Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world

Abstract

Foodborne illnesses pose a substantial health and economic burden, presenting challenges in prevention due to the diverse microbial hazards that can enter and spread within food systems. Various factors, including natural, political and commercial drivers, influence food production and distribution. The risks of foodborne illness will continue to evolve in step with these drivers and with changes to food systems. For example, climate impacts on water availability for agriculture, changes in food sustainability targets and evolving customer preferences can all have an impact on the ecology of foodborne pathogens and the agrifood niches that can carry microorganisms. Whole-genome and metagenome sequencing, combined with microbial surveillance schemes and insights from the food system, can provide authorities and businesses with transformative information to address risks and implement new food safety interventions across the food chain. In this Review, we describe how genome-based approaches have advanced our understanding of the evolution and spread of enduring bacterial foodborne hazards as well as their role in identifying emerging foodborne hazards. Furthermore, foodborne hazards exist in complex microbial communities across the entire food chain, and consideration of these co-existing organisms is essential to understanding the entire ecology supporting pathogen persistence and transmission in an evolving food system.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Ecological and socioeconomic drivers that may shape the microbial landscape of food and food production systems.
Fig. 2: Relationship between pathogen populations and the dynamics of foodborne illness.
Fig. 3: International impacts and prioritization of bacterial foodborne illness.
Fig. 4: One Health approach to ecological frameworks and epidemiology in food safety.
Fig. 5: Tracing bacterial hazards in the food chain.
Fig. 6: The impact of high-resolution WGS on the detection and resolution of foodborne outbreaks.

Similar content being viewed by others

References

  1. Zurek, M. et al. Assessing sustainable food and nutrition security of the EU food system-an integrated approach. Sustainability 10, 4271 (2018).

    Article  Google Scholar 

  2. Parsons, K., Hawkes, C. & Wells, R. Brief 2. Understanding the Food System: Why it Matters for Food Policy (Centre for Food Policy, 2019).

  3. Hasnain, S., Ingram, J. & Zurek, M. Mapping the UK Food System — A Report for the UKRI Transforming UK Food Systems Programme (Environmental Change Institute, 2020).

  4. Food and Agriculture Organization. If It Isn’t Safe, It Isn’t Food https://www.fao.org/newsroom/story/If-it-isn-t-safe-it-isn-t-food/ (2019).

  5. Food and Agriculture Organization. FAO Strategic Priorities for Food Safety within the FAO Strategic Framework 2022-2031 (FAO, 2023).

  6. Climate Change Committee. Progress in Adapting to Climate Change; 2023 Report to Parliament https://www.theccc.org.uk/publication/progress-in-adapting-to-climate-change-2023-report-to-parliament/ (2023).

  7. European Food Safety Authority. Maggiore, A., Afonso, A., Barrucci, F. & De Sanctis, G. Climate Change as a Driver of Emerging Risks for Food and Feed Safety, Plant, Animal Health and Nutritional Quality (EFSA, 2020).

  8. Morgado, M. E. et al. Climate change, extreme events, and increased risk of salmonellosis: foodborne diseases active surveillance network (FoodNet), 2004-2014. Env. Health 20, 105 (2021).

    Article  Google Scholar 

  9. Food and Agriculture Organization. Final Meeting Report: Technical Meeting on the Impact of Whole Genome Sequencing (WGS) on Food Safety Management: Within a One Health Approach (FAO, 2016).

  10. Morse, S. S. Factors in the emergence of infectious diseases. Emerg. Infect. Dis. 1, 7–15 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Grad, Y. H. et al. Genomic epidemiology of the Escherichia coli O104:H4 outbreaks in Europe, 2011. Proc. Natl Acad. Sci. USA 109, 3065–3070 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kingsley, R. A. et al. Epidemic multiple drug resistant Salmonella Typhimurium causing invasive disease in sub-Saharan Africa have a distinct genotype. Genome Res. 19, 2279–2287 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Seribelli, A. A. et al. Phylogenetic analysis revealed that Salmonella Typhimurium ST313 isolated from humans and food in Brazil presented a high genomic similarity. Braz. J. Microbiol. 51, 53–64 (2020).

    Article  CAS  PubMed  Google Scholar 

  14. Almeida, F. et al. Multilocus sequence typing of Salmonella Typhimurium reveals the presence of the highly invasive ST313 in Brazil. Infect. Genet. Evol. 51, 41–44 (2017).

    Article  CAS  PubMed  Google Scholar 

  15. Bian, X. et al. Campylobacter abundance in breastfed infants and identification of a new species in the Global Enterics Multicenter Study. mSphere 5, e00735-19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Ramees, T. P. et al. Arcobacter: an emerging food-borne zoonotic pathogen, its public health concerns and advances in diagnosis and control — a comprehensive review. Vet. Q. 37, 136–161 (2017).

    Article  PubMed  Google Scholar 

  17. Centers for Disease Control and Prevention. Reoccurring, Emerging, and Persisting Enteric Bacterial Strains https://www.cdc.gov/ncezid/dfwed/outbreak-response/rep-strains.html (2023).

  18. World Health Organization. WHO Estimates of the Global Burden of Foodborne Diseases: Foodborne Disease Burden Epidemiology Reference Group 2007-2015 (WHO, 2015).

  19. Devleesschauwer, B., Haagsma, J. A., Mangen, M.-J. J., Lake, R. J. & Havelaar, A. H. In: Food Safety Economics (ed. Roberts, T.) (Springer, 2018).

  20. Abebe, E., Gugsa, G. & Ahmed, M. Review on major food-borne zoonotic bacterial pathogens. J. Trop. Med. 2020, 4674235 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Bintsis, T. Foodborne pathogens. AIMS Microbiol. 3, 529–563 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Foley, S. L., Lynne, A. M. & Nayak, R. Molecular typing methodologies for microbial source tracking and epidemiological investigations of gram-negative bacterial foodborne pathogens. Infect. Genet. Evol. 9, 430–440 (2009).

    Article  CAS  PubMed  Google Scholar 

  23. Deng, X., den Bakker, H. C. & Hendriksen, R. S. Genomic epidemiology: whole-genome-sequencing-powered surveillance and outbreak investigation of foodborne bacterial pathogens. Annu. Rev. Food Sci. Technol. 7, 353–374 (2016).

    Article  PubMed  Google Scholar 

  24. Taboada, E. N., Graham, M. R., Carrico, J. A. & Van Domselaar, G. Food safety in the age of next generation sequencing, bioinformatics, and open data access. Front. Microbiol. 8, 909 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  25. Threlfall, E. J. Epidemic Salmonella Typhimurium DT104 — a truly international multiresistant clone. J. Antimicrob. Chemother. 46, 7–10 (2000).

    Article  CAS  PubMed  Google Scholar 

  26. Petrovska, L. et al. Microevolution of monophasic Salmonella Typhimurium during epidemic, United Kingdom, 2005-2010. Emerg. Infect. Dis. 22, 617–624 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Alikhan, N. F. et al. Dynamics of Salmonella enterica and antimicrobial resistance in the Brazilian poultry industry and global impacts on public health. PLoS Genet. 18, e1010174 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Langridge, G. C. et al. Patterns of genome evolution that have accompanied host adaptation in Salmonella. Proc. Natl Acad. Sci. USA 112, 863–868 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Moura, A. et al. Whole genome-based population biology and epidemiological surveillance of Listeria monocytogenes. Nat. Microbiol. 2, 16185 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ma, L.-C., Zhao, H.-Q., Wu, L. B., Cheng, Z.-L. & Liu, C. Impact of the microbiome on human, animal and environmental health from a One Health perspective. Sci. One Health 2, 100037 (2023).

    Article  Google Scholar 

  31. Srikumar, S. et al. RNA sequencing-based transcriptional overview of xerotolerance in Cronobacter sakazakii SP291. Appl. Environ. Microbiol. 85, e01993-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Luo, N. et al. Enhanced in vivo fitness of fluoroquinolone-resistant Campylobacter jejuni in the absence of antibiotic selection pressure. Proc. Natl Acad. Sci. USA 102, 541–546 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Marmion, M., Macori, G., Ferone, M., Whyte, P. & Scannell, A. G. M. Survive and thrive: control mechanisms that facilitate bacterial adaptation to survive manufacturing-related stress. Int. J. Food Microbiol. 368, 109612 (2022).

    Article  CAS  PubMed  Google Scholar 

  34. Gauvry, E. et al. Knowledge of the physiology of spore-forming bacteria can explain the origin of spores in the food environment. Res. Microbiol. 168, 369–378 (2017).

    Article  PubMed  Google Scholar 

  35. NicAogain, K. & O’Byrne, C. P. The role of stress and stress adaptations in determining the fate of the bacterial pathogen Listeria monocytogenes in the food chain. Front. Microbiol. 7, 1865 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Teh, A. H. T., Lee, S. M. & Dykes, G. A. Association of some Campylobacter jejuni with Pseudomonas aeruginosa biofilms increases attachment under conditions mimicking those in the environment. PLoS One 14, e0215275 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Whelan, M. V. X. et al. Acquisition of fluoroquinolone resistance leads to increased biofilm formation and pathogenicity in Campylobacter jejuni. Sci. Rep. 9, 18216 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ong, K. J. et al. Food safety considerations and research priorities for the cultured meat and seafood industry. Compr. Rev. Food Sci. Food Saf. 20, 5421–5448 (2021).

    Article  PubMed  Google Scholar 

  39. Food and Agriculture Organization of the United Nations & World Health Organization. Food Safety Aspects of Cell-based Food (FAO & WHO, 2023).

  40. Bartula, K., Begley, M., Latour, N. & Callanan, M. Growth of food-borne pathogens Listeria and Salmonella and spore-forming Paenibacillus and Bacillus in commercial plant-based milk alternatives. Food Microbiol. 109, 104143 (2023).

    Article  CAS  PubMed  Google Scholar 

  41. Li, M. et al. Global disease burden of pathogens in animal source foods, 2010. PLoS One 14, e0216545 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Jaffee, S., Henson, S., Unnevehr, L., Grace, D. & Cassou, E. The Safe Food Imperative: Accelerating Progress in Low- and Middle-income Countries (World Bank, 2019).

  43. Hellberg, R. S. & Chu, E. Effects of climate change on the persistence and dispersal of foodborne bacterial pathogens in the outdoor environment: a review. Crit. Rev. Microbiol. 42, 548–572 (2016).

    Article  PubMed  Google Scholar 

  44. Jones, B. A. et al. Zoonosis emergence linked to agricultural intensification and environmental change. Proc. Natl Acad. Sci. USA 110, 8399–8404 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mora, C. et al. Over half of known human pathogenic diseases can be aggravated by climate change. Nat. Clim. Change 12, 869–875 (2022).

    Article  Google Scholar 

  46. Archer, E. J. et al. Climate warming and increasing Vibrio vulnificus infections in North America. Sci. Rep. 13, 3893 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Vezzulli, L. et al. Climate influence on Vibrio and associated human diseases during the past half-century in the coastal North Atlantic. Proc. Natl Acad. Sci. USA 113, E5062–E5071 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Baker-Austin, C. et al. Emerging Vibrio risk at high latitudes in response to ocean warming. Nat. Clim. Change 3, 73–77 (2013).

    Article  Google Scholar 

  49. Food Standards Agency. The Burden of Foodborne Disease in the UK 2018 (FSA, 2020).

  50. Kristensen, J. M., Nierychlo, M., Albertsen, M. & Nielsen, P. H. Bacteria from the genus Arcobacter are abundant in effluent from wastewater treatment plants. Appl. Environ. Microbiol. 86, e03044-19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Billington, C., Kingsbury, J. M. & Rivas, L. Metagenomics approaches for improving food safety: a review. J. Food Prot. 85, 448–464 (2022).

    Article  CAS  PubMed  Google Scholar 

  52. Bloomfield, S. J. et al. Determination and quantification of microbial communities and antimicrobial resistance on food through host DNA-depleted metagenomics. Food Microbiol. 110, 104162 (2023).

    Article  CAS  PubMed  Google Scholar 

  53. Carleton, H. A. et al. Metagenomic approaches for public health surveillance of foodborne infections: opportunities and challenges. Foodborne Pathog. Dis. 16, 474–479 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Ray, L. C. et al. Changing diagnostic testing practices for foodborne pathogens, Foodborne Diseases Active Surveillance Network, 2012-2019. Open Forum Infect. Dis. 9, ofac344 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Anyansi, C., Straub, T. J., Manson, A. L., Earl, A. M. & Abeel, T. Computational methods for strain-level microbial detection in colony and metagenome sequencing data. Front. Microbiol. 11, 1925 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Kang, X., Luo, X. & Schonhuth, A. StrainXpress: strain aware metagenome assembly from short reads. Nucleic Acids Res. 50, e101 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Rolon, M. L., Voloshchuk, O., Bartlett, K. V., LaBorde, L. F. & Kovac, J. Multi-species biofilms of environmental microbiota isolated from fruit packing facilities promoted tolerance of Listeria monocytogenes to benzalkonium chloride. Biofilm 7, 100177 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Lupolova, N., Dallman, T. J., Matthews, L., Bono, J. L. & Gally, D. L. Support vector machine applied to predict the zoonotic potential of E. coli O157 cattle isolates. Proc. Natl Acad. Sci. USA 113, 11312–11317 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Im, H., Hwang, S. H., Kim, B. S. & Choi, S. H. Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model. Proc. Natl Acad. Sci. USA 118, e2018877118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Burgaya, J. et al. The bacterial genetic determinants of Escherichia coli capacity to cause bloodstream infections in humans. PLoS Genet. 19, e1010842 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Sadilek, A. et al. Machine-learned epidemiology: real-time detection of foodborne illness at scale. NPJ Digit. Med. 1, 36 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Franz, E. et al. Phylogeographic analysis reveals multiple international transmission events have driven the global emergence of Escherichia coli O157:H7. Clin. Infect. Dis. 69, 428–437 (2019).

    Article  CAS  PubMed  Google Scholar 

  63. Moura, A. et al. Emergence and global spread of Listeria monocytogenes main clinical clonal complex. Sci. Adv. 7, eabj9805 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Bayliss, S. C. et al. Rapid geographical source attribution of Salmonella enterica serovar enteritidis genomes using hierarchical machine learning. eLife 12, e84167 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Brown, B., Allard, M., Bazaco, M. C., Blankenship, J. & Minor, T. An economic evaluation of the whole genome sequencing source tracking program in the U.S. PLoS One 16, e0258262 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Thomas, J. et al. Outbreak of listeriosis in South Africa associated with processed meat. N. Engl. J. Med. 382, 632–643 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Kovanen, S. M. et al. Multilocus sequence typing (MLST) and whole-genome MLST of Campylobacter jejuni isolates from human infections in three districts during a seasonal peak in Finland. J. Clin. Microbiol. 52, 4147–4154 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Yang, C. et al. Outbreak dynamics of foodborne pathogen Vibrio parahaemolyticus over a seventeen year period implies hidden reservoirs. Nat. Microbiol. 7, 1221–1229 (2022).

    Article  CAS  PubMed  Google Scholar 

  69. Pightling, A. W. et al. Interpreting whole-genome sequence analyses of foodborne bacteria for regulatory applications and outbreak investigations. Front. Microbiol. 9, 1482 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Duval, A., Opatowski, L. & Brisse, S. Defining genomic epidemiology thresholds for common-source bacterial outbreaks: a modelling study. Lancet Microbe 4, e349–e357 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Hoffmann, M. et al. Tracing origins of the Salmonella Bareilly strain causing a food-borne outbreak in the United States. J. Infect. Dis. 213, 502–508 (2016).

    Article  PubMed  Google Scholar 

  72. Dooley, D. M. et al. FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration. NPJ Sci. Food 2, 23 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  73. National Center for Biotechnology Information. NCBI Datasets https://www.ncbi.nlm.nih.gov/datasets/ (2024).

  74. Timme, R. E. et al. GenomeTrakr proficiency testing for foodborne pathogen surveillance: an exercise from 2015. Microb. Genom. 4, e000185 (2018).

    PubMed  PubMed Central  Google Scholar 

  75. Moran-Gilad, J. et al. Proficiency testing for bacterial whole genome sequencing: an end-user survey of current capabilities, requirements and priorities. BMC Infect. Dis. 15, 174 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Jolley, K. A., Bray, J. E. & Maiden, M. C. J. Open-access bacterial population genomics: BIGSdb software, the PubMLST.org website and their applications. Wellcome Open Res. 3, 124 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Baker, K. S. et al. Genomics for public health and international surveillance of antimicrobial resistance. Lancet Microbe 4, e1047–e1055 (2023).

    Article  PubMed  Google Scholar 

  78. Kovac, J., den Bakker, H., Carroll, L. M. & Wiedmann, M. Precision food safety: a systems approach to food safety facilitated by genomics tools. TrAC Trends Anal. Chem. 96, 52–61 (2017).

    Article  CAS  Google Scholar 

  79. World Health Organization. Whole Genome Sequencing as a Tool to Strengthen Foodborne Disease Surveillance and Response: Module 1: Introductory Module (WHO, 2023).

  80. Jackson, B. R. et al. Implementation of nationwide real-time whole-genome sequencing to enhance listeriosis outbreak detection and investigation. Clin. Infect. Dis. 63, 380–386 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Dallman, T. J. et al. Whole-genome sequencing for national surveillance of Shiga toxin-producing Escherichia coli O157. Clin. Infect. Dis. 61, 305–312 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Grace, D. Food safety in low and middle income countries. Int. J. Env. Res. Public Health 12, 10490–10507 (2015).

    Article  CAS  Google Scholar 

  83. Apruzzese, I. et al. Investing in food safety for developing countries: opportunities and challenges in applying whole-genome sequencing for food safety management. Foodborne Pathog. Dis. 16, 463–473 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Hadfield, J. et al. Nextstrain: real-time tracking of pathogen evolution. Bioinformatics 34, 4121–4123 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Argimon, S. et al. Microreact: visualizing and sharing data for genomic epidemiology and phylogeography. Microb. Genom. 2, e000093 (2016).

    PubMed  PubMed Central  Google Scholar 

  86. Argimon, S. et al. A global resource for genomic predictions of antimicrobial resistance and surveillance of Salmonella Typhi at pathogenwatch. Nat. Commun. 12, 2879 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Gangavarapu, K. et al. Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations. Nat. Methods 20, 512–522 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Reuter, S. et al. Directional gene flow and ecological separation in Yersinia enterocolitica. Microb. Genom. 1, e000030 (2015).

    PubMed  PubMed Central  Google Scholar 

  89. Rodrigues, J. A. et al. Pangenomic analyses of antibiotic-resistant Campylobacter jejuni reveal unique lineage distributions and epidemiological associations. Microb. Genom. 9, mgen001073 (2023).

    PubMed  PubMed Central  Google Scholar 

  90. Neves, A. et al. The Swiss Pathogen Surveillance Platform — towards a nation-wide One Health data exchange platform for bacterial, viral and fungal genomics and associated metadata. Micro. Genom. 9, mgen001073 (2023).

    Google Scholar 

  91. Sears, A. et al. Marked campylobacteriosis decline after interventions aimed at poultry, New Zealand. Emerg. Infect. Dis. 17, 1007–1015 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  92. Gardner, T. J. et al. Outbreak of campylobacteriosis associated with consumption of raw peas. Clin. Infect. Dis. 53, 26–32 (2011).

    Article  PubMed  Google Scholar 

  93. Cody, A. J., Maiden, M. C., Strachan, N. J. & McCarthy, N. D. A systematic review of source attribution of human campylobacteriosis using multilocus sequence typing. Eur. Surveill. 24, 1800696 (2019).

    Article  Google Scholar 

  94. Pires, S. M., Vieira, A. R., Hald, T. & Cole, D. Source attribution of human salmonellosis: an overview of methods and estimates. Foodborne Pathog. Dis. 11, 667–676 (2014).

    Article  PubMed  Google Scholar 

  95. Mullner, P. et al. Assigning the source of human campylobacteriosis in New Zealand: a comparative genetic and epidemiological approach. Infect. Genet. Evol. 9, 1311–1319 (2009).

    Article  PubMed  Google Scholar 

  96. Sheppard, S. K. et al. Campylobacter genotyping to determine the source of human infection. Clin. Infect. Dis. 48, 1072–1078 (2009).

    Article  PubMed  Google Scholar 

  97. Mughini-Gras, L. et al. Tracing the sources of human salmonellosis: a multi-model comparison of phenotyping and genotyping methods. Infect. Genet. Evol. 28, 251–260 (2014).

    Article  PubMed  Google Scholar 

  98. Wilson, D. J. et al. Tracing the source of campylobacteriosis. PLoS Genet. 4, e1000203 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Liao, S. J., Marshall, J., Hazelton, M. L. & French, N. P. Extending statistical models for source attribution of zoonotic diseases: a study of campylobacteriosis. J. R. Soc. Interface 16, 20180534 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Arning, N., Sheppard, S. K., Bayliss, S., Clifton, D. A. & Wilson, D. J. Machine learning to predict the source of campylobacteriosis using whole genome data. PLoS Genet. 17, e1009436 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Munck, N., Njage, P. M. K., Leekitcharoenphon, P., Litrup, E. & Hald, T. Application of whole-genome sequences and machine learning in source attribution of Salmonella Typhimurium. Risk Anal. 40, 1693–1705 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Wainaina, L. et al. Source attribution of human campylobacteriosis using whole-genome sequencing data and network analysis. Pathogens 11, 645 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  103. Jehanne, Q. et al. Genome-wide identification of host-segregating single-nucleotide polymorphisms for source attribution of clinical Campylobacter coli isolates. Appl. Environ. Microbiol. 86, e01787-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  104. Duarte, A. S. R. et al. Metagenomics-based approach to source-attribution of antimicrobial resistance determinants — identification of reservoir resistome signatures. Front. Microbiol. 11, 601407 (2020).

    Article  PubMed  Google Scholar 

  105. Pasquali, F., Remondini, D., Snary, E. L., Hald, T. & Guillier, L. Editorial: integrating whole genome sequencing into source attribution and risk assessment of foodborne bacterial pathogens. Front. Microbiol. 12, 795098 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Tanui, C. K., Benefo, E. O., Karanth, S. & Pradhan, A. K. A machine learning model for food source attribution of Listeria monocytogenes. Pathogens 11, 691 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Liao, J. et al. Comparative genomics unveils extensive genomic variation between populations of Listeria species in natural and food-associated environments. ISME Commun. 3, 85 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Beck, K. L. et al. Monitoring the microbiome for food safety and quality using deep shotgun sequencing. NPJ Sci. Food 5, 3 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Mughini-Gras, L. et al. Risk factors for human salmonellosis originating from pigs, cattle, broiler chickens and egg laying hens: a combined case-control and source attribution analysis. PLoS One 9, e87933 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  110. Lake, R. J. et al. Source attributed case-control study of campylobacteriosis in New Zealand. Int. J. Infect. Dis. 103, 268–277 (2021).

    Article  CAS  PubMed  Google Scholar 

  111. Amini, S. How next-generation sequencing will impact your food safety program. FoodNavigator Europe https://www.foodnavigator.com/Article/2018/12/10/How-next-generation-sequencing-will-impact-your-food-safety-program (William Reed, 2018).

  112. Gerner-Smidt, P. Whole-Genome Sequencing for Food Safety. Food Safety Magazine https://www.food-safety.com/articles/7205-whole-genome-sequencing-for-food-safety (BNP Media, 2021).

  113. Global Food Safety Initiative. A Culture of Food Safety: A Position Paper from the Global Food Safety Initiative (GFSI) (GFSI, 2018).

  114. Espinoza, M. S. A., Flink, C., Boisen, N., Scheutz, F. & Käsbohrer, A. Microbiological sampling and analyses in the food business operators’ HACCP-based self-control programmes. Front. Food Sci. Technol. https://doi.org/10.3389/frfst.2023.1110359 (2023).

  115. Chilled Food Association. Principles of an Environmental Monitoring Program for the Management of Listeria monocytogenes (CFA, 2023).

  116. Jagadeesan, B. et al. The use of next generation sequencing for improving food safety: translation into practice. Food Microbiol. 79, 96–115 (2019).

    Article  CAS  PubMed  Google Scholar 

  117. Tran, M. et al. Economic evaluations of whole-genome sequencing for pathogen identification in public health surveillance and health-care-associated infections: a systematic review. Lancet Microbe 4, e953–e962 (2023).

    Article  PubMed  Google Scholar 

  118. Jain, S., Mukhopadhyay, K. & Thomassin, P. J. An economic analysis of Salmonella detection in fresh produce, poultry, and eggs using whole genome sequencing technology in Canada. Food Res. Int. 116, 802–809 (2019).

    Article  CAS  PubMed  Google Scholar 

  119. World Health Organization. Food Safety https://www.who.int/news-room/fact-sheets/detail/food-safety (2022).

  120. Ogden, N. H., AbdelMalik, P. & Pulliam, J. Emerging infectious diseases: prediction and detection. Can. Commun. Dis. Rep. 43, 206–211 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Plowright, R. K. et al. Pathways to zoonotic spillover. Nat. Rev. Microbiol. 15, 502–510 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Falloon, P. et al. What do changing weather and climate shocks and stresses mean for the UK food system? Env. Res. Lett. 17, 051001 (2022).

    Article  Google Scholar 

  123. Elliott, C. In: New Food (Russell Publishing Ltd., 2022). https://www.newfoodmagazine.com/article/168316/five-food-safety-challenges/.

  124. McLauchlin, J. et al. An outbreak of human listeriosis associated with frozen sweet corn consumption: investigations in the UK. Int. J. Food Microbiol. 338, 108994 (2021).

    Article  CAS  PubMed  Google Scholar 

  125. Kindle, P., Nuesch-Inderbinen, M., Cernela, N. & Stephan, R. Detection, isolation, and characterization of Shiga toxin-producing Escherichia coli in flour. J. Food Prot. 82, 164–167 (2019).

    Article  PubMed  Google Scholar 

  126. Bloomfield, S. J. et al. Genomic analysis of Salmonella enterica serovar Typhimurium DT160 associated with a 14-year outbreak, New Zealand, 1998-2012. Emerg. Infect. Dis. 23, 906–913 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Pijnacker, R. et al. An international outbreak of Salmonella enterica serotype enteritidis linked to eggs from Poland: a microbiological and epidemiological study. Lancet Infect. Dis. 19, 778–786 (2019).

    Article  PubMed  Google Scholar 

  128. Centers for Disease Control and Prevention. A-Z Index for Foodborne Illness https://www.cdc.gov/foodsafety/diseases/index.html (2021).

  129. European Food Safety Authority (EFSA); European Centre for Disease Prevention and Control (ECDC). The European Union One Health 2021 zoonoses report. EFSA J. 20, e07666 (2022).

    PubMed Central  Google Scholar 

  130. Wagenaar, J. A., French, N. P. & Havelaar, A. H. Preventing Campylobacter at the source: why is it so difficult? Clin. Infect. Dis. 57, 1600–1606 (2013).

    Article  PubMed  Google Scholar 

  131. Centers for Disease Control and Prevention. Detecting Outbreaks with Whole Genome Sequencing https://www.cdc.gov/amd/how-it-works/detecting-outbreaks-wgs.html (2019).

Download references

Acknowledgements

A.E.M. and M.W.G. are supported by the Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Microbes and Food Safety BB/X011011/1 and its constituent project BBS/E/F/000PR13634 (Theme 1, Microbial threats from foods in established and evolving food systems). This work was also supported in part by BBSRC grants BB/V018221/1 and BB/X002985/1 (UK Food Safety Research Network). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

The authors contributed equally to all aspects of the article.

Corresponding author

Correspondence to Alison E. Mather.

Ethics declarations

Competing interests

International Patent Application No. PCT/GB2023/050906 entitled “Determination and quantification of the microbial communities and antimicrobial resistance genes on food” in the name of Quadram Institute Bioscience has been filed (priority date 05/04/2022) and is currently in the international phase; A.E.M. is an inventor. This relates to the aspect of the Review where it is mentioned that the potential detection of pathogens through metagenomics can be affected by the amount of contaminating host DNA and sequencing depth. N.P.F. is a member of the International Commission on Microbiological Specifications for Foods (ICMSF) and an Emeritus Director of the New Zealand Food Safety Science and Research Centre. Both organizations receive support from the food industry and government agencies. Both roles are unpaid and advisory, and related to food safety research, and neither organization influenced the content contributed by N.P.F. to the Review. The other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Microbiology thanks Steven Djordjevic, who co-reviewed with Veronica Jarocki; Jasna Kovac; and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mather, A.E., Gilmour, M.W., Reid, S.W.J. et al. Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world. Nat Rev Microbiol (2024). https://doi.org/10.1038/s41579-024-01051-z

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41579-024-01051-z

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research