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Identification and quantification of meat product ingredients by whole-genome metagenomics (All-Food-Seq)

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Abstract

Complex food matrices bear the risk of intentional or accidental admixture of non-declared species. Moreover, declared components can be present in false proportions, since expensive taxa might be exchanged for cheaper ones. We have previously reported that PCR-free metagenomic sequencing of total DNA extracted from sausage samples combined with bioinformatic analysis (termed All-Food-Seq, AFS) can be a valuable screening tool to identify the taxon composition of food ingredients. Here, we illustrate this principle by analysing regional Doner kebap samples, which revealed unexpected and unlabelled poultry and plant components in three of five cases. In addition, we systematically apply AFS to a broad set of reference meat material of known composition (i.e. reference sausages) to evaluate quantification accuracy and potential limitations. We include a detailed analysis of the effect of different food matrices and the possibility of false-positive sequence read assignment to closely related species, and we compare AFS quantification results to quantitative real-time PCR (qPCR) and droplet digital PCR (ddPCR). AFS emerges as a potent PCR-free screening tool, which can detect multiple target species of different kingdoms of life within a single assay. Mathematical calibration accounting for pronounced matrix effects can significantly improve AFS quantification accuracy. In comparison, AFS performs better than classical qPCR, and is on par with ddPCR.

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References

  1. German Federal Office for Risk Assessment: Food safety and globalisation—challenges and opportunities (Bundesamt für Risikobewertung) (2019) https://www.bfr.bund.de/en/press_information/2014/13/food_safety_and_globalisation___challenges_and_opportunities-190341.html. Accessed 28 Aug 2019

  2. German Federal Office of Consumer Protection and Food Safety (Bundesamt für Verbraucherschutz und Lebensmittelsicherheit) (2019) https://www.bvl.bund.de/DE/Home/homepage_node.html. Accessed 28 Aug 2019

  3. German Drug Law (Bundesministerium der Justiz und für Verbraucherschutz: Gesetz über den Verkehr mit Arzneimitteln) (2019) https://www.gesetze-im-internet.de/amg_1976/AMG.pdf. Accessed 28 Aug 2019

  4. Swiss Food Legislation (Schweizerisches Bundesgesetz über Lebensmittel und Gebrauchsgegenstände (Lebensmittelgesetz, LMG) vom 20 (2014) https://www.admin.ch/opc/de/official-compilation/2017/249.pdf. Accessed 28 Aug 2019

  5. Brodmann PD, Moor D (2003) Sensitive and semi-quantitative TaqManTM real-time polymerase chain reaction systems for the detection of beef (Bos taurus) and the detection of the family Mammalia in food and feed. Meat Sci 65:599–607. https://doi.org/10.1016/S0309-1740(02)00253-X

    Article  CAS  PubMed  Google Scholar 

  6. Zhang C-L, Fowler MR, Scott NW et al (2007) A TaqMan real-time PCR system for the identification and quantification of bovine DNA in meats, milks and cheeses. Food Control 18:1149–1158. https://doi.org/10.1016/J.FOODCONT.2006.07.018

    Article  CAS  Google Scholar 

  7. Köppel R, Ruf J, Zimmerli F, Breitenmoser A (2008) Multiplex real-time PCR for the detection and quantification of DNA from beef, pork, chicken and turkey. Eur Food Res Technol 227:1199–1203. https://doi.org/10.1007/s00217-008-0837-7

    Article  CAS  Google Scholar 

  8. Köppel R, Ruf J, Rentsch J (2011) Multiplex real-time PCR for the detection and quantification of DNA from beef, pork, horse and sheep. Eur Food Res Technol 232:151–155. https://doi.org/10.1007/s00217-010-1371-y

    Article  CAS  Google Scholar 

  9. Köppel R, Eugster A, Ruf J, Rentsch J (2012) Quantification of meat proportions by measuring DNA contents in raw and boiled sausages using matrix-adapted calibrators and multiplex real-time PCR. J AOAC Int 95:494–499. https://doi.org/10.5740/jaoacint.11-115

    Article  CAS  PubMed  Google Scholar 

  10. Ulca P, Balta H, Çağın İ, Senyuva HZ (2013) Meat species identification and Halal authentication using PCR analysis of raw and cooked traditional Turkish foods. Meat Sci 94:280–284. https://doi.org/10.1016/j.meatsci.2013.03.008

    Article  CAS  PubMed  Google Scholar 

  11. Floren C, Wiedemann I, Brenig B et al (2015) Species identification and quantification in meat and meat products using droplet digital PCR (ddPCR). Food Chem 173:1054–1058. https://doi.org/10.1016/j.foodchem.2014.10.138

    Article  CAS  PubMed  Google Scholar 

  12. Song K-Y, Hwang HJ, Kim JH (2017) Ultra-fast DNA-based multiplex convection PCR method for meat species identification with possible on-site applications. Food Chem 229:341–346. https://doi.org/10.1016/j.foodchem.2017.02.085

    Article  CAS  PubMed  Google Scholar 

  13. Köppel R, Ganeshan A, Weber S et al (2019) Duplex digital PCR for the determination of meat proportions of sausages containing meat from chicken, turkey, horse, cow, pig and sheep. Eur Food Res Technol 245:853–862. https://doi.org/10.1007/s00217-018-3220-3

    Article  CAS  Google Scholar 

  14. Markoulatos P, Siafakas N, Moncany M (2002) Multiplex polymerase chain reaction: a practical approach. J Clin Lab Anal 16:47–51. https://doi.org/10.1002/jcla.2058

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Berry D, Ben Mahfoudh K, Wagner M, Loy A (2011) Barcoded primers used in multiplex amplicon pyrosequencing bias amplification. Appl Environ Microbiol 77:7846–7849. https://doi.org/10.1128/AEM.05220-11

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Tedersoo L, Anslan S, Bahram M et al (2015) Shotgun metagenomes and multiple primer pair-barcode combinations of amplicons reveal biases in metabarcoding analyses of fungi. MycoKeys 10:1–43. https://doi.org/10.3897/mycokeys.10.4852

    Article  Google Scholar 

  17. Sze MA, Schloss PD (2019) The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data. mSphere 4:e00163–19. https://doi.org/10.1128/msphere.00163-19

  18. Ripp F, Krombholz C, Liu Y et al (2014) All-Food-Seq (AFS): a quantifiable screen for species in biological samples by deep DNA sequencing. BMC Genomics 15:639. https://doi.org/10.1186/1471-2164-15-639

    Article  PubMed  PubMed Central  Google Scholar 

  19. Liu Y, Ripp F, Koeppel R et al (2017) AFS: identification and quantification of species composition by metagenomic sequencing. Bioinformatics 33:btw822. https://doi.org/10.1093/bioinformatics/btw822

    Article  CAS  Google Scholar 

  20. Eugster A, Ruf J, Rentsch J, Köppel R (2009) Quantification of beef, pork, chicken and turkey proportions in sausages: use of matrix-adapted standards and comparison of single versus multiplex PCR in an interlaboratory trial. Eur Food Res Technol 230:55–61. https://doi.org/10.1007/s00217-009-1138-5

    Article  CAS  Google Scholar 

  21. FASTQC (2019) A quality control tool for high throughput sequence data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 28 Aug 2019

  22. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30:2114–2120. https://doi.org/10.1093/bioinformatics/btu170

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. BBTools (2019) A suite of fast, multithreaded bioinformatics tools designed for analysis of DNA and RNA sequence data. https://sourceforge.net/projects/bbmap/. Accessed 28 Aug 2019

  24. Kumar S, Stecher G, Suleski M, Hedges SB (2017) TimeTree: a resource for timelines, timetrees, and divergence times. Mol Biol Evol 34:1812–1819. https://doi.org/10.1093/molbev/msx116

    Article  CAS  PubMed  Google Scholar 

  25. Armbruster DA, Pry T (2008) Limit of blank, limit of detection and limit of quantitation. Clin Biochem Rev 29(Suppl 1):S49–S52

    PubMed  PubMed Central  Google Scholar 

  26. Marking of “doner kebab” and “similar” products by bulk delivery (2019) (Kenntlichmachung von „Döner Kebab“und „ähnlichen“Erzeugnissen bei loser Abgabe. Bayerisches Landesamt für Gesundheit und Lebensmittelsicherheit). https://www.lgl.bayern.de/downloads/lebensmittel/doc/merkblatt_doener_kebab.pdf. Accessed 28 Aug 2019

  27. The composition and labelling of doner kebabs (2019) LACORS. https://www.ihsti.com/lacors/ContentDetails.aspx?id=21001. Accessed 28 Aug 2019

  28. Frequently minced meat and additives in doner kebab (2019) (Häufig Fleischbrät und Zusatzstoffe im Döner. Norddeutscher Rundfunk). https://www.ndr.de/ratgeber/verbraucher/Haeufig-Fleischbraet-und-Zusatzstoffe-im-Doener,doener164.html. Accessed 28 Aug 2019

  29. Cai Y, Li X, Lv R et al (2014) Quantitative analysis of pork and chicken products by droplet digital PCR. Biomed Res Int 2014:810209. https://doi.org/10.1155/2014/810209

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

TH and SLH gratefully acknowledge funding by the Federal Office for Agriculture and Food (project ID: 2816503814), Johannes Gutenberg University Center for Computational Sciences (CSM) and the Ministry of Justice and for Consumer Safety Rhineland-Palatinate.

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Author notes

  1. Fabian Ripp

    Present address: MVZ Labor Volkmann, Karlsruhe, Germany

  2. Sören Lukas Hellmann and Fabian Ripp equal contribution.

Authors and Affiliations

  1. Institute of Organismic and Molecular Evolution, Molecular Genetics and Genome Analysis, Johannes Gutenberg University Mainz, J. J. Becher-Weg 30A, 55099, Mainz, Germany

    Sören Lukas Hellmann, Fabian Ripp & Thomas Hankeln

  2. StarSEQ GmbH, Mainz, Germany

    Sven-Ernö Bikar

  3. Institute of Informatics, Johannes Gutenberg University Mainz, Mainz, Germany

    Bertil Schmidt

  4. Official Food Control Authority of the Canton Zurich, Zurich, Switzerland

    Rene Köppel

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  1. Sören Lukas Hellmann
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  2. Fabian Ripp
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Correspondence to Thomas Hankeln.

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Hellmann, S.L., Ripp, F., Bikar, SE. et al. Identification and quantification of meat product ingredients by whole-genome metagenomics (All-Food-Seq). Eur Food Res Technol 246, 193–200 (2020). https://doi.org/10.1007/s00217-019-03404-y

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  • DOI: https://doi.org/10.1007/s00217-019-03404-y

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