocabularies that are connected with logical relationships to describe swab site locations. 5 informational facets that were described by 338 unique terms were identified via content analysis. Term hierarchy facets were developed, as were statements (called axioms) about how the entities within these five domains are related. The schema developed through this study has been integrated into a publicly available pathogen metadata standard, facilitating ongoing surveillance and investigations. The One Health Enteric Package was available at NCBI BioSample, beginning in 2022. The collective use of metadata standards increases the interoperability of DNA sequence databases and enables large-scale approaches to data sharing and artificial intelligence as well as big-data solutions to food safety. IMPORTANCE The regular analysis of whole-genome sequence data in collections such as NCBI's Pathogen Detection Database is used by many public health organizations to detect outbreaks of infectious disease. However, isolate metadata in these databases are often incomplete and of poor quality. These complex, raw metadata must often be reorganized and manually formatted for use in aggregate analyses. These processes are inefficient and time-consuming, increasing the interpretative labor needed by public health groups to extract actionable information. The future use of open genomic epidemiology networks will be supported through the development of an internationally applicable vocabulary system with which swab site locations can be described.
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Cernava T, Rybakova D, Buscot F, Clavel T, McHardy AC, Meyer F, Meyer F, Overmann J, Stecher B, Sessitsch A, Schloter M, Berg G. Metadata harmonization-Standards are the key for a better usage of omics data for integrative microbiome analysis. ENVIRONMENTAL MICROBIOME 2022; 17:33. [PMID: 35751093 PMCID: PMC9233336 DOI: 10.1186/s40793-022-00425-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 05/29/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND Tremendous amounts of data generated from microbiome research studies during the last decades require not only standards for sampling and preparation of omics data but also clear concepts of how the metadata is prepared to ensure re-use for integrative and interdisciplinary microbiome analysis. RESULTS In this Commentary, we present our views on the key issues related to the current system for metadata submission in omics research, and propose the development of a global metadata system. Such a system should be easy to use, clearly structured in a hierarchical way, and should be compatible with all existing microbiome data repositories, following common standards for minimal required information and common ontology. Although minimum metadata requirements are essential for microbiome datasets, the immense technological progress requires a flexible system, which will have to be constantly improved and re-thought. While FAIR principles (Findable, Accessible, Interoperable, and Reusable) are already considered, international legal issues on genetic resource and sequence sharing provided by the Convention on Biological Diversity need more awareness and engagement of the scientific community. CONCLUSIONS The suggested approach for metadata entries would strongly improve retrieving and re-using data as demonstrated in several representative use cases. These integrative analyses, in turn, would further advance the potential of microbiome research for novel scientific discoveries and the development of microbiome-derived products.
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Affiliation(s)
- Tomislav Cernava
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | - Daria Rybakova
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
| | - François Buscot
- 2Soil Ecology Department, Helmholtz Centre for Environmental Research (UFZ), Halle (Saale), Germany
- 3German Centre for Integrative Biodiversity Research (iDiv) Halle–Jena–Leipzig, Leipzig, Germany
| | - Thomas Clavel
- Functional Microbiome Research Group, Institute of Medical Microbiology, RWTH University Hospital, Aachen, Germany
| | - Alice Carolyn McHardy
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
- German Center for Infection Research (DZIF), Hannover-Braunschweig site, Hannover, Germany
- Cluster of Excellence RESIST (EXC2155), Hannover Medical School, Hannover, Germany
| | - Fernando Meyer
- Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), Technische Universität Braunschweig, Braunschweig, Germany
| | | | - Jörg Overmann
- Leibniz Institute DSMZ German Collection of Microorganisms and Cell Cultures, Braunschweig, Germany
- Technical University of Braunschweig, Braunschweig, Germany
| | - Bärbel Stecher
- Max Von Pettenkofer Institute of Hygiene and Medical Microbiology, Faculty of Medicine, LMU Munich, Munich, Germany
- German Center for Infection Research (DZIF), Munich, Germany
| | - Angela Sessitsch
- Bioresources Unit, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Gabriele Berg
- Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria
- Leibniz-Institute for Agricultural Engineering Potsdam (ATB), Potsdam, Germany
- University of Potsdam, Potsdam, Germany
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Marimuthu M, Arumugam SS, Jiao T, Sabarinathan D, Li H, Chen Q. Metal organic framework based sensors for the detection of food contaminants. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116642] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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4
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A Comprehensive Review on the Use of Metal–Organic Frameworks (MOFs) Coupled with Enzymes as Biosensors. ELECTROCHEM 2022. [DOI: 10.3390/electrochem3010006] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Several studies have shown the development of electrochemical biosensors based on enzymes immobilized in metal–organic frameworks (MOFs). Although enzymes have unique properties, such as efficiency, selectivity, and environmental sustainability, when immobilized, these properties are improved, presenting significant potential for several biotechnological applications. Using MOFs as matrices for enzyme immobilization has been considered a promising strategy due to their many advantages compared to other supporting materials, such as larger surface areas, higher porosity rates, and better stability. Biosensors are analytical tools that use a bioactive element and a transducer for the detection/quantification of biochemical substances in the most varied applications and areas, in particular, food, agriculture, pharmaceutical, and medical. This review will present novel insights on the construction of biosensors with materials based on MOFs. Herein, we have been highlighted the use of MOF for biosensing for biomedical, food safety, and environmental monitoring areas. Additionally, different methods by which immobilizations are performed in MOFs and their main advantages and disadvantages are presented.
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Aslam B, Khurshid M, Arshad MI, Muzammil S, Rasool M, Yasmeen N, Shah T, Chaudhry TH, Rasool MH, Shahid A, Xueshan X, Baloch Z. Antibiotic Resistance: One Health One World Outlook. Front Cell Infect Microbiol 2021; 11:771510. [PMID: 34900756 PMCID: PMC8656695 DOI: 10.3389/fcimb.2021.771510] [Citation(s) in RCA: 152] [Impact Index Per Article: 50.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 10/29/2021] [Indexed: 01/07/2023] Open
Abstract
Antibiotic resistance (ABR) is a growing public health concern worldwide, and it is now regarded as a critical One Health issue. One Health’s interconnected domains contribute to the emergence, evolution, and spread of antibiotic-resistant microorganisms on a local and global scale, which is a significant risk factor for global health. The persistence and spread of resistant microbial species, and the association of determinants at the human-animal-environment interface can alter microbial genomes, resulting in resistant superbugs in various niches. ABR is motivated by a well-established link between three domains: human, animal, and environmental health. As a result, addressing ABR through the One Health approach makes sense. Several countries have implemented national action plans based on the One Health approach to combat antibiotic-resistant microbes, following the Tripartite’s Commitment Food and Agriculture Organization (FAO)-World Organization for Animal Health (OIE)-World Health Organization (WHO) guidelines. The ABR has been identified as a global health concern, and efforts are being made to mitigate this global health threat. To summarize, global interdisciplinary and unified approaches based on One Health principles are required to limit the ABR dissemination cycle, raise awareness and education about antibiotic use, and promote policy, advocacy, and antimicrobial stewardship.
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Affiliation(s)
- Bilal Aslam
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Mohsin Khurshid
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | | | - Saima Muzammil
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Maria Rasool
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan
| | - Nafeesa Yasmeen
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Taif Shah
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
| | - Tamoor Hamid Chaudhry
- Department of Microbiology, Government College University Faisalabad, Faisalabad, Pakistan.,Public Health Laboratories Division, National Institute of Health, Islamabad, Pakistan
| | | | - Aqsa Shahid
- Faculty of Rehabilitation and Allied Health Sciences, Riphah International University, Faisalabad, Pakistan
| | - Xia Xueshan
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
| | - Zulqarnain Baloch
- Faculty of Life Science and Technology, Kunming University of Life Science and Technology, Kunming, China
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Pires J, Huisman JS, Bonhoeffer S, Van Boeckel TP. Increase in antimicrobial resistance in Escherichia coli in food animals between 1980 and 2018 assessed using genomes from public databases. J Antimicrob Chemother 2021; 77:646-655. [PMID: 34894245 DOI: 10.1093/jac/dkab451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Accepted: 11/09/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Next-generation sequencing has considerably increased the number of genomes available in the public domain. However, efforts to use these genomes for surveillance of antimicrobial resistance have thus far been limited and geographically heterogeneous. We inferred global resistance trends in Escherichia coli in food animals using genomes from public databases. METHODS We retrieved 7632 E. coli genomes from public databases (NCBI, PATRIC and EnteroBase) and screened for antimicrobial resistance genes (ARGs) using ResFinder. Selection bias towards resistance, virulence or specific strains was accounted for by screening BioProject descriptions. Temporal trends for MDR, resistance to antimicrobial classes and ARG prevalence were inferred using generalized linear models for all genomes, including those not subjected to selection bias. RESULTS MDR increased by 1.6 times between 1980 and 2018, as genomes carried, on average, ARGs conferring resistance to 2.65 antimicrobials in swine, 2.22 in poultry and 1.58 in bovines. Highest resistance levels were observed for tetracyclines (42.2%-69.1%), penicillins (19.4%-47.5%) and streptomycin (28.6%-56.6%). Resistance trends were consistent after accounting for selection bias, although lower mean absolute resistance estimates were associated with genomes not subjected to selection bias (difference of 3.16%±3.58% across years, hosts and antimicrobial classes). We observed an increase in extended-spectrum cephalosporin ARG blaCMY-2 and a progressive substitution of tetB by tetA. Estimates of resistance prevalence inferred from genomes in the public domain were in good agreement with reports from systematic phenotypic surveillance. CONCLUSIONS Our analysis illustrates the potential of using the growing volume of genomes in public databases to track AMR trends globally.
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Affiliation(s)
- João Pires
- Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland
| | - Jana S Huisman
- Institute of Integrative Biology, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Thomas P Van Boeckel
- Institute for Environmental Decisions, ETH Zurich, Zurich, Switzerland.,Center for Disease Dynamics, Economics & Policy, New Delhi, India
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Multidrug Resistance Dynamics in Salmonella in Food Animals in the United States: An Analysis of Genomes from Public Databases. Microbiol Spectr 2021; 9:e0049521. [PMID: 34704804 PMCID: PMC8549754 DOI: 10.1128/spectrum.00495-21] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
The number of bacterial genomes deposited each year in public databases is growing exponentially. However, efforts to use these genomes to track trends in antimicrobial resistance (AMR) have been limited thus far. We used 22,102 genomes from public databases to track AMR trends in nontyphoidal Salmonella in food animals in the United States. In 2018, genomes deposited in public databases carried genes conferring resistance, on average, to 2.08 antimicrobial classes in poultry, 1.74 in bovines, and 1.28 in swine. This represents a decline in AMR of over 70% compared to the levels in 2000 in bovines and swine, and an increase of 13% for poultry. Trends in resistance inferred from genomic data showed good agreement with U.S. phenotypic surveillance data (weighted mean absolute difference ± standard deviation, 5.86% ± 8.11%). In 2018, resistance to 3rd-generation cephalosporins in bovines, swine, and poultry decreased to 9.97% on average, whereas in quinolones and 4th-generation cephalosporins, resistance increased to 12.53% and 3.87%, respectively. This was concomitant with a decrease of blaCMY-2 but an increase in blaCTX-M-65 and gyrA D87Y (encoding a change of D to Y at position 87). Core genome single-nucleotide polymorphism (SNP) phylogenies show that resistance to these antimicrobial classes was predominantly associated with Salmonella enterica serovar Infantis and, to a lesser extent, S. enterica serovar Typhimurium and its monophasic variant I 4,[5],12:i:−, whereas quinolone resistance was also associated with S. enterica serovar Dublin. Between 2000 and 2018, trends in serovar prevalence showed a composition shift where S. Typhimurium decreased while S. Infantis increased. Our findings illustrate the growing potential of using genomes in public databases to track AMR in regions where sequencing capacities are currently expanding. IMPORTANCE Next-generation sequencing has led to an exponential increase in the number of genomes deposited in public repositories. This growing volume of information presents opportunities to track the prevalence of genes conferring antimicrobial resistance (AMR), a growing threat to the health of humans and animals. Using 22,102 public genomes, we estimated that the prevalence of multidrug resistance (MDR) in the United States decreased in nontyphoidal Salmonella isolates recovered from bovines and swine between 2000 and 2018, whereas it increased in poultry. These trends are consistent with those detected by national surveillance systems that monitor resistance using phenotypic testing. However, using genomes, we identified that genes conferring resistance to critically important antimicrobials were associated with specific MDR serovars that could be the focus for future interventions. Our analysis illustrates the growing potential of public repositories to monitor AMR trends and shows that similar efforts could soon be carried out in other regions where genomic surveillance is increasing.
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8
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Pettengill JB, Beal J, Balkey M, Allard M, Rand H, Timme R. Interpretative labor and the bane of non-standardized metadata in public health surveillance and food safety. Clin Infect Dis 2021; 73:1537-1539. [PMID: 34240118 DOI: 10.1093/cid/ciab615] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Indexed: 11/14/2022] Open
Abstract
Open source DNA sequence databases have long been touted as beneficial to public health, including the facilitation of earlier detection and response to infectious disease outbreaks. Of critical importance to harnessing these benefits is the metadata which describes general and other domain specific attributes (e.g., collection location, isolate type, etc.) of a sample. Unlike the sequence data, the metadata is often incomplete and lacks adherence to an international standard. We describe the problem posed by such variable and incomplete metadata in terms of interpretative labor costs (the time and energy necessary to make sense of the signal in the genetic data), and the impact such metadata has on foodborne outbreak detection and response. Improving the quality of sequence-associated metadata would allow for earlier detection of emerging food safety hazards and allow faster response to foodborne outbreaks.
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Affiliation(s)
- James B Pettengill
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration
| | - Jennifer Beal
- Signals Team, Coordinated Outbreak Response and Evaluation Network, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration
| | - Maria Balkey
- Division of Microbiology, Office of Regulatory Science, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration
| | - Marc Allard
- Division of Microbiology, Office of Regulatory Science, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration
| | - Hugh Rand
- Biostatistics and Bioinformatics Staff, Office of Analytics and Outreach, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration
| | - Ruth Timme
- Division of Microbiology, Office of Regulatory Science, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration
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9
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Xue Y, Peng Y, Geng Z, Wang Y, Ung COL, Hu H. Metal–Organic Frameworks (MOFs) Based Analytical Techniques for Food Safety Evaluation. EFOOD 2021. [DOI: 10.2991/efood.k.210209.001] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
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11
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Wee BA, Muloi DM, van Bunnik BAD. Quantifying the transmission of antimicrobial resistance at the human and livestock interface with genomics. Clin Microbiol Infect 2020; 26:1612-1616. [PMID: 32979568 PMCID: PMC7721588 DOI: 10.1016/j.cmi.2020.09.019] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 09/05/2020] [Accepted: 09/11/2020] [Indexed: 12/27/2022]
Abstract
BACKGROUND Livestock have been implicated as a reservoir for antimicrobial resistance (AMR) that can spread to humans. Close proximity and ecological interfaces involving livestock have been posited as risk factors for the transmission of AMR. In spite of this, there are sparse data and limited agreement on the transmission dynamics that occur. OBJECTIVES To identify how genome sequencing approaches can be used to quantify the dynamics of AMR transmission at the human-livestock interface, and where current knowledge can be improved to better understand the impact of transmission on the spread of AMR. SOURCES Key articles investigating various aspects of AMR transmission at the human-livestock interface are discussed, with a focus on Escherichia coli. CONTENT We recapitulate the current understanding of the transmission of AMR between humans and livestock based on current genomic and epidemiological approaches. We discuss how the use of well-designed, high-resolution genome sequencing studies can improve our understanding of the human-livestock interface. IMPLICATIONS A better understanding of the human-livestock interface will aid in the development of evidence-based and effective One Health interventions that can ultimately reduce the burden of AMR in humans.
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Affiliation(s)
- Bryan A Wee
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom.
| | - Dishon M Muloi
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Centre for Immunity, Infection & Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom; International Livestock Research Institute, Nairobi, Kenya
| | - Bram A D van Bunnik
- Usher Institute, University of Edinburgh, Edinburgh, United Kingdom; Centre for Immunity, Infection & Evolution, School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom
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Schriml LM, Mitraka E, Munro J, Tauber B, Schor M, Nickle L, Felix V, Jeng L, Bearer C, Lichenstein R, Bisordi K, Campion N, Hyman B, Kurland D, Oates CP, Kibbey S, Sreekumar P, Le C, Giglio M, Greene C. Human Disease Ontology 2018 update: classification, content and workflow expansion. Nucleic Acids Res 2020; 47:D955-D962. [PMID: 30407550 PMCID: PMC6323977 DOI: 10.1093/nar/gky1032] [Citation(s) in RCA: 234] [Impact Index Per Article: 58.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Accepted: 10/22/2018] [Indexed: 12/22/2022] Open
Abstract
The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.
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Affiliation(s)
- Lynn M Schriml
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | | | - James Munro
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Becky Tauber
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Mike Schor
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Lance Nickle
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Victor Felix
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Linda Jeng
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Cynthia Bearer
- University of Maryland School of Medicine, Baltimore, MD, USA
| | | | | | - Nicole Campion
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brooke Hyman
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - David Kurland
- New York University Langone Medical Center, Department of Neurosurgery, New York, NY, USA
| | - Connor Patrick Oates
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Siobhan Kibbey
- University of Maryland School of Medicine, Baltimore, MD, USA
| | | | - Chris Le
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Michelle Giglio
- University of Maryland School of Medicine, Institute for Genome Sciences, Baltimore, MD, USA
| | - Carol Greene
- University of Maryland School of Medicine, Baltimore, MD, USA
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13
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Crisan A, Gardy JL, Munzner T. A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT. Bioinformatics 2020; 35:1668-1676. [PMID: 30256887 PMCID: PMC6513170 DOI: 10.1093/bioinformatics/bty832] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Revised: 08/27/2018] [Accepted: 09/24/2018] [Indexed: 11/28/2022] Open
Abstract
Motivation Data visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options. Results We have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools. Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas. Availability and implementation Our browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Anamaria Crisan
- Department of Computer Science, University of British Columbia, Vancouver, Canada
| | - Jennifer L Gardy
- School of Population and Public Health, University of British Columbia, Vancouver, Canada.,British Columbia Centre for Disease Control, Vancouver, Canada
| | - Tamara Munzner
- Department of Computer Science, University of British Columbia, Vancouver, Canada
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Performance and Accuracy of Four Open-Source Tools for In Silico Serotyping of Salmonella spp. Based on Whole-Genome Short-Read Sequencing Data. Appl Environ Microbiol 2020; 86:AEM.02265-19. [PMID: 31862714 PMCID: PMC7028957 DOI: 10.1128/aem.02265-19] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2019] [Accepted: 12/16/2019] [Indexed: 12/19/2022] Open
Abstract
We compared the performance of four open-source in silico Salmonella typing tools (SeqSero, SeqSero2, Salmonella In Silico Typing Resource [SISTR], and Metric Oriented Sequence Typer [MOST]) to assess their potential for replacing laboratory serological testing with serovar predictions from whole-genome sequencing data. We conducted a retrospective analysis of 1,624 Salmonella isolates of 72 serovars submitted to the German National Salmonella Reference Laboratory between 1999 and 2019. All isolates are derived from animal and foodstuff origins. We conducted Illumina short-read sequencing and compared the in silico serovar prediction results with the results of routine laboratory serotyping. We found the best-performing in silico serovar prediction tool to be SISTR, with 94% correctly typed isolates, followed by SeqSero2 (87%), SeqSero (81%), and MOST (79%). Furthermore, we found that mapping-based tools like SeqSero and SeqSero2 (allele mode) were more reliable for the prediction of monophasic variants, while sequence type and cluster-based methods like MOST and SISTR (core-genome multilocus sequence type [cgMLST]), showed greater resilience when confronted with GC-biased sequencing data. We showed that the choice of library preparation kit could substantially affect O antigen detection, due to the low GC content of the wzx and wzy genes. Although the accuracy of computational serovar predictions is still not quite on par with traditional serotyping by Salmonella reference laboratories, the command-line tools investigated in this study perform a rapid, efficient, inexpensive, and reproducible analysis, which can be integrated into in-house characterization pipelines. Based on our results, we find SISTR most suitable for automated, routine serotyping for public health surveillance of Salmonella IMPORTANCE Salmonella spp. are important foodborne pathogens. To reduce the number of infected patients, it is essential to understand which subtypes of the bacteria cause disease outbreaks. Traditionally, characterization of Salmonella requires serological testing, a laboratory method by which Salmonella isolates can be classified into over 2,600 distinct subtypes, called serovars. Due to recent advances in whole-genome sequencing, many tools have been developed to replace traditional testing methods with computational analysis of genome sequences. It is crucial to validate that these tools, many already in use for routine surveillance, deliver accurate and reliable serovar information. In this study, we set out to compare which of the currently available open-source command-line tools is most suitable to replace serological testing. A thorough evaluation of the differing computational approaches is highly important to ensure the backward compatibility of serotyping data and to maintain comparability between laboratories.
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Koutsoumanis K, Allende A, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Hilbert F, Lindqvist R, Nauta M, Peixe L, Ru G, Simmons M, Skandamis P, Suffredini E, Jenkins C, Malorny B, Ribeiro Duarte AS, Torpdahl M, da Silva Felício MT, Guerra B, Rossi M, Herman L. Whole genome sequencing and metagenomics for outbreak investigation, source attribution and risk assessment of food-borne microorganisms. EFSA J 2019; 17:e05898. [PMID: 32626197 PMCID: PMC7008917 DOI: 10.2903/j.efsa.2019.5898] [Citation(s) in RCA: 64] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
This Opinion considers the application of whole genome sequencing (WGS) and metagenomics for outbreak investigation, source attribution and risk assessment of food‐borne pathogens. WGS offers the highest level of bacterial strain discrimination for food‐borne outbreak investigation and source‐attribution as well as potential for more precise hazard identification, thereby facilitating more targeted risk assessment and risk management. WGS improves linking of sporadic cases associated with different food products and geographical regions to a point source outbreak and can facilitate epidemiological investigations, allowing also the use of previously sequenced genomes. Source attribution may be favoured by improved identification of transmission pathways, through the integration of spatial‐temporal factors and the detection of multidirectional transmission and pathogen–host interactions. Metagenomics has potential, especially in relation to the detection and characterisation of non‐culturable, difficult‐to‐culture or slow‐growing microorganisms, for tracking of hazard‐related genetic determinants and the dynamic evaluation of the composition and functionality of complex microbial communities. A SWOT analysis is provided on the use of WGS and metagenomics for Salmonella and Shigatoxin‐producing Escherichia coli (STEC) serotyping and the identification of antimicrobial resistance determinants in bacteria. Close agreement between phenotypic and WGS‐based genotyping data has been observed. WGS provides additional information on the nature and localisation of antimicrobial resistance determinants and on their dissemination potential by horizontal gene transfer, as well as on genes relating to virulence and biological fitness. Interoperable data will play a major role in the future use of WGS and metagenomic data. Capacity building based on harmonised, quality controlled operational systems within European laboratories and worldwide is essential for the investigation of cross‐border outbreaks and for the development of international standardised risk assessments of food‐borne microorganisms.
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16
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Zhang Y, Wang H. Building an information infrastructure of spectroscopic profiling data for food-drug quality and safety management. ENTERP INF SYST-UK 2019. [DOI: 10.1080/17517575.2019.1684567] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Yinsheng Zhang
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China
- School of Information Sciences, University of Illinois at Urbana Champaign, Champaign, IL, USA
| | - Haiyan Wang
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China
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17
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Wang PL, Xie LH, Joseph EA, Li JR, Su XO, Zhou HC. Metal-Organic Frameworks for Food Safety. Chem Rev 2019; 119:10638-10690. [PMID: 31361477 DOI: 10.1021/acs.chemrev.9b00257] [Citation(s) in RCA: 270] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Food safety is a prevalent concern around the world. As such, detection, removal, and control of risks and hazardous substances present from harvest to consumption will always be necessary. Metal-organic frameworks (MOFs), a class of functional materials, possess unique physical and chemical properties, demonstrating promise in food safety applications. In this review, the synthesis and porosity of MOFs are first introduced by some representative examples that pertain to the field of food safety. Following that, the application of MOFs and MOF-based materials in food safety monitoring, food processing, covering preservation, sanitation, and packaging is overviewed. Future perspectives, as well as potential opportunities and challenges faced by MOFs in this field will also be discussed. This review aims to promote the development and progress of MOF chemistry and application research in the field of food safety, potentially leading to novel solutions.
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Affiliation(s)
- Pei-Long Wang
- Institute of Quality Standards and Testing Technology for Agro-products , Chinese Academy of Agricultural Sciences , Beijing 100081 , P. R. China.,Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemistry and Chemical Engineering, College of Environmental and Energy Engineering , Beijing University of Technology , Beijing 100124 , P. R. China
| | - Lin-Hua Xie
- Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemistry and Chemical Engineering, College of Environmental and Energy Engineering , Beijing University of Technology , Beijing 100124 , P. R. China
| | - Elizabeth A Joseph
- Department of Chemistry , Texas A&M University , P.O. Box 30012, College Station , Texas 77842-3012 , United States
| | - Jian-Rong Li
- Beijing Key Laboratory for Green Catalysis and Separation and Department of Chemistry and Chemical Engineering, College of Environmental and Energy Engineering , Beijing University of Technology , Beijing 100124 , P. R. China
| | - Xiao-Ou Su
- Institute of Quality Standards and Testing Technology for Agro-products , Chinese Academy of Agricultural Sciences , Beijing 100081 , P. R. China
| | - Hong-Cai Zhou
- Department of Chemistry , Texas A&M University , P.O. Box 30012, College Station , Texas 77842-3012 , United States
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18
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Relevance of Food Microbiology Issues to Current Trends (2008-2018) in Food Production and Imported Foods. Food Microbiol 2019. [DOI: 10.1128/9781555819972.ch42] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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19
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Collineau L, Boerlin P, Carson CA, Chapman B, Fazil A, Hetman B, McEwen SA, Parmley EJ, Reid-Smith RJ, Taboada EN, Smith BA. Integrating Whole-Genome Sequencing Data Into Quantitative Risk Assessment of Foodborne Antimicrobial Resistance: A Review of Opportunities and Challenges. Front Microbiol 2019; 10:1107. [PMID: 31231317 PMCID: PMC6558386 DOI: 10.3389/fmicb.2019.01107] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Accepted: 05/01/2019] [Indexed: 12/20/2022] Open
Abstract
Whole-genome sequencing (WGS) will soon replace traditional phenotypic methods for routine testing of foodborne antimicrobial resistance (AMR). WGS is expected to improve AMR surveillance by providing a greater understanding of the transmission of resistant bacteria and AMR genes throughout the food chain, and therefore support risk assessment activities. At this stage, it is unclear how WGS data can be integrated into quantitative microbial risk assessment (QMRA) models and whether their integration will impact final risk estimates or the assessment of risk mitigation measures. This review explores opportunities and challenges of integrating WGS data into QMRA models that follow the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR. We describe how WGS offers an opportunity to enhance the next-generation of foodborne AMR QMRA modeling. Instead of considering all hazard strains as equally likely to cause disease, WGS data can improve hazard identification by focusing on those strains of highest public health relevance. WGS results can be used to stratify hazards into strains with similar genetic profiles that are expected to behave similarly, e.g., in terms of growth, survival, virulence or response to antimicrobial treatment. The QMRA input distributions can be tailored to each strain accordingly, making it possible to capture the variability in the strains of interest while decreasing the uncertainty in the model. WGS also allows for a more meaningful approach to explore genetic similarity among bacterial populations found at successive stages of the food chain, improving the estimation of the probability and magnitude of exposure to AMR hazards at point of consumption. WGS therefore has the potential to substantially improve the utility of foodborne AMR QMRA models. However, some degree of uncertainty remains in relation to the thresholds of genetic similarity to be used, as well as the degree of correlation between genotypic and phenotypic profiles. The latter could be improved using a functional approach based on prediction of microbial behavior from a combination of 'omics' techniques (e.g., transcriptomics, proteomics and metabolomics). We strongly recommend that methodologies to incorporate WGS data in risk assessment be included in any future revision of the Codex Alimentarius Guidelines for Risk Analysis of Foodborne AMR.
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Affiliation(s)
- Lucie Collineau
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON, Canada
| | - Patrick Boerlin
- Department of Pathobiology, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Carolee A. Carson
- Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON, Canada
| | - Brennan Chapman
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON, Canada
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Aamir Fazil
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON, Canada
| | - Benjamin Hetman
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Scott A. McEwen
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - E. Jane Parmley
- Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON, Canada
| | - Richard J. Reid-Smith
- Centre for Foodborne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON, Canada
- Department of Population Medicine, Ontario Veterinary College, University of Guelph, Guelph, ON, Canada
| | - Eduardo N. Taboada
- National Microbiology Laboratory, Public Health Agency of Canada, Winnipeg, MB, Canada
| | - Ben A. Smith
- Public Health Risk Sciences Division, National Microbiology Laboratory, Public Health Agency of Canada, Guelph, ON, Canada
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20
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Dórea FC, Vial F, Hammar K, Lindberg A, Lambrix P, Blomqvist E, Revie CW. Drivers for the development of an Animal Health Surveillance Ontology (AHSO). Prev Vet Med 2019; 166:39-48. [PMID: 30935504 DOI: 10.1016/j.prevetmed.2019.03.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 01/07/2019] [Accepted: 03/05/2019] [Indexed: 02/01/2023]
Abstract
Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.
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Affiliation(s)
- Fernanda C Dórea
- Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
| | | | - Karl Hammar
- Department of Computer Science and Informatics, Jönköping University, Sweden; Department of Computer and Information Science, Linköping University, Sweden
| | - Ann Lindberg
- Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden
| | - Patrick Lambrix
- Department of Computer and Information Science, Linköping University, Sweden; Swedish e-Science Centre, Linköping University, Sweden
| | - Eva Blomqvist
- Department of Computer and Information Science, Linköping University, Sweden
| | - Crawford W Revie
- Atlantic Veterinary College, University of Prince Edward Island, Canada
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21
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Dooley DM, Griffiths EJ, Gosal GS, Buttigieg PL, Hoehndorf R, Lange MC, Schriml LM, Brinkman FSL, Hsiao WWL. FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration. NPJ Sci Food 2018; 2:23. [PMID: 31304272 PMCID: PMC6550238 DOI: 10.1038/s41538-018-0032-6] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 09/25/2018] [Indexed: 11/09/2022] Open
Abstract
The construction of high capacity data sharing networks to support increasing government and commercial data exchange has highlighted a key roadblock: the content of existing Internet-connected information remains siloed due to a multiplicity of local languages and data dictionaries. This lack of a digital lingua franca is obvious in the domain of human food as materials travel from their wild or farm origin, through processing and distribution chains, to consumers. Well defined, hierarchical vocabulary, connected with logical relationships-in other words, an ontology-is urgently needed to help tackle data harmonization problems that span the domains of food security, safety, quality, production, distribution, and consumer health and convenience. FoodOn (http://foodon.org) is a consortium-driven project to build a comprehensive and easily accessible global farm-to-fork ontology about food, that accurately and consistently describes foods commonly known in cultures from around the world. FoodOn addresses food product terminology gaps and supports food traceability. Focusing on human and domesticated animal food description, FoodOn contains animal and plant food sources, food categories and products, and other facets like preservation processes, contact surfaces, and packaging. Much of FoodOn's vocabulary comes from transforming LanguaL, a mature and popular food indexing thesaurus, into a World Wide Web Consortium (W3C) OWL Web Ontology Language-formatted vocabulary that provides system interoperability, quality control, and software-driven intelligence. FoodOn compliments other technologies facilitating food traceability, which is becoming critical in this age of increasing globalization of food networks.
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Affiliation(s)
- Damion M. Dooley
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
| | - Emma J. Griffiths
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC Canada
- Present Address: Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
| | - Gurinder S. Gosal
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
| | - Pier L. Buttigieg
- Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremen, Germany
| | - Robert Hoehndorf
- King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Matthew C. Lange
- Department of Food Science and Technology, UC Davis, Davis, CA USA
| | - Lynn M. Schriml
- Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD USA
| | - Fiona S. L. Brinkman
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC Canada
| | - William W. L. Hsiao
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC Canada
- Department of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC Canada
- British Columbia Centre for Disease Control Public Health Laboratory, Vancouver, BC Canada
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22
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Llarena A, Ribeiro‐Gonçalves BF, Nuno Silva D, Halkilahti J, Machado MP, Da Silva MS, Jaakkonen A, Isidro J, Hämäläinen C, Joenperä J, Borges V, Viera L, Gomes JP, Correia C, Lunden J, Laukkanen‐Ninios R, Fredriksson‐Ahomaa M, Bikandi J, Millan RS, Martinez‐Ballesteros I, Laorden L, Mäesaar M, Grantina‐Ievina L, Hilbert F, Garaizar J, Oleastro M, Nevas M, Salmenlinna S, Hakkinen M, Carriço JA, Rossi M. INNUENDO: A cross‐sectoral platform for the integration of genomics in the surveillance of food‐borne pathogens. ACTA ACUST UNITED AC 2018. [DOI: 10.2903/sp.efsa.2018.en-1498] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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23
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Pightling AW, Pettengill JB, Luo Y, Baugher JD, Rand H, Strain E. Interpreting Whole-Genome Sequence Analyses of Foodborne Bacteria for Regulatory Applications and Outbreak Investigations. Front Microbiol 2018; 9:1482. [PMID: 30042741 PMCID: PMC6048267 DOI: 10.3389/fmicb.2018.01482] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2018] [Accepted: 06/13/2018] [Indexed: 12/05/2022] Open
Abstract
Whole-genome sequence (WGS) analysis has revolutionized the food safety industry by enabling high-resolution typing of foodborne bacteria. Higher resolving power allows investigators to identify origins of contamination during illness outbreaks and regulatory activities quickly and accurately. Government agencies and industry stakeholders worldwide are now analyzing WGS data routinely. Although researchers have published many studies that assess the efficacy of WGS data analysis for source attribution, guidance for interpreting WGS analyses is lacking. Here, we provide the framework for interpreting WGS analyses used by the Food and Drug Administration's Center for Food Safety and Applied Nutrition (CFSAN). We based this framework on the experiences of CFSAN investigators, collaborations and interactions with government and industry partners, and evaluation of the published literature. A fundamental question for investigators is whether two or more bacteria arose from the same source of contamination. Analysts often count the numbers of nucleotide differences [single-nucleotide polymorphisms (SNPs)] between two or more genome sequences to measure genetic distances. However, using SNP thresholds alone to assess whether bacteria originated from the same source can be misleading. Bacteria that are isolated from food, environmental, or clinical samples are representatives of bacterial populations. These populations are subject to evolutionary forces that can change genome sequences. Therefore, interpreting WGS analyses of foodborne bacteria requires a more sophisticated approach. Here, we present a framework for interpreting WGS analyses that combines SNP counts with phylogenetic tree topologies and bootstrap support. We also clarify the roles of WGS, epidemiological, traceback, and other evidence in forming the conclusions of investigations. Finally, we present examples that illustrate the application of this framework to real-world situations.
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Affiliation(s)
- Arthur W. Pightling
- Biostatistics and Bioinformatics, Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
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24
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Fegan N, Jenson I. The role of meat in foodborne disease: Is there a coming revolution in risk assessment and management? Meat Sci 2018; 144:22-29. [PMID: 29716760 DOI: 10.1016/j.meatsci.2018.04.018] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 04/17/2018] [Accepted: 04/18/2018] [Indexed: 12/12/2022]
Abstract
Meat has featured prominently as a source of foodborne disease and a public health concern. For about the past 20 years the risk management paradigm has dominated international thinking about food safety. Control through the supply chain is supported by risk management concepts, as the public health risk at the point of consumption becomes the accepted outcome based measure. Foodborne pathogens can be detected at several points in the supply chain and determining the source of where these pathogens arise and how they behave throughout meat production and processing are important parts of risk based approaches. Recent improvements in molecular and genetic based technologies and data analysis for investigating source attribution and pathogen behaviour have enabled greater insights into how foodborne outbreaks occur and where controls can be implemented. These new approaches will improve our understanding of the role of meat in foodborne disease and are expected to have a significant impact on our understanding in the coming years.
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Affiliation(s)
- Narelle Fegan
- Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation, 671 Sneydes Rd, Werribee, VIC 3030, Australia.
| | - Ian Jenson
- Meat and Livestock Australia, Level 1, 40 Mount Street, North Sydney, NSW 2060, Australia
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25
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Le KK, Whiteside MD, Hopkins JE, Gannon VPJ, Laing CR. Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2018; 2018:1-10. [PMID: 30212910 PMCID: PMC6146121 DOI: 10.1093/database/bay086] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 07/23/2018] [Indexed: 01/16/2023]
Abstract
Public health laboratories are currently moving to whole-genome sequence (WGS)-based analyses, and require the rapid prediction of standard reference laboratory methods based solely on genomic data. Currently, these predictive genomics tasks rely on workflows that chain together multiple programs for the requisite analyses. While useful, these systems do not store the analyses in a genome-centric way, meaning the same analyses are often re-computed for the same genomes.
To solve this problem, we created Spfy, a platform that rapidly performs the common reference laboratory tests, uses a graph database to store and retrieve the results from the computational workflows and links data to individual genomes using standardized ontologies. The Spfy platform facilitates rapid phenotype identification, as well as the efficient storage and downstream comparative analysis of tens of thousands of genome sequences. Though generally applicable to bacterial genome sequences, Spfy currently contains 10 243 Escherichia coli genomes, for which in-silico serotype and Shiga-toxin subtype, as well as the presence of known virulence factors and antimicrobial resistance determinants have been computed. Additionally, the presence/absence of the entire E. coli pan-genome was computed and linked to each genome. Owing to its database of diverse pre-computed results, and the ability to easily incorporate user data, Spfy facilitates hypothesis testing in fields ranging from population genomics to epidemiology, while mitigating the re-computation of analyses. The graph approach of Spfy is flexible, and can accommodate new analysis software modules as they are developed, easily linking new results to those already stored. Spfy provides a database and analyses approach for E. coli that is able to match the rapid accumulation of WGS data in public databases.
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Affiliation(s)
- Kevin K Le
- National Microbiology Laboratory at Lethbridge, Public Health Agency of Canada, Lethbridge, Canada
| | - Matthew D Whiteside
- National Microbiology Laboratory at Lethbridge, Public Health Agency of Canada, Lethbridge, Canada
| | - James E Hopkins
- National Microbiology Laboratory at Lethbridge, Public Health Agency of Canada, Lethbridge, Canada
| | - Victor P J Gannon
- National Microbiology Laboratory at Lethbridge, Public Health Agency of Canada, Lethbridge, Canada
| | - Chad R Laing
- National Microbiology Laboratory at Lethbridge, Public Health Agency of Canada, Lethbridge, Canada
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