The Role of Genomics in Tracing Foodborne Outbreaks
Foodborne outbreaks pose significant public health challenges worldwide, often causing illness, hospitalizations, and even fatalities. However, the landscape of outbreak investigation has been revolutionized by the advent of genomics, particularly whole genome sequencing (WGS).
Understanding Foodborne Outbreaks
Foodborne outbreaks occur when two or more people experience similar illnesses from consuming the same contaminated food or beverage. These outbreaks can be caused by various pathogens including bacteria (e.g., *Salmonella*, *E. coli*), viruses (e.g., Norovirus, Hepatitis A), parasites (e.g., *Cryptosporidium*), and toxins (e.g., botulinum toxin). Tracing the source of contamination is crucial for mitigating ongoing risks, implementing targeted interventions, and preventing future outbreaks.
Detecting Outbreaks with Whole Genome Sequencing
Whole genome sequencing (WGS) has revolutionized the detection and investigation of foodborne outbreaks. WGS provides a highly detailed DNA fingerprint, enabling epidemiologists and laboratory scientists to outbreaks sooner, including many that would previously have gone undetected. As a result, numerous national public health bodies have recently adopted WGS for real-time surveillance of foodborne bacterial pathogens.
Retrospective Outbreak Investigations
WGS can uncover persistent, low-level outbreaks undetectable by traditional methods. By providing a high level of detail regarding the genetic makeup of pathogens, WGS can identify outbreaks that may not manifest as significant clusters of illness but can still pose serious public health risks. Through the high-resolution data provided by WGS, epidemiologists can discern genetic similarities among cases that might appear unrelated through other methods. This precise identification helps in establishing connections between cases, facilitating quicker responses and interventions that can mitigate the spread of the outbreak.
The high level of strain differentiation also enhances the specificity of case definitions during investigations. This increased precision is vital in distinguishing between cases associated with the outbreak and those caused by unrelated strains. Such differentiation minimizes the risk of misclassification, ensuring that public health resources are directed appropriately and effectively. Furthermore, WGS supports robust epidemiological investigations by facilitating the mapping of transmission pathways. Understanding how a pathogen spreads within a population allows for strategic interventions that can effectively break the chain of transmission.
For example, WGS was able to detect a prolonged outbreak linked to reptile feeder mice in the UK. Traditional surveillance categorized these cases as sporadic and unlinked. However, WGS revealed genetic connections among cases, enabling health authorities to identify the common source of contamination. By recognizing that the same strain was responsible for multiple cases, public health officials were able to implement targeted measures to address the source and prevent further infections.
Predicting Future Outbreaks
By comparing genomes from different cases, scientists can reconstruct transmission pathways and identify common exposures or transmission routes. This information is invaluable for understanding how pathogens spread within communities and among populations.
Source attribution analysis seeks to determine the relative contributions of various sources to human disease. WGS significantly enhances these models by providing high-resolution genetic data that allows for precise source tracing. By analyzing the genetic similarities and differences between human isolates and potential environmental or food sources, scientists can infer the origins of human infections with greater accuracy. This probabilistic approach aids public health officials in prioritizing interventions, as they can focus on the most significant sources of contamination that pose a risk to public health.
Moreover, WGS allows for the monitoring of new variants within foodborne pathogen populations. For example, the emergence of Shiga toxin subtype Stx2a in *Shiga toxin-producing Escherichia coli* (STEC) O157 has been linked to the development of highly pathogenic clones. These virulent strains pose heightened risks to public health due to their increased potential for causing severe illness. Real-time WGS surveillance enables public health officials to track the emergence of new pathogenic variants and associated transmission modes. This capability enhances the prediction and prevention of future outbreaks, as officials can rapidly respond to the identification of virulent clones and implement necessary control measures.
Furthermore, WGS facilitates ongoing monitoring of pathogen populations, allowing for the detection of genetic changes that may indicate shifts in virulence or resistance patterns. By continuously analyzing genomic data, public health agencies can stay ahead of emerging threats and adapt their surveillance and intervention strategies accordingly. This proactive approach is essential in the face of evolving pathogens, particularly in an era marked by globalization and increased interconnectivity, which can facilitate the rapid spread of foodborne diseases across borders.
Monitoring Antimicrobial Resistance in Foodborne Pathogens
Antimicrobial resistance (AMR) is an escalating global public health threat that compromises the effectiveness of antibiotics and other antimicrobial agents. WGS can reliably detect drug susceptibility and resistance markers in bacterial and viral pathogens, improving AMR profiling and biological risk prediction. Integrating WGS into frontline public health practices can enhance monitoring and mitigation of AMR transmission risks from animals to humans through the food chain.
By providing detailed assessments of the genetic basis of antimicrobial resistance, WGS allows researchers to identify specific genes associated with resistance, their genetic locations within the microbial genome, and potential mechanisms for multidrug resistance. This level of precision is vital in tracking the emergence and spread of resistance traits among foodborne pathogens, ultimately aiding in the development of targeted interventions to mitigate these threats.
One notable initiative utilizing WGS for AMR monitoring is the National Antimicrobial Resistance Monitoring System (NARMS), a collaborative effort involving the Centers for Disease Control and Prevention (CDC), the Food and Drug Administration (FDA), the Food Safety and Inspection Service (FSIS), and the Agricultural Research Service (ARS). Since incorporating WGS into its surveillance activities in 2015, NARMS has significantly enhanced its ability to detect and characterize novel AMR genes. For instance, the identification of the extended-β-lactamase CTX-M-65 gene in *Salmonella* serotype Infantis, as well as plasmid-mediated quinolone resistance in *Salmonella* strains isolated from food animals and retail meat, exemplifies how WGS can uncover emerging resistance patterns.
Case Study: Salmonella Outbreaks
Salmonella is one of the most prevalent causes of foodborne illness worldwide. In 2018, outbreak investigations revealed 149 cases of Salmonella serotype Enteritidis across seven states in the United States.
Traditionally, outbreak investigations have relied on methods like pulsed-field gel electrophoresis (PFGE) to identify and classify the genetic profiles of pathogens. While PFGE can provide some level of discrimination among strains, it often falls short when differentiating between closely related or unrelated cases, which can complicate the investigation process. In the 2018 Salmonella outbreak, related cases could be roughly sorted with PFGE, but unrelated cases often complicated investigations, making it difficult to trace the precise source of contamination effectively.
In contrast, WGS enabled more precise clustering of outbreak cases, revealing connections between cases that PFGE could not. By comparing the entire genomic sequences of the Salmonella isolates from the affected individuals, WGS revealed a more extensive network of linked cases, indicating a larger outbreak than initially perceived. For example, WGS identified several patients not initially linked to the outbreak in two restaurants in different states. The epidemiologic data suggested that shell eggs were the cause, and WGS confirmed the attribution, leading to a nationwide recall.
References
Brown, E., Dessai, U., McGarry, S., & Gerner-Smidt, P. (2019). Use of Whole-Genome Sequencing for Food Safety and Public Health in the United States. Foodborne pathogens and disease, 16(7), 441–450. https://doi.org/10.1089/fpd.2019.2662
CDC. (2024, March 4). Detecting Outbreaks with Whole Genome Sequencing. https://www.cdc.gov/advanced-molecular-detection/about/detecting-outbreaks.html
WHO. (2023, October 19). Whole genome sequencing as a tool to strengthen foodborne disease surveillance and response. Module 2: Whole genome sequencing in foodborne disease outbreak investigations. https://www.who.int/publications/i/item/9789240021242
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