Highlights

  • Getting to Know Metagenomics

  • Metagenomics Strengths and Weaknesses

  • Food Industry Applications

In our previous Next Generation Sequencing (NGS) blog, we went over the concepts of metabarcoding and metagenomics focusing on the food-related applications for metabarcoding. In this third, and final, part of our NGS blog series, we will dive deeper into understanding metagenomics and how this technology can benefit the food industry.

Metagenomics: An Overview

Metagenomics is also referred to as “shotgun metagenomics”, as it repeatedly and randomly sequences all of the DNA fragments in a sample. Both NGS technologies, short- and long-read sequencing (SRS and LRS) can be used for metagenomics, with their own pros and cons.

SRS generates short reads (<300 bp), which may make genome assembly and structural analyses challenging, but it is compensated by relatively high sequencing accuracy (>98%) and low cost per Gb ($30-1000).

Although LRS generates long reads (>1,000 bp), it comes with lower accuracy (>87%) and higher cost per Gb ($60-2000) (1). One may choose the appropriate sequencing platform by weighing the options, including the number and type of samples, budget, and study goal.

The Pros and Cons of Metagenomics

One of the advantages of metagenomics is that it can survey any community (whether it be bacterial, fungal, viral, vegetal, animals, etc.), reveal genetic functions, and map metabolic pathways from a complex sample in a single experiment. As a trade-off to the “catch ‘em all” feature, metagenomics contains a large amount of data from deeply sequencing hundreds, if not thousands of species of interest and inevitably the host genome.

In food-related NGS applications, the host genome is normally considered as the background noise to be avoided in sequencing and removed in data analysis. The host genome removal, also known as host decontamination, is another reason that metagenomics requires a large dataset (e.g., 10GB/sample), making these experiments costly. Additionally, the high demands for advanced bioinformatics skills and intensive computing power are other challenges with this technology.

Metagenomics: Applications for the Food Industry

Although whole genome sequencing (WGS) serves as a powerful tool in pathogen detection, source tracking, and surveillance, it has some limitations that may be overcome by metagenomics. For example, while WGS requires pure isolates, metagenomics may identify microorganisms even at the strain level directly from foods (2). Mechan et al. used LRS to determine which pathogen, at strain level, that caused disease in tomatoes. Moreover, with as little as 4 hours of enrichment, metagenomics can rapidly (e.g., with only 2 hours of sequencing) detect microorganisms, especially pathogens at the species level from food products (3).

Metagenomics, combined with machine learning, may also benefit the food industry by predicting problems like spoilage, based on the presence and absence of microbes or genes (4).

To achieve this, metagenomics is performed on both “normal” and “problematic” samples and machine learning is applied to analyze the data. The goal is to understand the changes in the microbial communities, as well as genetic and metabolic pathways, in order to identify certain microbes or genes that are strongly associated with the problem.

As sequencing costs continue to decrease and the amount of user-friendly tools increase, more NGS applications will be developed for the food industry.


We’ve covered a wide range of topics in our NGS blog series – from WGS and shotgun metagenomics, to spoilage investigations and machine learning. With our dedicated team of experts, Mérieux NutriSciences can help you understand how NGS can benefit your business.

 

 

References

  1. Bahassi, E.M. and Stambrook, P.J., 2014. Next-generation sequencing technologies: breaking the sound barrier of human genetics. Mutagenesis, 29(5), pp.303-310.
  2. Mechan Llontop, M.E., Sharma, P., Aguilera Flores, M., Yang, S., Pollok, J., Tian, L., Huang, C., Rideout, S., Heath, L.S., Li, S. and Vinatzer, B.A., 2020. Strain-level identification of bacterial tomato pathogens directly from metagenomic sequences. Phytopathology, pp.PHYTO-09.
  3. Ottesen, A., Ramachandran, P., Reed, E., White, J.R., Hasan, N., Subramanian, P., Ryan, G., Jarvis, K., Grim, C., Daquiqan, N. and Hanes, D., 2016. Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC microbiology, 16(1), p.275.
  4. Deneke, C., Rentzsch, R. and Renard, B.Y., 2017. PaPrBaG: A machine learning approach for the detection of novel pathogens from NGS data. Scientific reports, 7(1), pp.1-13.
  5. Farrell, F., Soyer, O.S. and Quince, C., 2018. Machine learning-based prediction of functional capabilities in metagenomically assembled microbial genomes. BioRxiv, p.307157.

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