Skip to main content

Exploring Foodborne Pathogen Ecology and Antimicrobial Resistance in the Light of Shotgun Metagenomics

  • Protocol
  • First Online:
Foodborne Bacterial Pathogens

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1918))

Abstract

In this chapter, applications of shotgun metagenomics for taxonomic profiling and functional investigation of food microbial communities with a focus on antimicrobial resistance (AMR) were overviewed in the light of last data in the field. Potentialities of metagenomic approach, along with the challenges encountered for a wider and routinely use in food safety was discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Vayssier-Taussat M, Albina E, Citti C, Cosson JF, Jacques MA, Lebrun MH, Le Loir Y, Ogliastro M, Petit MA, Roumagnac P, Candresse T (2014) Shifting the paradigm from pathogens to pathobiome: new concepts in the light of meta-omics. Front Cell Infect Microbiol 4:29. https://doi.org/10.3389/fcimb.2014.00029

    Article  PubMed  PubMed Central  Google Scholar 

  2. Bridier A, Sanchez-Vizuete P, Guilbaud M, Piard JC, Naitali M, Briandet R (2015) Biofilm-associated persistence of food-borne pathogens. Food Microbiol 45(Pt B):167–178. https://doi.org/10.1016/j.fm.2014.04.015

    Article  CAS  PubMed  Google Scholar 

  3. Bridier A, Briandet R, Thomas V, Dubois-Brissonnet F (2011) Resistance of bacterial biofilms to disinfectants: a review. Biofouling 27(9):1017–1032. https://doi.org/10.1080/08927014.2011.626899

    Article  CAS  PubMed  Google Scholar 

  4. Bridier A, Dubois-Brissonnet F, Greub G, Thomas V, Briandet R (2011) Dynamics of the action of biocides in Pseudomonas aeruginosa biofilms. Antimicrob Agents Chemother 55(6):2648–2654. https://doi.org/10.1128/AAC.01760-10

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Giaouris E, Heir E, Desvaux M, Hebraud M, Moretro T, Langsrud S, Doulgeraki A, Nychas GJ, Kacaniova M, Czaczyk K, Olmez H, Simoes M (2015) Intra- and inter-species interactions within biofilms of important foodborne bacterial pathogens. Front Microbiol 6:841. https://doi.org/10.3389/fmicb.2015.00841

    Article  PubMed  PubMed Central  Google Scholar 

  6. Roder HL, Raghupathi PK, Herschend J, Brejnrod A, Knochel S, Sorensen SJ, Burmolle M (2015) Interspecies interactions result in enhanced biofilm formation by co-cultures of bacteria isolated from a food processing environment. Food Microbiol 51:18–24. https://doi.org/10.1016/j.fm.2015.04.008

    Article  CAS  PubMed  Google Scholar 

  7. Sanchez-Vizuete P, Orgaz B, Aymerich S, Le Coq D, Briandet R (2015) Pathogens protection against the action of disinfectants in multispecies biofilms. Front Microbiol 6:705. https://doi.org/10.3389/fmicb.2015.00705

    Article  PubMed  PubMed Central  Google Scholar 

  8. van der Veen S, Abee T (2011) Mixed species biofilms of Listeria monocytogenes and lactobacillus plantarum show enhanced resistance to benzalkonium chloride and peracetic acid. Int J Food Microbiol 144(3):421–431. https://doi.org/10.1016/j.ijfoodmicro.2010.10.029

    Article  CAS  PubMed  Google Scholar 

  9. Jahid IK, Han N, Zhang CY, Ha SD (2015) Mixed culture biofilms of Salmonella Typhimurium and cultivable indigenous microorganisms on lettuce show enhanced resistance of their sessile cells to cold oxygen plasma. Food Microbiol 46:383–394. https://doi.org/10.1016/j.fm.2014.08.003

    Article  CAS  PubMed  Google Scholar 

  10. Bridier A, Sanchez-Vizuete Mdel P, Le Coq D, Aymerich S, Meylheuc T, Maillard JY, Thomas V, Dubois-Brissonnet F, Briandet R (2012) Biofilms of a Bacillus subtilis hospital isolate protect Staphylococcus aureus from biocide action. PLoS One 7(9):e44506. https://doi.org/10.1371/journal.pone.0044506

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Habimana O, Guillier L, Kulakauskas S, Briandet R (2011) Spatial competition with Lactococcus lactis in mixed-species continuous-flow biofilms inhibits Listeria monocytogenes growth. Biofouling 27(9):1065–1072. https://doi.org/10.1080/08927014.2011.626124

    Article  PubMed  Google Scholar 

  12. Gomez NC, Ramiro JM, Quecan BX, de Melo Franco BD (2016) Use of potential probiotic lactic acid Bacteria (LAB) biofilms for the control of Listeria monocytogenes, Salmonella Typhimurium, and Escherichia coli O157:H7 biofilms formation. Front Microbiol 7:863. https://doi.org/10.3389/fmicb.2016.00863

    Article  PubMed  PubMed Central  Google Scholar 

  13. Møretrø T, Langsrud S (2017) Residential Bacteria on surfaces in the food industry and their implications for food safety and quality. Compr Rev Food Sci Food Saf 16(5):1022–1041. https://doi.org/10.1111/1541-4337.12283

    Article  CAS  PubMed  Google Scholar 

  14. Mayo B, Rachid CT, Alegria A, Leite AM, Peixoto RS, Delgado S (2014) Impact of next generation sequencing techniques in food microbiology. Curr Genomics 15(4):293–309. https://doi.org/10.2174/1389202915666140616233211

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Moran-Gilad J (2017) Whole genome sequencing (WGS) for food-borne pathogen surveillance and control—taking the pulse. Euro Surveill 22(23). https://doi.org/10.2807/1560-7917.ES.2017.22.23.30547

  16. Ronholm J, Nasheri N, Petronella N, Pagotto F (2016) Navigating microbiological food safety in the era of whole-genome sequencing. Clin Microbiol Rev 29(4):837–857. https://doi.org/10.1128/CMR.00056-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kwong JC, McCallum N, Sintchenko V, Howden BP (2015) Whole genome sequencing in clinical and public health microbiology. Pathology 47(3):199–210. https://doi.org/10.1097/PAT.0000000000000235

    Article  CAS  PubMed  Google Scholar 

  18. Sekse C, Holst-Jensen A, Dobrindt U, Johannessen GS, Li W, Spilsberg B, Shi J (2017) High throughput sequencing for detection of foodborne pathogens. Front Microbiol 8:2029. https://doi.org/10.3389/fmicb.2017.02029

    Article  PubMed  PubMed Central  Google Scholar 

  19. Sharpton TJ (2014) An introduction to the analysis of shotgun metagenomic data. Front Plant Sci 5:209. https://doi.org/10.3389/fpls.2014.00209

    Article  PubMed  PubMed Central  Google Scholar 

  20. Escobar-Zepeda A, Vera-Ponce de Leon A, Sanchez-Flores A (2015) The road to metagenomics: from microbiology to DNA sequencing technologies and bioinformatics. Front Genet 6:348. https://doi.org/10.3389/fgene.2015.00348

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Roumpeka DD, Wallace RJ, Escalettes F, Fotheringham I, Watson M (2017) A review of bioinformatics tools for bio-prospecting from metagenomic sequence data. Front Genet 8:23. https://doi.org/10.3389/fgene.2017.00023

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Doyle CJ, O’Toole PW, Cotter PD (2017) Metagenome-based surveillance and diagnostic approaches to studying the microbial ecology of food production and processing environments. Environ Microbiol 19(11):4382–4391. https://doi.org/10.1111/1462-2920.13859

    Article  PubMed  Google Scholar 

  23. Andersen SC, Hoorfar J (2018) Surveillance of foodborne pathogens: towards diagnostic metagenomics of fecal samples. Genes (Basel) 9(1). https://doi.org/10.3390/genes9010014

  24. Forbes JD, Knox NC, Ronholm J, Pagotto F, Reimer A (2017) Metagenomics: the next culture-independent game changer. Front Microbiol 8:1069. https://doi.org/10.3389/fmicb.2017.01069

    Article  PubMed  PubMed Central  Google Scholar 

  25. Franzosa EA, Hsu T, Sirota-Madi A, Shafquat A, Abu-Ali G, Morgan XC, Huttenhower C (2015) Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nat Rev Microbiol 13(6):360–372. https://doi.org/10.1038/nrmicro3451

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Fantini E, Gianese G, Giuliano G, Fiore A (2015) Bacterial metabarcoding by 16S rRNA gene ion torrent amplicon sequencing. Methods Mol Biol 1231:77–90. https://doi.org/10.1007/978-1-4939-1720-4_5

    Article  CAS  PubMed  Google Scholar 

  27. Marchesi JR, Ravel J (2015) The vocabulary of microbiome research: a proposal. Microbiome 3:31. https://doi.org/10.1186/s40168-015-0094-5

    Article  PubMed  PubMed Central  Google Scholar 

  28. Esposito A, Kirschberg M (2014) How many 16S-based studies should be included in a metagenomic conference? It may be a matter of etymology. FEMS Microbiol Lett 351(2):145–146. https://doi.org/10.1111/1574-6968.12375

    Article  CAS  PubMed  Google Scholar 

  29. Gulitz A, Stadie J, Ehrmann MA, Ludwig W, Vogel RF (2013) Comparative phylobiomic analysis of the bacterial community of water kefir by 16S rRNA gene amplicon sequencing and ARDRA analysis. J Appl Microbiol 114(4):1082–1091. https://doi.org/10.1111/jam.12124

    Article  CAS  PubMed  Google Scholar 

  30. Kim D, Hong S, Kim YT, Ryu S, Kim HB, Lee JH (2017) Metagenomic approach to identifying foodborne pathogens on Chinese cabbage. J Microbiol Biotechnol. https://doi.org/10.4014/jmb.1710.10021

    Article  PubMed  Google Scholar 

  31. Leff JW, Fierer N (2013) Bacterial communities associated with the surfaces of fresh fruits and vegetables. PLoS One 8(3):e59310. https://doi.org/10.1371/journal.pone.0059310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Ganda EK, Bisinotto RS, Lima SF, Kronauer K, Decter DH, Oikonomou G, Schukken YH, Bicalho RC (2016) Longitudinal metagenomic profiling of bovine milk to assess the impact of intramammary treatment using a third-generation cephalosporin. Sci Rep 6:37565. https://doi.org/10.1038/srep37565

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Riquelme C, Camara S, Dapkevicius Mde L, Vinuesa P, da Silva CC, Malcata FX, Rego OA (2015) Characterization of the bacterial biodiversity in Pico cheese (an artisanal Azorean food). Int J Food Microbiol 192:86–94. https://doi.org/10.1016/j.ijfoodmicro.2014.09.031

    Article  CAS  PubMed  Google Scholar 

  34. Stellato G, La Storia A, De Filippis F, Borriello G, Villani F, Ercolini D (2016) Overlap of spoilage-associated microbiota between meat and the meat processing environment in small-scale and large-scale retail distributions. Appl Environ Microbiol 82(13):4045–4054. https://doi.org/10.1128/AEM.00793-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Chaillou S, Chaulot-Talmon A, Caekebeke H, Cardinal M, Christieans S, Denis C, Desmonts MH, Dousset X, Feurer C, Hamon E, Joffraud JJ, La Carbona S, Leroi F, Leroy S, Lorre S, Mace S, Pilet MF, Prevost H, Rivollier M, Roux D, Talon R, Zagorec M, Champomier-Verges MC (2015) Origin and ecological selection of core and food-specific bacterial communities associated with meat and seafood spoilage. ISME J 9(5):1105–1118. https://doi.org/10.1038/ismej.2014.202

    Article  PubMed  Google Scholar 

  36. Giusti A, Armani A, Sotelo CG (2017) Advances in the analysis of complex food matrices: species identification in surimi-based products using next generation sequencing technologies. PLoS One 12(10):e0185586. https://doi.org/10.1371/journal.pone.0185586

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Ferrocino I, Cocolin L (2017) Current perspectives in food-based studies exploiting multi-omics approaches. Curr Opin Food Sci 13:10–15. https://doi.org/10.1016/j.cofs.2017.01.002

    Article  Google Scholar 

  38. de Boer P, Caspers M, Sanders JW, Kemperman R, Wijman J, Lommerse G, Roeselers G, Montijn R, Abee T, Kort R (2015) Amplicon sequencing for the quantification of spoilage microbiota in complex foods including bacterial spores. Microbiome 3:30. https://doi.org/10.1186/s40168-015-0096-3

    Article  PubMed  PubMed Central  Google Scholar 

  39. Kovac J, Hd B, Carroll LM, Wiedmann M (2017) Precision food safety: a systems approach to food safety facilitated by genomics tools. TrAC Trends Anal Chem 96:52–61. https://doi.org/10.1016/j.trac.2017.06.001

    Article  CAS  Google Scholar 

  40. Ceuppens S, De Coninck D, Bottledoorn N, Van Nieuwerburgh F, Uyttendaele M (2017) Microbial community profiling of fresh basil and pitfalls in taxonomic assignment of enterobacterial pathogenic species based upon 16S rRNA amplicon sequencing. Int J Food Microbiol 257:148–156. https://doi.org/10.1016/j.ijfoodmicro.2017.06.016

    Article  CAS  PubMed  Google Scholar 

  41. Poretsky R, Rodriguez RL, Luo C, Tsementzi D, Konstantinidis KT (2014) Strengths and limitations of 16S rRNA gene amplicon sequencing in revealing temporal microbial community dynamics. PLoS One 9(4):e93827. https://doi.org/10.1371/journal.pone.0093827

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Shah N, Tang H, Doak TG, Ye Y (2011) Comparing bacterial communities inferred from 16S rRNA gene sequencing and shotgun metagenomics. Pac Symp Biocomput:165–176

    Google Scholar 

  43. Ranjan R, Rani A, Metwally A, McGee HS, Perkins DL (2016) Analysis of the microbiome: advantages of whole genome shotgun versus 16S amplicon sequencing. Biochem Biophys Res Commun 469(4):967–977. https://doi.org/10.1016/j.bbrc.2015.12.083

    Article  CAS  PubMed  Google Scholar 

  44. Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S, Mitchel T, Perry T, Kao D, Mason AL, Madsen KL, Wong GK (2016) Characterization of the gut microbiome using 16S or shotgun metagenomics. Front Microbiol 7:459. https://doi.org/10.3389/fmicb.2016.00459

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tessler M, Neumann JS, Afshinnekoo E, Pineda M, Hersch R, Velho LFM, Segovia BT, Lansac-Toha FA, Lemke M, DeSalle R, Mason CE, Brugler MR (2017) Large-scale differences in microbial biodiversity discovery between 16S amplicon and shotgun sequencing. Sci Rep 7(1):6589. https://doi.org/10.1038/s41598-017-06665-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C (2012) Metagenomic microbial community profiling using unique clade-specific marker genes. Nat Methods 9(8):811–814. https://doi.org/10.1038/nmeth.2066

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Zolfo M, Tett A, Jousson O, Donati C, Segata N (2017) MetaMLST: multi-locus strain-level bacterial typing from metagenomic samples. Nucleic Acids Res 45(2):e7. https://doi.org/10.1093/nar/gkw837

    Article  CAS  PubMed  Google Scholar 

  48. Tu Q, He Z, Zhou J (2014) Strain/species identification in metagenomes using genome-specific markers. Nucleic Acids Res 42(8):e67. https://doi.org/10.1093/nar/gku138

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Roosaare M, Vaher M, Kaplinski L, Mols M, Andreson R, Lepamets M, Koressaar T, Naaber P, Koljalg S, Remm M (2017) StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees. PeerJ 5:e3353. https://doi.org/10.7717/peerj.3353

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wood DE, Salzberg SL (2014) Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15(3):R46. https://doi.org/10.1186/gb-2014-15-3-r46

    Article  PubMed  PubMed Central  Google Scholar 

  51. Luo C, Knight R, Siljander H, Knip M, Xavier RJ, Gevers D (2015) ConStrains identifies microbial strains in metagenomic datasets. Nat Biotechnol 33(10):1045–1052. https://doi.org/10.1038/nbt.3319

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Hyeon JY, Li S, Mann DA, Zhang S, Li Z, Chen Y, Deng X (2017) Quasi-metagenomics and realtime sequencing aided detection and subtyping of Salmonella enterica from food samples. Appl Environ Microbiol. https://doi.org/10.1128/AEM.02340-17

  53. Sedlar K, Kupkova K, Provaznik I (2017) Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics. Comput Struct Biotechnol J 15:48–55. https://doi.org/10.1016/j.csbj.2016.11.005

    Article  CAS  PubMed  Google Scholar 

  54. Cocolin L, Mataragas M, Bourdichon F, Doulgeraki A, Pilet MF, Jagadeesan B, Rantsiou K, Phister T (2017) Next generation microbiological risk assessment meta-omics: the next need for integration. Int J Food Microbiol. https://doi.org/10.1016/j.ijfoodmicro.2017.11.008

    Article  PubMed  Google Scholar 

  55. Leonard SR, Mammel MK, Lacher DW, Elkins CA (2015) Application of metagenomic sequencing to food safety: detection of Shiga toxin-producing Escherichia coli on fresh bagged spinach. Appl Environ Microbiol 81(23):8183–8191. https://doi.org/10.1128/AEM.02601-15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Leonard SR, Mammel MK, Lacher DW, Elkins CA (2016) Strain-level discrimination of shiga toxin-producing Escherichia coli in spinach using metagenomic sequencing. PLoS One 11(12):e0167870. https://doi.org/10.1371/journal.pone.0167870

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ottesen AR, Gonzalez A, Bell R, Arce C, Rideout S, Allard M, Evans P, Strain E, Musser S, Knight R, Brown E, Pettengill JB (2013) Co-enriching microflora associated with culture based methods to detect Salmonella from tomato phyllosphere. PLoS One 8(9):e73079. https://doi.org/10.1371/journal.pone.0073079

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Ottesen A, Ramachandran P, Reed E, White JR, Hasan N, Subramanian P, Ryan G, Jarvis K, Grim C, Daquiqan N, Hanes D, Allard M, Colwell R, Brown E, Chen Y (2016) Enrichment dynamics of Listeria monocytogenes and the associated microbiome from naturally contaminated ice cream linked to a listeriosis outbreak. BMC Microbiol 16(1):275. https://doi.org/10.1186/s12866-016-0894-1

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Walsh AM, Crispie F, Daari K, O'Sullivan O, Martin JC, Arthur CT, Claesson MJ, Scott KP, Cotter PD (2017) Strain-level metagenomic analysis of the fermented dairy beverage Nunu highlights potential food safety risks. Appl Environ Microbiol 83(16). https://doi.org/10.1128/AEM.01144-17

  60. Yang X, Noyes NR, Doster E, Martin JN, Linke LM, Magnuson RJ, Yang H, Geornaras I, Woerner DR, Jones KL, Ruiz J, Boucher C, Morley PS, Belk KE (2016) Use of metagenomic shotgun sequencing technology to detect foodborne pathogens within the microbiome of the beef production chain. Appl Environ Microbiol 82(8):2433–2443. https://doi.org/10.1128/AEM.00078-16

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Loman NJ, Constantinidou C, Christner M, Rohde H, Chan JZ, Quick J, Weir JC, Quince C, Smith GP, Betley JR, Aepfelbacher M, Pallen MJ (2013) A culture-independent sequence-based metagenomics approach to the investigation of an outbreak of Shiga-toxigenic Escherichia coli O104:H4. JAMA 309(14):1502–1510. https://doi.org/10.1001/jama.2013.3231

    Article  CAS  PubMed  Google Scholar 

  62. Huang AD, Luo C, Pena-Gonzalez A, Weigand MR, Tarr CL, Konstantinidis KT (2017) Metagenomics of two severe foodborne outbreaks provides diagnostic signatures and signs of coinfection not attainable by traditional methods. Appl Environ Microbiol 83(3). https://doi.org/10.1128/AEM.02577-16

  63. De Filippis F, Parente E, Ercolini D (2017) Metagenomics insights into food fermentations. Microb Biotechnol 10(1):91–102. https://doi.org/10.1111/1751-7915.12421

    Article  PubMed  Google Scholar 

  64. Ferrocino I, Bellio A, Giordano M, Macori G, Romano A, Rantsiou K, Decastelli L, Cocolin L (2018) Shotgun metagenomics and volatilome profile of the microbiota of fermented sausages. Appl Environ Microbiol 84(3). https://doi.org/10.1128/AEM.02120-17

  65. Sternes PR, Lee D, Kutyna DR, Borneman AR (2017) A combined meta-barcoding and shotgun metagenomic analysis of spontaneous wine fermentation. Gigascience 6(7):1–10. https://doi.org/10.1093/gigascience/gix040

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Sulaiman J, Gan HM, Yin WF, Chan KG (2014) Microbial succession and the functional potential during the fermentation of Chinese soy sauce brine. Front Microbiol 5:556. https://doi.org/10.3389/fmicb.2014.00556

    Article  PubMed  PubMed Central  Google Scholar 

  67. O’Neill J (2016) Tackling drug-resistance infections globally:final report and recommendations. Review on antimicrobial resistance

    Google Scholar 

  68. Luby E, Ibekwe AM, Zilles J, Pruden A (2016) Molecular methods for assessment of antibiotic resistance in agricultural ecosystems: prospects and challenges. J Environ Qual 45(2):441–453. https://doi.org/10.2134/jeq2015.07.0367

    Article  CAS  PubMed  Google Scholar 

  69. Robinson TP, Bu DP, Carrique-Mas J, Fevre EM, Gilbert M, Grace D, Hay SI, Jiwakanon J, Kakkar M, Kariuki S, Laxminarayan R, Lubroth J, Magnusson U, Thi Ngoc P, Van Boeckel TP, Woolhouse ME (2016) Antibiotic resistance is the quintessential one health issue. Trans R Soc Trop Med Hyg 110(7):377–380. https://doi.org/10.1093/trstmh/trw048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Verraes C, Van Boxstael S, Van Meervenne E, Van Coillie E, Butaye P, Catry B, de Schaetzen MA, Van Huffel X, Imberechts H, Dierick K, Daube G, Saegerman C, De Block J, Dewulf J, Herman L (2013) Antimicrobial resistance in the food chain: a review. Int J Environ Res Public Health 10(7):2643–2669. https://doi.org/10.3390/ijerph10072643

    Article  PubMed  PubMed Central  Google Scholar 

  71. Bengtsson-Palme J (2017) Antibiotic resistance in the food supply chain: where can sequencing and metagenomics aid risk assessment? Curr Opin Food Sci 14:66–71. https://doi.org/10.1016/j.cofs.2017.01.010

    Article  Google Scholar 

  72. Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DG (2016) The structure and diversity of human, animal and environmental resistomes. Microbiome 4(1):54. https://doi.org/10.1186/s40168-016-0199-5

    Article  PubMed  PubMed Central  Google Scholar 

  73. Noyes NR, Yang X, Linke LM, Magnuson RJ, Cook SR, Zaheer R, Yang H, Woerner DR, Geornaras I, McArt JA, Gow SP, Ruiz J, Jones KL, Boucher CA, McAllister TA, Belk KE, Morley PS (2016) Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems. Sci Rep 6:24645. https://doi.org/10.1038/srep24645

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Pitta DW, Dou Z, Kumar S, Indugu N, Toth JD, Vecchiarelli B, Bhukya B (2016) Metagenomic evidence of the prevalence and distribution patterns of antimicrobial resistance genes in dairy agroecosystems. Foodborne Pathog Dis 13(6):296–302. https://doi.org/10.1089/fpd.2015.2092

    Article  CAS  PubMed  Google Scholar 

  75. Thomas M, Webb M, Ghimire S, Blair A, Olson K, Fenske GJ, Fonder AT, Christopher-Hennings J, Brake D, Scaria J (2017) Metagenomic characterization of the effect of feed additives on the gut microbiome and antibiotic resistome of feedlot cattle. Sci Rep 7(1):12257. https://doi.org/10.1038/s41598-017-12481-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Auffret MD, Dewhurst RJ, Duthie CA, Rooke JA, John Wallace R, Freeman TC, Stewart R, Watson M, Roehe R (2017) The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome 5(1):159. https://doi.org/10.1186/s40168-017-0378-z

    Article  PubMed  PubMed Central  Google Scholar 

  77. Noyes NR, Weinroth ME, Parker JK, Dean CJ, Lakin SM, Raymond RA, Rovira P, Doster E, Abdo Z, Martin JN, Jones KL, Ruiz J, Boucher CA, Belk KE, Morley PS (2017) Enrichment allows identification of diverse, rare elements in metagenomic resistome-virulome sequencing. Microbiome 5(1):142. https://doi.org/10.1186/s40168-017-0361-8

    Article  PubMed  PubMed Central  Google Scholar 

  78. Zaheer R, Noyes N, Ortega Polo R, Cook SR, Marinier E, Van Domselaar G, Belk KE, Morley PS, McAllister TA (2018) Impact of sequencing depth on the characterization of the microbiome and resistome. Sci Rep 8(1):5890. https://doi.org/10.1038/s41598-018-24280-8

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Munk P, Andersen VD, de Knegt L, Jensen MS, Knudsen BE, Lukjancenko O, Mordhorst H, Clasen J, Agerso Y, Folkesson A, Pamp SJ, Vigre H, Aarestrup FM (2017) A sampling and metagenomic sequencing-based methodology for monitoring antimicrobial resistance in swine herds. J Antimicrob Chemother 72(2):385–392. https://doi.org/10.1093/jac/dkw415

    Article  CAS  PubMed  Google Scholar 

  80. Osterlund T, Jonsson V, Kristiansson E (2017) HirBin: high-resolution identification of differentially abundant functions in metagenomes. BMC Genomics 18(1):316. https://doi.org/10.1186/s12864-017-3686-6

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. McArthur AG, Tsang KK (2017) Antimicrobial resistance surveillance in the genomic age. Ann N Y Acad Sci 1388(1):78–91. https://doi.org/10.1111/nyas.13289

    Article  PubMed  Google Scholar 

  82. Afshinnekoo E, Chou C, Alexander N, Ahsanuddin S, Schuetz AN, Mason CE (2017) Precision metagenomics: rapid metagenomic analyses for infectious disease diagnostics and public health surveillance. J Biomol Tech 28(1):40–45. https://doi.org/10.7171/jbt.17-2801-007

    Article  PubMed  PubMed Central  Google Scholar 

  83. Xavier BB, Das AJ, Cochrane G, De Ganck S, Kumar-Singh S, Aarestrup FM, Goossens H, Malhotra-Kumar S (2016) Consolidating and exploring antibiotic resistance gene data resources. J Clin Microbiol 54(4):851–859. https://doi.org/10.1128/JCM.02717-15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, Lago BA, Dave BM, Pereira S, Sharma AN, Doshi S, Courtot M, Lo R, Williams LE, Frye JG, Elsayegh T, Sardar D, Westman EL, Pawlowski AC, Johnson TA, Brinkman FS, Wright GD, McArthur AG (2017) CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Res 45(D1):D566–D573. https://doi.org/10.1093/nar/gkw1004

    Article  CAS  PubMed  Google Scholar 

  85. Liu B, Pop M (2009) ARDB—antibiotic resistance genes database. Nucleic Acids Res 37(Database issue):D443–D447. https://doi.org/10.1093/nar/gkn656

    Article  CAS  PubMed  Google Scholar 

  86. Kleinheinz KA, Joensen KG, Larsen MV (2014) Applying the ResFinder and VirulenceFinder web-services for easy identification of acquired antibiotic resistance and E. coli virulence genes in bacteriophage and prophage nucleotide sequences. Bacteriophage 4(1):e27943. https://doi.org/10.4161/bact.27943

    Article  PubMed  PubMed Central  Google Scholar 

  87. Tsafnat G, Copty J, Partridge SR (2011) RAC: repository of antibiotic resistance cassettes. Database (Oxford) 2011:bar054. https://doi.org/10.1093/database/bar054

    Article  CAS  Google Scholar 

  88. Thai QK, Bos F, Pleiss J (2009) The lactamase engineering database: a critical survey of TEM sequences in public databases. BMC Genomics 10:390. https://doi.org/10.1186/1471-2164-10-390

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Thai QK, Pleiss J (2010) SHV lactamase engineering database: a reconciliation tool for SHV beta-lactamases in public databases. BMC Genomics 11:563. https://doi.org/10.1186/1471-2164-11-563

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Danishuddin M, Hassan Baig M, Kaushal L, Khan AU (2013) BLAD: a comprehensive database of widely circulated beta-lactamases. Bioinformatics 29(19):2515–2516. https://doi.org/10.1093/bioinformatics/btt417

    Article  CAS  PubMed  Google Scholar 

  91. Zhou CE, Smith J, Lam M, Zemla A, Dyer MD, Slezak T (2007) MvirDB—a microbial database of protein toxins, virulence factors and antibiotic resistance genes for bio-defence applications. Nucleic Acids Res 35(Database):D391–D394. https://doi.org/10.1093/nar/gkl791

    Article  CAS  PubMed  Google Scholar 

  92. Lakin SM, Dean C, Noyes NR, Dettenwanger A, Ross AS, Doster E, Rovira P, Abdo Z, Jones KL, Ruiz J, Belk KE, Morley PS, Boucher C (2017) MEGARes: an antimicrobial resistance database for high throughput sequencing. Nucleic Acids Res 45(D1):D574–D580. https://doi.org/10.1093/nar/gkw1009

    Article  CAS  PubMed  Google Scholar 

  93. Pal C, Bengtsson-Palme J, Rensing C, Kristiansson E, Larsson DG (2014) BacMet: antibacterial biocide and metal resistance genes database. Nucleic Acids Res 42(Database issue):D737–D743. https://doi.org/10.1093/nar/gkt1252

    Article  CAS  PubMed  Google Scholar 

  94. Gibson MK, Forsberg KJ, Dantas G (2015) Improved annotation of antibiotic resistance determinants reveals microbial resistomes cluster by ecology. ISME J 9(1):207–216. https://doi.org/10.1038/ismej.2014.106

    Article  CAS  PubMed  Google Scholar 

  95. Scaria J, Chandramouli U, Verma SK (2005) Antibiotic resistance genes online (ARGO): a database on vancomycin and beta-lactam resistance genes. Bioinformation 1(1):5–7

    Article  PubMed  PubMed Central  Google Scholar 

  96. Srivastava A, Singhal N, Goel M, Virdi JS, Kumar M (2014) CBMAR: a comprehensive beta-lactamase molecular annotation resource. Database (Oxford) 2014:bau111. https://doi.org/10.1093/database/bau111

    Article  CAS  Google Scholar 

  97. The r, database (2014) http://www.1928diagnostics.com/resdb/

  98. Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, Rolain JM (2014) ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother 58(1):212–220. https://doi.org/10.1128/AAC.01310-13

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Yang Y, Jiang X, Chai B, Ma L, Li B, Zhang A, Cole JR, Tiedje JM, Zhang T (2016) ARGs-OAP: online analysis pipeline for antibiotic resistance genes detection from metagenomic data using an integrated structured ARG-database. Bioinformatics 32(15):2346–2351. https://doi.org/10.1093/bioinformatics/btw136

    Article  CAS  PubMed  Google Scholar 

  100. Rowe W, Baker KS, Verner-Jeffreys D, Baker-Austin C, Ryan JJ, Maskell D, Pearce G (2015) Search engine for antimicrobial resistance: a cloud compatible pipeline and web Interface for rapidly detecting antimicrobial resistance genes directly from sequence data. PLoS One 10(7):e0133492. https://doi.org/10.1371/journal.pone.0133492

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L (2018) DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome 6(1):23. https://doi.org/10.1186/s40168-018-0401-z

    Article  PubMed  PubMed Central  Google Scholar 

  102. de Man TJ, Limbago BM (2016) SSTAR, a stand-alone easy-to-use antimicrobial resistance gene predictor. mSphere 1(1). https://doi.org/10.1128/mSphere.00050-15

  103. Hunt M, Mather AE, Sanchez-Buso L, Page AJ, Parkhill J, Keane JA, Harris SR (2017) ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads. Microb Genom 3(10):e000131. https://doi.org/10.1099/mgen.0.000131

    Article  PubMed  PubMed Central  Google Scholar 

  104. Olekhnovich EI, Vasilyev AT, Ulyantsev VI, Kostryukova ES, Tyakht AV (2018) MetaCherchant: analyzing genomic context of antibiotic resistance genes in gut microbiota. Bioinformatics 34(3):434–444. https://doi.org/10.1093/bioinformatics/btx681

    Article  CAS  PubMed  Google Scholar 

  105. Lanza VF, Baquero F, Martinez JL, Ramos-Ruiz R, Gonzalez-Zorn B, Andremont A, Sanchez-Valenzuela A, Ehrlich SD, Kennedy S, Ruppe E, van Schaik W, Willems RJ, de la Cruz F, Coque TM (2018) In-depth resistome analysis by targeted metagenomics. Microbiome 6(1):11. https://doi.org/10.1186/s40168-017-0387-y

    Article  PubMed  PubMed Central  Google Scholar 

  106. Antonopoulos DA, Assaf R, Aziz RK, Brettin T, Bun C, Conrad N, Davis JJ, Dietrich EM, Disz T, Gerdes S, Kenyon RW, Machi D, Mao C, Murphy-Olson DE, Nordberg EK, Olsen GJ, Olson R, Overbeek R, Parrello B, Pusch GD, Santerre J, Shukla M, Stevens RL, VanOeffelen M, Vonstein V, Warren AS, Wattam AR, Xia F, Yoo H (2017) PATRIC as a unique resource for studying antimicrobial resistance. Brief Bioinform. https://doi.org/10.1093/bib/bbx083

    Article  PubMed Central  Google Scholar 

  107. Oh M, Pruden A, Chen C, Heath LS, Xia K, Zhang L (2018) MetaCompare: a computational pipeline for prioritizing environmental resistome risk. FEMS Microbiol Ecol. https://doi.org/10.1093/femsec/fiy079

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arnaud Bridier .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Bridier, A. (2019). Exploring Foodborne Pathogen Ecology and Antimicrobial Resistance in the Light of Shotgun Metagenomics. In: Bridier, A. (eds) Foodborne Bacterial Pathogens. Methods in Molecular Biology, vol 1918. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9000-9_19

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-9000-9_19

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-4939-8999-7

  • Online ISBN: 978-1-4939-9000-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics