The sources and transmission routes of microbial populations throughout a meat processing facility

  • 1.

    Buzby, J. C., Wells, H. F. & Hyman, J. The Estimated Amount, Value, and Calories of Postharvest Food Losses at the Retail and Consumer Levels in the United States. (EIB-121, U.S. Department of Agriculture, Economic Research Service, Washington, 2014).

  • 2.

    Huis In’t Veld, J. H. J. Microbial and biochemical spoilage of foods: an overview. Int. J. Food Microbiol.33, 1–18 (1996).

    Google Scholar 

  • 3.

    Havelaar, A. H. et al. World Health Organization global estimates and regional comparisons of the burden of foodborne disease in 2010. PLoS Med.12, e1001923 (2015).

    PubMed  PubMed Central  Google Scholar 

  • 4.

    EFSA (European Food Safety Authority) and ECDC (European Centre for Disease Prevention and Control), The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2015. EFSA J. 14(12): 4634, 231, (2016).

  • 5.

    Gill, C. O. Meat spoilage and evaluation of the potential storage life of fresh meat. J. Food Prot.46, 444–452 (1983).

    CAS  PubMed  Google Scholar 

  • 6.

    Giaouris, E. et al. Attachment and biofilm formation by foodborne bacteria in meat processing environments: causes, implications, role of bacterial interactions and control by alternative novel methods. Meat Sci.97, 289–309 (2014).

    Google Scholar 

  • 7.

    Choi, Y. M. et al. Changes in microbial contamination levels of porcine carcasses and fresh pork in slaughterhouses, processing lines, retail outlets, and local markets by commercial distribution. Res. Vet. Sci.94, 413–418 (2013).

    CAS  PubMed  Google Scholar 

  • 8.

    Sheridan, J. J. Sources of contamination during slaughter and measures of control. J. Food Saf.18, 321–339 (1998).

    Google Scholar 

  • 9.

    International Organization for Standardization. Microbiology of the Food ChainCarcass Sampling for Microbiological Analysis. (2015). ISO 17604:2015, Retrieved from

  • 10.

    Nocker, A., Burr, M. & Camper, A. K. Genotypic microbial community profiling: a critical technical review. Microb. Ecol.54, 276–289 (2007).

    CAS  PubMed  Google Scholar 

  • 11.

    Hultman, J., Rahkila, R., Ali, J., Rousu, J. & Björkroth, K. J. Meat processing plant microbiome and contamination patterns of cold-tolerant bacteria causing food safety and spoilage risks in the manufacture of vacuum-packaged cooked sausages. Appl. Environ. Microbiol.81, 7088–7097 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 12.

    Chaillou, S. et al. Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage. ISME J.9, 1105–1118 (2015).

    PubMed  Google Scholar 

  • 13.

    Yang, H. et al. Uncovering the composition of microbial community structure and metagenomics among three gut locations in pigs with distinct fatness. Sci. Rep.6, 27427 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 14.

    Bokulich, N. A., Bergsveinson, J., Ziola, B. & Mills, D. A. Mapping microbial ecosystems and spoilage-gene flow in breweries highlights patterns of contamination and resistance. Elife4, e04634 (2015).

    PubMed Central  Google Scholar 

  • 15.

    Mann, E. et al. Psychrophile spoilers dominate the bacterial microbiome in musculature samples of slaughter pigs. Meat Sci.117, 36–40 (2016).

    CAS  PubMed  Google Scholar 

  • 16.

    Bokulich, N. A., Lewis, Z. T., Boundy-Mills, K. & Mills, D. A. A new perspective on microbial landscapes within food production. Curr. Opin. Biotechnol.37, 182–189 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 17.

    Bridier, A. et al. Impact of cleaning and disinfection procedures on microbial ecology and Salmonella antimicrobial resistance in a pig slaughterhouse. Sci. Rep.9, 12947 (2019).

    PubMed  PubMed Central  Google Scholar 

  • 18.

    Kang, S., Ravensdale, J., Coorey, R., Dykes, G. A. & Barlow, R. A comparison of 16S rRNA profiles through slaughter in Australian export beef abattoirs. Front. Microbiol.10, 2747 (2019).

  • 19.

    Stellato, G. et al. Overlap of spoilage microbiota between meat and meat processing environment in small-scale 2 vs. large-scale retail distribution. Appl. Environ. Microbiol.82, 4045–4054 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 20.

    Campos Calero, G. et al. Deciphering resistome and virulome diversity in a porcine slaughterhouse and pork products through its production chain. Front. Microbiol.9, 2099 (2018).

  • 21.

    Johnson, J. S. et al. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nat. Commun.10, 5029 (2019).

    PubMed  PubMed Central  Google Scholar 

  • 22.

    Spescha, C., Stephan, R. & Zweifel, C. Microbiological contamination of pig carcasses at different stages of slaughter in two European Union—approved abattoirs. J. Food Prot.69, 2568–2575 (2006).

    CAS  PubMed  Google Scholar 

  • 23.

    Warriner, K., Aldsworth, T. G., Kaur, S. & Dodd, C. E. R. Cross-contamination of carcasses and equipment during pork processing. J. Appl. Microbiol.93, 169–177 (2002).

    CAS  PubMed  Google Scholar 

  • 24.

    Wheatley, P., Giotis, E. S. & McKevitt, A. I. Effects of slaughtering operations on carcass contamination in an Irish pork production plant. Ir. Vet. J.67, 1 (2014).

    PubMed  PubMed Central  Google Scholar 

  • 25.

    Gill, C. O. in Woodhead Publishing Series in Food Science, Technology and Nutrition (ed. Sofos, J. N. et al.) 630–672 (Woodhead Publishing, Sawston, 2005).

  • 26.

    de Filippis, F., La Storia, A., Villani, F. & Ercolini, D. Exploring the sources of bacterial spoilers in beefsteaks by culture-independent high-throughput sequencing. PLoS ONE8, e70222 (2013).

  • 27.

    de Smidt, O. The use of PCR-DGGE to determine bacterial fingerprints for poultry and red meat abattoir effluent. Lett. Appl. Microbiol.62, 1–8 (2016).

    PubMed  Google Scholar 

  • 28.

    Andrew, D. & Board, R. Microbiology of Meat and Poultry. (Blackie Academic & Professional, Glasgow, 1998).

  • 29.

    Khan, I. U. et al. Anoxybacillus sediminis sp. nov., a novel moderately thermophilic bacterium isolated from a hot spring. Antonie Van. Leeuwenhoek111, 2275–2282 (2018).

    PubMed  Google Scholar 

  • 30.

    Pikuta, E. et al. Anoxybacillus pushchinensis gen. nov., sp. nov., a novel anaerobic, alkaliphilic, moderately thermophilic bacterium from manure, and description of Anoxybacillus flavitherms comb. nov. Int. J. Syst. Evol. Microbiol.50, 2109–2117 (2000).

    CAS  PubMed  Google Scholar 

  • 31.

    Burgess, S. A., Lindsay, D. & Flint, S. H. Thermophilic bacilli and their importance in dairy processing. Int. J. Food Microbiol.144, 215–225 (2010).

    CAS  PubMed  Google Scholar 

  • 32.

    Burgess, S. A., Brooks, J. D., Rakonjac, J., Walker, K. M. & Flint, S. H. The formation of spores in biofilms of Anoxybacillus flavithermus. J. Appl. Microbiol.107, 1012–1018 (2009).

    CAS  PubMed  Google Scholar 

  • 33.

    Goh, K. M. et al. Recent discoveries and applications of Anoxybacillus. Appl. Microbiol. Biotechnol.97, 1475–1488 (2013).

    CAS  PubMed  Google Scholar 

  • 34.

    Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods8, 761–763 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 35.

    Henry, R. et al. Into the deep: evaluation of sourcetracker for assessment of faecal contamination of coastal waters. Water Res.93, 242–253 (2016).

    CAS  PubMed  Google Scholar 

  • 36.

    Liu, G. et al. Assessing the origin of bacteria in tap water and distribution system in an unchlorinated drinking water system by SourceTracker using microbial community fingerprints. Water Res.138, 86–96 (2018).

    CAS  PubMed  Google Scholar 

  • 37.

    Bik, H. M. et al. Microbial community patterns associated with automated teller machine keypads in New York City. mSphere1, e00226–16 (2016).

    PubMed  PubMed Central  Google Scholar 

  • 38.

    Hewitt, K. M. et al. Bacterial diversity in two neonatal intensive care units (NICUs). PLoS ONE8, e54703 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 39.

    Li, L.-G., Yin, X. & Zhang, T. Tracking antibiotic resistance gene pollution from different sources using machine-learning classification. Microbiome6, 93 (2018).

    PubMed  PubMed Central  Google Scholar 

  • 40.

    Bolton, D. J. et al. Washing and chilling as critical control points in pork slaughter hazard analysis and critical control point (HACCP) systems. J. Appl. Microbiol.92, 893–902 (2002).

  • 41.

    Yu, S. L. et al. Effect of dehairing operations on microbiological quality of swine carcasses. J. Food Prot.62, 1478–1481 (1999).

    CAS  PubMed  Google Scholar 

  • 42.

    Jagadeesan, B. et al. The use of next generation sequencing for improving food safety: translation into practice. Food Microbiol.79, 96–115 (2019).

    CAS  PubMed  Google Scholar 

  • 43.

    Bergholz, T. M., Moreno Switt, A. I. & Wiedmann, M. Omics approaches in food safety: fulfilling the promise? Trends Microbiol.22, 275–281 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 44.

    Leonard, S. R., Mammel, M. K., Lacher, D. W. & Elkins, C. A. Application of metagenomic sequencing to food safety: detection of shiga toxin-producing Escherichia coli on fresh bagged spinach. Appl. Environ. Microbiol.81, 8183–8191 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 45.

    Moura, A. et al. Real-time whole-genome sequencing for surveillance of listeria monocytogenes, France. Emerg. Infect. Dis.23, 1462–1470 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 46.

    Wang, S. et al. Food safety trends: from globalization of whole genome sequencing to application of new tools to prevent foodborne diseases. Trends Food Sci. Technol.57, 188–198 (2016).

    CAS  Google Scholar 

  • 47.

    Nastasijevic, I. et al. Tracking of listeria monocytogenes in meat establishment using whole genome sequencing as a food safety management tool: a proof of concept. Int. J. Food Microbiol.257, 157–164 (2017).

    PubMed  Google Scholar 

  • 48.

    Weimer, B. C. et al. Defining the food microbiome for authentication, safety, and process management. IBM J. Res. Dev.60, 1:1–1:13 (2016).

    Google Scholar 

  • 49.

    Köster, J. & Rahmann, S. Snakemake—a scalable bioinformatics workflow engine. Bioinformatics28, 2520–2522 (2012).

    PubMed  Google Scholar 

  • 50.

    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol.37, 852–857 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 51.

    Martijn, J. et al. Confident phylogenetic identification of uncultured prokaryotes through long read amplicon sequencing of the 16S-ITS-23S rRNA operon. Environ. Microbiol.21, 2485–2498 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 52.

    Pearce, R. A. & Bolton, D. J. Excision vs sponge swabbing—a comparison of methods for the microbiological sampling of beef, pork and lamb carcasses. J. Appl. Microbiol.98, 896–900 (2005).

    CAS  PubMed  Google Scholar 

  • 53.

    Zwirzitz, B. et al. Culture-independent evaluation of bacterial contamination patterns on pig carcasses at a commercial slaughter facility. J. Food Prot.82, 1677–1682 (2019).

    CAS  PubMed  Google Scholar 

  • 54.

    Muyzer, G., De Waal, E. C. & Uitterlinden, A. G. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol.59, 695–700 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 55.

    Stoddard, S. F., Smith, B. J., Hein, R., Roller, B. R. K. & Schmidt, T. M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res.43, D593–D598 (2015).

    CAS  PubMed  Google Scholar 

  • 56.

    Větrovský, T. & Baldrian, P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE8, 1–10 (2013).

    Google Scholar 

  • 57.

    Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res.41, 1–11 (2013).

    Google Scholar 

  • 58.

    Pacific Biosciences SMRT® Tools Reference Guide. (2018).

  • 59.

    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 60.

    Callahan, B. J. et al. High-throughput amplicon sequencing of the full-length 16S rRNA gene with single-nucleotide resolution. Nucleic Acids Res.47, e103–e103 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 61.

    Alishum, A. et al. DADA2 formatted 16S rRNA gene sequences for both bacteria & archaea. (2019).

  • 62.

    Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol.36, 996–1004 (2018).

    CAS  PubMed  Google Scholar 

  • 63.

    Davis, N. M., Proctor, D., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. bioRxiv221499, (2017).

  • 64.

    McMurdie, P. J. & Holmes, S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One8, e61217 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  • 65.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer, New York, 2016).

  • 66.

    Lindstrom, J. C. Tsnemicrobiota: T-distributed stochastic neighbor embedding for microbiota data. (2017). Github Repository,

  • 67.

    Cardoso, P., Rigal, F. & Carvalho, J. C. BAT—biodiversity Assessment Tools, an R package for the measurement and estimation of alpha and beta taxon, phylogenetic and functional diversity. Methods Ecol. Evol.6, 232–236 (2015).

    Google Scholar 

  • Source: Ecology -

    MIT research on seawater surface tension becomes international guideline

    Echolocation at high intensity imposes metabolic costs on flying bats