Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

A reference gene catalogue of the pig gut microbiome

Nature Microbiology volume 1, Article number: 16161 (2016) Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Rarefaction curves and shared metagenome among pigs.
Figure 2: Comparison of the gut microbiome gene catalogues of human, mouse and pig.
Figure 3: Influence of breed, age and sex on the pig gut microbiota composition, as represented by NMDS plots at the NR gene count level.
Figure 4: Prevalence of antibiotic resistance genes (ARGs).

Similar content being viewed by others

References

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Sayah, R. S., Kaneene, J. B., Johnson, Y. & Miller, R. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Lammie, S. L. & Hughes, J. M. Antimicrobial resistance, food safety, and one health: the need for convergence. Annu. Rev. Food Sci. Technol. 7, 287–312 (2016).

    Article  CAS  PubMed  Google Scholar 

  10. Sanderson, H., Fricker, C., Brown, R. S., Majury, A. & Liss, S. N. Antibiotic resistance genes as an emerging environmental contaminant. Environ. Rev. 24 205–218 (2016).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  14. Benson, A. K. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Modi, S. R., Collins, J. J. & Relman, D. A. Antibiotics and the gut microbiota. J. Clin. Invest. 124, 4212–4218 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kohanski, M. A., Dwyer, D. J., Hayete, B., Lawrence, C. A. & Collins, J. J. A common mechanism of cellular death induced by bactericidal antibiotics. Cell 130, 797–810 (2007).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  19. Besemer, J., Lomsadze, A. & Borodovsky, M. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gerlach, W. & Stoye, J. Taxonomic classification of metagenomic shotgun sequences with CARMA3. Nucleic Acids Res. 39, e91 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  24. Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44, D457–D462 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  26. Dixon, P. & Palmer, M. W. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Paulson, J. N., Stine, O. C., Bravo, H. C. & Pop, M. Differential abundance analysis for microbial marker-gene surveys. Nat. Methods 10, 1200–1202 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yamada, T., Letunic, I., Okuda, S., Kanehisa, M. & Bork, P. Ipath2.0: interactive pathway explorer. Nucleic Acids Res. 39, W412–W415 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

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

  1. Qiang Feng & Edi Prifti

    Present address: ‡ 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.).,

  2. Liang Xiao, Jordi Estellé and Pia Kiilerich: These authors contributed equally to this work

Authors and Affiliations

  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. Department of Biology, Laboratory of Genomics and Molecular Biomedicine, 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

Authors
  1. Liang Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jordi Estellé
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pia Kiilerich
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuliaxis Ramayo-Caldas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhongkui Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qiang Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Suisha Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Anni Øyan Pedersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Niels Jørgen Kjeldsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Chuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Emmanuelle Maguin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Joël Doré
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Nicolas Pons
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Emmanuelle Le Chatelier
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Edi Prifti
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Junhua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Huijue Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Xin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Xun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Stanislav D. Ehrlich
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Lise Madsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Karsten Kristiansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Claire Rogel-Gaillard
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

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.

Corresponding authors

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

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Figures 1–8, legends for Supplementary Tables 1–9 (PDF 2038 kb)

Supplementary Table 1

Background information on the 287 pig samples. (XLSX 14 kb)

Supplementary Table 2

Description of the assembly data from the 287 samples. (XLSX 9 kb)

Supplementary Table 3

Assembly results of the pig, human and mouse data. (XLSX 10 kb)

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. (XLSX 11 kb)

Supplementary Table 5

Significantly differentially represented KEGG pathways in the gut microbiota between castrated males and females (Svindinge farm). (XLSX 9 kb)

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. (XLSX 12 kb)

Supplementary Table 7

Significantly differentially abundant MGSs in the gut microbiota composition of males and females (Stæminde farm, wet feed). (XLSX 17 kb)

Supplementary Table 8

Significantly differentially represented KEGG pathways in the gut microbiota between males and females (Stæminde farm, wet feed). (XLSX 54 kb)

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. (XLSX 27 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, L., Estellé, J., Kiilerich, P. et al. A reference gene catalogue of the pig gut microbiome. Nat Microbiol 1, 16161 (2016). https://doi.org/10.1038/nmicrobiol.2016.161

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/nmicrobiol.2016.161

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing