In an article published in Critical Reviews in Food Science and Nutrition, University of Georgia Professor Hendrik Den Bakker analyzes how new technologies are changing how we approach food safety.
A Breakdown of Metagenomics
In “A critical review of metagenomic approaches for foodborne pathogen surveillance,” Den Bakker and his collaborators Jia Wang and Tom Denes from the University of Tennessee Knoxville reviews the current state of the field and identifies the gaps in current knowledge and methodology. Traditional methods for detecting bacteria, viruses or other germs that cause foodborne illnesses rely heavily on the ability to cultivate these organisms in the lab, which is time and resource intensive.
These analog methods can miss organisms that will not grow in traditional mediums, such as Petri dishes, or can make it difficult to identify pathogens when they are present in very low numbers.
The Future of Food Safety Surveillance
One of the new technologies available to researchers is metagenomics, which directly analyzes all the genetic material present in a sample – whether it’s from food, a processing facility or a patient.
Metagenomics maps the complete genetic snapshot of all the microbes in a sample, rather than from isolated, cultured microorganisms. This approach allows for a more comprehensive analysis of foodborne pathogens, providing a way to identify new organisms and isolate complete genomes from unculturable species that are present within an environmental sample.
The future of foodborne pathogen surveillance will be heavily influenced by advancements in metagenomics, especially with the integration of machine learning. Machine learning algorithms can rapidly process vast amounts of genetic data, improving the speed and accuracy of pathogen identification.
“Being able to retrieve genomes from pathogens directly from food, clinical samples and environmental sources (e.g., wastewater) has the potential to speed up diagnoses and surveillance tremendously,” Den Bakker said.
Metagenomics can also be used to predict bacterial behavior, such as growth and toxin production. This capability helps scientists identify specific genetic adaptations that allow bacteria to survive food processing, which can then inform better control strategies.
In the last decade, culture-independent diagnostic tests (CIDT) have been extensively used in clinical laboratories to rapidly detect pathogenic organisms in patient samples. These tests are more efficient and cost-effective than traditional laboratory-based culture tests and they can find more than one pathogen in the same sample. However, the increasing use of CIDTs dilutes public health surveillance because test results indicate only whether the sample is positive or negative for microorganisms without isolating the sources.
In response to the emergence of CIDTs the Centers for Disease Control and Prevention (CDC) is exploring the application of a metagenomic technique called Highly Multiplexed Amplicon Sequencing (HMAS). This procedure determines the order of bases for specific sections in the genome of an organism. This information can link cases together and allow an outbreak of foodborne illness to be identified and resolved sooner. The CDC hopes to implement HMAS by winter 2026.
Roadblocks in the Implementation of Metagenomics
Applying metagenomics to food safety has challenges, particularly because foodborne pathogens are often found in very small amounts in food and natural environments.
General databases used to identify microbes from metagenomic data can be incomplete or contain errors, leading to less accurate results.
Getting high-quality Metagenome-Assembled Genomes (MAGs) for low-abundance foodborne pathogens has been difficult because there isn’t enough genetic data available about them.
Testing Metagenomics Methods to Address the Gaps
To address the challenges of low-abundance pathogens, scientists are relying on two primary metagenomics techniques.
The first is using spiked samples, in which known foodborne pathogens are introduced into food products for study. This creates realistic scenarios to test how well the metagenomic workflows perform.
Additionally, scientists are creating simulated mock communities — computer-generated mixes of DNA sequences from different microbes — that are used to evaluate the accuracy of analytical tools.
Because pathogens are often scarce in samples, researchers can use quasi-metagenomics, a strategy that incorporates a short enrichment step. The sample is placed in a special liquid that encourages the target pathogens to increase in number before the DNA is sequenced.
Long-read sequencing is a newer technology used to produce longer DNA sequences. This method is better at analyzing complex genetic regions and detecting pathogens that are present in low numbers. This cost-effective practice can provide results in real-time.
Another technique is Hybrid Assembly, a technique that combines the best features of different sequencing technologies by merging the high accuracy of short-read sequencing with the long stretches of DNA obtained from long-read sequencing.
“Because we can skip the lengthy process of culturing pathogens, metagenomic approaches of pathogen surveillance will speed up the time it takes to discover and identify outbreaks,” Den Bakker said, “thus reducing both human and economic costs.”