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. 2017 Feb 15:8:230.
doi: 10.3389/fmicb.2017.00230. eCollection 2017.

Metagenomic Sequencing for Surveillance of Food- and Waterborne Viral Diseases

Affiliations

Metagenomic Sequencing for Surveillance of Food- and Waterborne Viral Diseases

David F Nieuwenhuijse et al. Front Microbiol. .

Abstract

A plethora of viruses can be transmitted by the food- and waterborne route. However, their recognition is challenging because of the variety of viruses, heterogeneity of symptoms, the lack of awareness of clinicians, and limited surveillance efforts. Classical food- and waterborne viral disease outbreaks are mainly caused by caliciviruses, but the source of the virus is often not known and the foodborne mode of transmission is difficult to discriminate from human-to-human transmission. Atypical food- and waterborne viral disease can be caused by viruses such as hepatitis A and hepatitis E. In addition, a source of novel emerging viruses with a potential to spread via the food- and waterborne route is the repeated interaction of humans with wildlife. Wildlife-to-human adaptation may give rise to self- limiting outbreaks in some cases, but when fully adjusted to the human host can be devastating. Metagenomic sequencing has been investigated as a promising solution for surveillance purposes as it detects all viruses in a single protocol, delivers additional genomic information for outbreak tracing, and detects novel unknown viruses. Nevertheless, several issues must be addressed to apply metagenomic sequencing in surveillance. First, sample preparation is difficult since the genomic material of viruses is generally overshadowed by host- and bacterial genomes. Second, several data analysis issues hamper the efficient, robust, and automated processing of metagenomic data. Third, interpretation of metagenomic data is hard, because of the lack of general knowledge of the virome in the food chain and the environment. Further developments in virus-specific nucleic acid extraction methods, bioinformatic data processing applications, and unifying data visualization tools are needed to gain insightful surveillance knowledge from suspect food samples.

Keywords: foodborne; high-throughput sequencing; metagenomics; surveillance; viral pathogens.

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Figures

FIGURE 1
FIGURE 1
Schematic representation of the phenomenon known as the “surveillance pyramid”. Layers represent different categories of infected individuals. Width of the layers represents the estimated number of individuals in that category. As indicated, individuals reported by surveillance programs generally originate from the hospitalized category.
FIGURE 2
FIGURE 2
Food- and waterborne viral surveillance targets for metagenomic sequencing approaches. (A) Environmental surveillance of food industry, wild meat and bushmeat habitat, and aquaculture and fishery environment. (B) Food surveillance of consumer and imported foods, including illegally imported foods. (C) Surveillance of food- and waterborne outbreaks, in clinic and locally. Potential of metagenomic sequencing based surveillance is listed next to each category.

References

    1. Aarestrup F. M., Brown E. W., Detter C., Gerner-Smidt P., Gilmour M. W., Harmsen D., et al. (2012). Integrating genome-based informatics to modernize global disease monitoring, information sharing, and response. Emerg. Infect. Dis. 18:e1 10.3201/eid/1811.120453 - DOI - PMC - PubMed
    1. Afiahayati R., Sato K., Sakakibara Y., Robertson D. L., Prosperi M., Afiahayati K., et al. (2015). MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Res. 22 69–77. 10.1093/dnares/dsu041 - DOI - PMC - PubMed
    1. Afshinnekoo E., Meydan C., Chowdhury S., Jaroudi D., Boyer C., Bernstein N., et al. (2015). Geospatial resolution of human and bacterial diversity with city-scale metagenomics. Cell Syst. 1 72–87. 10.1016/j.cels.2015.01.001 - DOI - PMC - PubMed
    1. Ahmed S. M., Hall A. J., Robinson A. E., Verhoef L., Premkumar P., Parashar U. D., et al. (2014). Global prevalence of norovirus in cases of gastroenteritis: a systematic review and meta-analysis. Lancet. Infect. Dis. 14 725–730. 10.1016/S1473-3099(14)70767-4 - DOI - PMC - PubMed
    1. Alavandi S. V., Poornima M. (2012). Viral metagenomics: a tool for virus discovery and diversity in aquaculture. Indian J. Virol. 23 88–98. 10.1007/s13337-012-0075-2 - DOI - PMC - PubMed

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