DNA sequencing can be used to determine the names, types, and proportions of microorganisms, the component species in a food sample, and track foodborne disease agents. Here we introduce a column exploring aspects and applications of these new techniques, known collectively as food genomics. Each month we will provide take-home knowledge in which every food safety scientist should be familiar.
We live in an exciting time of great change in all of biological and food sciences. In fact, it is not an overstatement to claim that a large portion of the fields of food science, biology, agriculture and medicine will be reformed in what has been called the post-genomics era or simply the genomics era. Food science and food microbiology are major players in this pack and moving in the fast track of these changes. This game-changing technology is fueled by the convergence of two rapidly evolving fields: DNA sequencing and the analysis of that sequencing data (i.e., bioinformatics).
The common jargon uses the acronym NGS for Next Generation Sequencing. NGS refers to the most updated automated DNA sequencing technology available. In several ways, sequencing can be considered a commodity service; hence its price has dropped and its availability is now widespread. What does this mean? A useful analogy is the following: Think of trying to publish a book you wrote. Would you go out, buy a printing press, paper, ink, binding machinery, and produce thousands of copies of your book, or, would you go to a professional printer and get them to print and manufacture copies? For most, the simplicity and experience of the professional print master trumps the do-it-yourself route. Once sequence data is obtained, what is next in the process of using that data? Analysis of sequence data is a specialized field called bioinformatics and has its own expert practitioners. It is a field of study that is a hybrid combination of mathematics, statistics, computer science, and biology. Bioinformatics analyzes the very large datasets produced by NGS and will be increasingly dependent on the internet cloud for its utility to be fully realized.
How will food genomics impact food safety and quality? How will it help in identifying the sources of outbreaks in a fraction of the time it once took? What will this mean for zero-tolerance, for pathogen control, and for responsibilities and liabilities of food producers and processors? There is a growing body of examples and literature that begins to apply genomics and microbiomics to the quality of food and sources of its microbial populations.5-7
Over the course of this column, we will be exploring several examples to alert the reader to the myriad of uses of genomics for solving food production issues.
Genomics (NGS and Bioinformatics) are the basis of the US-FDA GenomeTrakr program.1 Genomics offers an alternative means to serotype Salmonella isolates using DNA sequencing.2 There are several examples of using sequencing of solving the epidemiological source of foodborne microbial outbreaks by comparing the entire bacterial genomes of clinical and food isolates.3,4
One powerful application of genomics is to conduct the census of microbial communities to identify the microbial members and their relative proportions, an outcome called a microbiome, all from a single tube! The technique itself is termed microbiomics. Just think, we can now identify all bacteria in a complex mixture without isolating what will grow, as well as the many microorganisms we have not yet learned to culture or require unusual temperatures, nutrients, and atmospheres! Can you feel the excitement? Hopefully with knowledge of the power of food genomics you will begin to see the true utility of this technology and begin to appreciate its awesome power. Most importantly, you will begin to see how food genomics is a useful tool for the food science professional.
The microbiome field is changing as of this writing and moving toward using a technique known as whole shotgun metagenome (WSM) analysis in which all of the DNA in a sample is sequenced and not just bacterial, fungal, or specific genes; i.e., a metagenome approach vs. a targeted approach to determining the microbiome of a sample.8,9 The whole genome shotgun approach is also a powerful tool not only for creating food microbiomes, but can help in the identification of the plant and animal species used as ingredients in foods. WSM requires relatively advanced and sophisticated bioinformatics tools and at the same time sequencing chemistry is advancing, so is bioinformatics. For example, there is an online tool suite known as NEPHELE, which offers publically available online programs, software, and data handling capacity for sequence analysis.10,11
So here we are with some brand new shiny tools in the kit. Now the question is, how can the food safety professional begin to use these tools? More to the point is to understand when food genomic data is called for. The first step is to grasp some of the terminology and basic processes. Table 1 lists a few starter terms to become familiar with as well as some web resources that might be helpful to you in understanding these immensely powerful tools.12,13
|Table 1. Starter Terms in Food Genomics|
|High-quality, low-error, gap-free DNA sequence of an entire genome of an organism, in this case, an isolated bacterium, indicating genes and their locations. This can be considered a complete road map of an organism’s genetic makeup as expressed in the nucleotides Adenine, Thymine, Cytosine, and Guanine (ATCG’s). Can be referred to as WGS or Whole Genome Sequencing.|
|Bioinformatics||The science of managing and analyzing biological data using advanced computing techniques. Especially important in analyzing genomic research data.|
|Metagenomes or Whole Shotgun Sequencing||Sequences of Genetic material recovered directly from food, animal, plant, or environmental samples with no foreknowledge of the source of living materials therein. For instance, the metagenome of a yogurt sample will harbor DNA sequences characteristic of starter culture bacteria and bovine DNA (assuming it is bovine milk yogurt). This is another approach to obtaining a microbiome.6|
|Microbiome||A community of microorganisms that inhabit a particular environment or sample. For example, a plant microbiome includes all the microorganisms that colonize a plant’s surfaces and internal passages. This can be a Targeted (Amplicon Sequencing Based) or a Metagenome (Whole Shotgun Metagenome based) microbiome.6|
|Microbiomics||The process of determining a microbiome.|
|Microbiota||The ecological community of commensal, symbiotic, and pathogenic microorganisms that literally share a space or are within a sample. Formerly the term ‘microflora’ was used, but this term is waning in usage.14|
|NGS (Next Generation Sequencing)||High throughput automated sequencing of nucleic acids DNA or RNA.|
Finally, in the reference section we have tried to provide you with some useful online reference sources. The U.S. Department of Energy has perhaps the most intuitive, user-friendly and informative sites we have encountered as of late (“Genome Glossary,” 2016). The same source also published a talking glossary (“Talking Glossary of Genetic Terms,” 2016). The reader should be advised that genomic terminology and nomenclature is still not fully mature. In fact, the number of vague meanings, cross references, and acronyms can sometimes be frustrating; but fear not, as one reads and discusses the terms, they will become clearer. As a start we recommend downloading a helpful reference that follows.15 There are many other sites you will locate by performing a single web-search. If you would like to share your favorite genomics sites, please drop a line to either author and we will try to compile them into a single electronic document.
We hope this first column will find you coming back for more as we explore this burgeoning field and learn how it is being linked to food safety. Look for future articles on specific food applications, methods, and hot topics in food genomics. Goodbye for now.
- Allard, M. W., Strain, E., Melka, D., Bunning, K., Musser, S. M., Brown, E. W., & Timme, R. (2016). Practical Value of Food Pathogen Traceability through Building a Whole-Genome Sequencing Network and Database. Journal of Clinical Microbiology, 54(8), 1975–1983. https://doi.org/10.1128/JCM.00081-16
- Zhang, S., Yin, Y., Jones, M. B., Zhang, Z., Deatherage Kaiser, B. L., Dinsmore, B. A., … Deng, X. (2015). Salmonella serotype determination utilizing high-throughput genome sequencing data. Journal of Clinical Microbiology, 53(5), 1685–1692. https://doi.org/10.1128/JCM.00323-15
- Burall, L. S., Grim, C. J., Mammel, M. K., & Datta, A. R. (2016). Whole Genome Sequence Analysis Using JSpecies Tool Establishes Clonal Relationships between Listeria monocytogenes Strains from Epidemiologically Unrelated Listeriosis Outbreaks. PloS One, 11(3), e0150797. https://doi.org/10.1371/journal.pone.0150797
- Chen, Y., Burall, L. S., Luo, Y., Timme, R., Melka, D., Muruvanda, T., Brown, E. W. (2016). Isolation, enumeration and whole genome sequencing of Listeria monocytogenes in stone fruits linked to a multistate outbreak. Applied and Environmental Microbiology. https://doi.org/10.1128/AEM.01486-16
- Bokulich, N. A., Lewis, Z. T., Boundy-Mills, K., & Mills, D. A. (2016). A new perspective on microbial landscapes within food production. Current Opinion in Biotechnology, 37, 182–189. https://doi.org/10.1016/j.copbio.2015.12.008
- Bokulich, N. A., & Mills, D. A. (2012). Next-generation approaches to the microbial ecology of food fermentations. BMB Reports, 45(7), 377–389.
- Zarraonaindia, I., Owens, S. M., Weisenhorn, P., West, K., Hampton-Marcell, J., Lax, S., … Gilbert, J. A. (2015). The soil microbiome influences grapevine-associated microbiota. mBio, 6(2). https://doi.org/10.1128/mBio.02527-14
- Microbial Foods – The Science Of Fermented Foods. (n.d.). Retrieved November 21, 2016, from http://microbialfoods.org/
- Ranjan, R., Rani, A., Metwally, A., McGee, H. S., & Perkins, D. L. (2016). Analysis of the microbiome: Advantages of whole genome shotgun versus 16S amplicon sequencing. Biochemical and Biophysical Research Communications, 469(4), 967–977. https://doi.org/10.1016/j.bbrc.2015.12.083
- Colosimo, M. E., Peterson, M. W., Mardis, S., & Hirschman, L. (2011). Nephele: genotyping via complete composition vectors and MapReduce. Source Code for Biology and Medicine, 6, 13. https://doi.org/10.1186/1751-0473-6-13
- Weber, N. (n.d.). Cloud Computing for Scientific Research The NIH Nephele Project for Microbiome Analysis. Accessed November 21, 2016. Retrieved from https://www.google.com/url?q=http://casc.org/meetings/14sep/CASC-NIH-Microbiome-Cloud-Project-20140917.pdf&sa=U&ved=0ahUKEwj2mvTt1LrQAhXMC8AKHUiCBLsQFggHMAE&client=internal-uds-cse&usg=AFQjCNGt_lx2zw4qLNcHuYwZvSg10ivp5Aabout:blank
- Genome Glossary. (n.d.). Accessed November 21, 2016. Retrieved from http://doegenomestolife.org/glossary/index.shtml
- Talking Glossary of Genetic Terms. (n.d.). Accessed November 21, 2016. Retrieved from https://www.genome.gov/glossary/
- Microbiota. (2016, November 14). In Wikipedia. Retrieved from https://en.wikipedia.org/w/index.php?title=Microbiota&oldid=749454552
- Marchesi, J. R., & Ravel, J. (2015). The vocabulary of microbiome research: a proposal. Microbiome, 3, 31. https://doi.org/10.1186/s40168-015-0094-5
Nutrition, C. for F. S. and A. (n.d.). Whole Genome Sequencing (WGS) Program – GenomeTrakr Network [WebContent]. Accessed November 21, 2016. Retrieved from http://www.fda.gov/Food/FoodScienceResearch/WholeGenomeSequencingProgramWGS/ucm363134.htm