The pig is a major species for livestock production and is also extensively used as the preferred model species for analyses of a wide range of human physiological functions and diseases1. The importance of the gut microbiota in complementing the physiology and genome of the host is now well recognized2. Knowledge of the functional interplay between the gut microbiota and host physiology in humans has been advanced by the human gut reference catalogue3,4. Thus, establishment of a comprehensive pig gut microbiome gene reference catalogue constitutes a logical continuation of the recently published pig genome5. By deep metagenome sequencing of faecal DNA from 287 pigs, we identified 7.7 million non-redundant genes representing 719 metagenomic species. Of the functional pathways found in the human catalogue, 96% are present in the pig catalogue, supporting the potential use of pigs for biomedical research. We show that sex, age and host genetics are likely to influence the pig gut microbiome. Analysis of the prevalence of antibiotic resistance genes demonstrated the effect of eliminating antibiotics from animal diets and thereby reducing the risk of spreading antibiotic resistance associated with farming systems.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.


  1. 1.

    Advances in swine biomedical model genomics. Int. J. Biol. Sci. 3, 179–184 (2007).

  2. 2.

    & The gut microbiota—masters of host development and physiology. Nat. Rev. Microbiol. 11, 227–238 (2013).

  3. 3.

    et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464, 59–65 (2010).

  4. 4.

    et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

  5. 5.

    et al. Analyses of pig genomes provide insight into porcine demography and evolution. Nature 491, 393–398 (2012).

  6. 6.

    & Antibiotic types and handling practices in disease management among pig farms in Ashanti region, Ghana. J. Vet. Med. 2014, e531952 (2014).

  7. 7.

    et al. Antimicrobial resistance in commensal flora of pig farmers. Emerg. Infect. Dis. 10, 873–879 (2004).

  8. 8.

    , , & Patterns of antimicrobial resistance observed in Escherichia coli isolates obtained from domestic- and wild-animal faecal samples, human septage, and surface water. Appl. Environ. Microbiol. 71, 1394–1404 (2005).

  9. 9.

    & Antimicrobial resistance, food safety, and one health: the need for convergence. Annu. Rev. Food Sci. Technol. 7, 287–312 (2016).

  10. 10.

    , , , & Antibiotic resistance genes as an emerging environmental contaminant. Environ. Rev. 24 205–218 (2016).

  11. 11.

    et al. Antibiotics and antibiotic resistance in agroecosystems: state of the science. J. Environ. Qual. 45, 394–406 (2016).

  12. 12.

    et al. A catalog of the mouse gut metagenome. Nat. Biotechnol. 33, 1103–1108 (2015).

  13. 13.

    et al. Population history and genomic signatures for high-altitude adaptation in Tibetan pigs. BMC Genomics 15, 834 (2014).

  14. 14.

    et al. Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors. Proc. Natl Acad. Sci. USA 107, 18933–18938 (2010).

  15. 15.

    , & Antibiotics and the gut microbiota. J. Clin. Invest. 124, 4212–4218 (2014).

  16. 16.

    et al. In-feed antibiotic effects on the swine intestinal microbiome. Proc. Natl Acad. Sci. USA 109, 1691–1696 (2012).

  17. 17.

    , , , & A common mechanism of cellular death induced by bactericidal antibiotics. Cell 130, 797–810 (2007).

  18. 18.

    et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. GigaScience 1, 18 (2012).

  19. 19.

    , & Genemarks: a self-training method for prediction of gene starts in microbial genomes. Implications for finding sequence motifs in regulatory regions. Nucleic Acids Res. 29, 2607–2618 (2001).

  20. 20.

    BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

  21. 21.

    & Taxonomic classification of metagenomic shotgun sequences with CARMA3. Nucleic Acids Res. 39, e91 (2011).

  22. 22.

    , , , & Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

  23. 23.

    et al. eggNOG v4.0: nested orthology inference across 3686 organisms. Nucleic Acids Res. 42, D231–D239 (2014).

  24. 24.

    , , , & KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2015).

  25. 25.

    et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).

  26. 26.

    & VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

  27. 27.

    & An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27, 325–349 (1957).

  28. 28.

    The analysis of proximities: multidimensional scaling with an unknown distance function. I. Psychometrika 27, 125–140 (1962).

  29. 29.

    , , & Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200–1202 (2013).

  30. 30.

    , , , & Ipath2.0: interactive pathway explorer. Nucleic Acids Res. 39, W412–W415 (2011).

Download references


This work was supported financially by the INRA metaprogramme Meta-omics of Microbial Ecosystems (MEM), the Animal Genetics Division of INRA, the Biology Department of the University of Copenhagen and BGI-Shenzhen. Y.R.C. was funded by the European Union, in the framework of the Marie-Curie FP7 COFUND People Program, through the award of an AgreenSkills' fellowship (grant agreement no. 267196) linked to the METALIT project funded by the INRA MEM metaprogramme. This study was supported by the Shenzhen Municipal Government of China (grant nos. JCYJ20140418095735538, DRC-SZ[2015]162, CXB201108250098A and CXZZ20150330171521403), the National Transgenic Project of China (2014ZX0801007B), the Science and Technology Plan of Shenzhen City, the Shenzhen–Hong Kong Innovation Circle Research Fund, China (SGLH20120928140607107). The work was also supported by the Danish Innovation Fund (grant nos. 4105-00011A and 0603-00774B) and Metagenopolis grant ANR-11-DPBS-0001. The authors acknowledge technical support for faecal stool sampling provided by the INRA experimental units (Y. Billon, UE GENESI at le Magneraud; C. Jaeger and H. Demay, UMR SENAH at Saint Gilles; N. Müller, UE0787 at le Rheu; E. Venturi and his team, UE PAO at Nouzilly; J.-L. Gourdine and D. Renaudeau, URZ in the Guadeloupe; J.-J. Leplat, UMR GABI at Jouy-en-Josas). The authors thank K. Pedersen at Svindinge farm, T. Povlsen at Blæsenborg farm, V. Jensen and K. Jensen at Stærsminde farm and H. Loft Hansen for technical assistance in relation to collection of faecal samples from the Danish farms. The authors also thank X. Liu and Zhigang at the Institute of Allergy & Immunology, Shenzhen University, and L. Fang Liang, Q. Wei and Y. Li at the technical unit of the Chinese farms for help with faecal sampling and administration. The authors thank C. Denis (INRA, UMR GABI, Jouy-en-Josas) and F. Levenez (INRA, UMR MICALIS and MetaGenoPolis, Jouy-en-Josas) for faecal DNA preparation. The authors acknowledge the @BRIDGe platform (INRA, Jouy-en-Josas) for safe storage of samples. The authors are grateful to J. Nathaniel Paulson for kind assistance with the use of the R package referred to as metagenomeSeq, and J. Lunney (Animal Parasitic Diseases Laboratory, ARS, USDA, MD, Beltsville, USA) for discussions and the contribution to manuscript editing.

Author information

Author notes

    • Liang Xiao
    • , Jordi Estellé
    •  & Pia Kiilerich

    These authors contributed equally to this work

    • Qiang Feng
    •  & Edi Prifti

    Present addresses: Department of Human Microbiome, School of Stomatology, Shandong University, Shandong Provincial Key Laboratory of Oral Tissue Regeneration, Jinan, China. (Q.F.). Institute of Cardiometabolism and Nutrition, 75013 Paris, France. (E.P.).


  1. BGI-Shenzhen, 518083 Shenzhen, China

    • Liang Xiao
    • , Zhongkui Xia
    • , Qiang Feng
    • , Suisha Liang
    • , Chuan Liu
    • , Junhua Li
    • , Huijue Jia
    • , Xin Liu
    • , Xun Xu
    • , Lise Madsen
    • , Karsten Kristiansen
    •  & Jun Wang
  2. GABI, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France

    • Jordi Estellé
    • , Yuliaxis Ramayo-Caldas
    •  & Claire Rogel-Gaillard
  3. Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark

    • Pia Kiilerich
    • , Lise Madsen
    • , Karsten Kristiansen
    •  & Jun Wang
  4. SEGES Pig Research Centre, DK-1609 Copenhagen V, Denmark

    • Anni Øyan Pedersen
    •  & Niels Jørgen Kjeldsen
  5. Shenzhen Engineering Laboratory of Detection and Intervention of Human Intestinal Microbiome

    • Chuan Liu
  6. MICALIS Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France

    • Emmanuelle Maguin
    •  & Joël Doré
  7. MetaGénoPolis, INRA, Université Paris-Saclay, 78350 Jouy-en-Josas, France

    • Joël Doré
    • , Nicolas Pons
    • , Emmanuelle Le Chatelier
    • , Edi Prifti
    •  & Stanislav D. Ehrlich
  8. Shenzhen Key Laboratory of Human Commensal Microorganisms and Health Research

    • Junhua Li
  9. King's College London, Centre for Host–Microbiome Interactions, Dental Institute Central Office, Guy's Hospital, London SE1 9RT, UK

    • Stanislav D. Ehrlich
  10. National Institute of Nutrition and Seafood Research (NIFES), Postboks 2029, Nordnes, N-5817 Bergen, Norway

    • Lise Madsen


  1. Search for Liang Xiao in:

  2. Search for Jordi Estellé in:

  3. Search for Pia Kiilerich in:

  4. Search for Yuliaxis Ramayo-Caldas in:

  5. Search for Zhongkui Xia in:

  6. Search for Qiang Feng in:

  7. Search for Suisha Liang in:

  8. Search for Anni Øyan Pedersen in:

  9. Search for Niels Jørgen Kjeldsen in:

  10. Search for Chuan Liu in:

  11. Search for Emmanuelle Maguin in:

  12. Search for Joël Doré in:

  13. Search for Nicolas Pons in:

  14. Search for Emmanuelle Le Chatelier in:

  15. Search for Edi Prifti in:

  16. Search for Junhua Li in:

  17. Search for Huijue Jia in:

  18. Search for Xin Liu in:

  19. Search for Xun Xu in:

  20. Search for Stanislav D. Ehrlich in:

  21. Search for Lise Madsen in:

  22. Search for Karsten Kristiansen in:

  23. Search for Claire Rogel-Gaillard in:

  24. Search for Jun Wang in:


C.R.-G., J.W., L.M., E.M., J.D., S.D.E. and K.K. conceived and designed the project. K.K., C.R.-G., L.M., S.D.E., J.W., L.X., J.E., P.K., Y.R.-C., X.X., X.L., N.J.K. and Z.X. monitored the project. A.Ø.P., C.L. and E.P. collected samples and performed experiments. K.K., C.R.-G., L.M., J.W., J.E., Y.R.-C., P.K., N.P., E.L.C., E.P., S.D.E., Z.X., Q.F., S.L., A.Ø.P. and J.L. analysed and interpreted the data. K.K., C.R.-G., J.E., Y.R.-C., S.D.E., L.M., P.K., H.J. and L.X. wrote the paper. All authors commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Karsten Kristiansen or Claire Rogel-Gaillard or Jun Wang.

Supplementary information

PDF files

  1. 1.

    Supplementary information

    Supplementary Figures 1–8, legends for Supplementary Tables 1–9

Excel files

  1. 1.

    Supplementary Table 1

    Background information on the 287 pig samples.

  2. 2.

    Supplementary Table 2

    Description of the assembly data from the 287 samples.

  3. 3.

    Supplementary Table 3

    Assembly results of the pig, human and mouse data.

  4. 4.

    Supplementary Table 4

    Significantly differentially abundant genes in the gut microbiota composition between castrated males and females (Svindinge farm) at the genus and species bacteria levels.

  5. 5.

    Supplementary Table 5

    Significantly differentially represented KEGG pathways in the gut microbiota between castrated males and females (Svindinge farm).

  6. 6.

    Supplementary Table 6

    Significantly differentially abundant genes in the gut microbiota composition between males and females (Stæminde farm, wet feed) at the genus and species bacteria levels.

  7. 7.

    Supplementary Table 7

    Significantly differentially abundant MGSs in the gut microbiota composition of males and females (Stæminde farm, wet feed).

  8. 8.

    Supplementary Table 8

    Significantly differentially represented KEGG pathways in the gut microbiota between males and females (Stæminde farm, wet feed).

  9. 9.

    Supplementary Table 9

    List of the KEGG pathways mapped by iPATH2 that were significantly differentially represented in the gut microbiota between males and females (11 males, 14 females, Stæminde farm, wet feed). The three most represented pathways are highlighted in grey. This list is derived from Supplementary Table 8 and correspond to the differentially abundant functions mapping against the KEGG, according to iPATH2 tools.

About this article

Publication history