A reference gene catalogue of the pig gut microbiome

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.

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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).

References

  1. 1

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

    CAS  Article  Google Scholar 

  2. 2

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

    CAS  Article  Google Scholar 

  3. 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. 4

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

    CAS  Article  Google Scholar 

  5. 5

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

    CAS  Article  Google Scholar 

  6. 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. 7

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

    CAS  Article  Google Scholar 

  8. 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).

    CAS  Article  Google Scholar 

  9. 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).

    CAS  Article  Google Scholar 

  10. 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. 11

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

    CAS  Article  Google Scholar 

  12. 12

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

    CAS  Article  Google Scholar 

  13. 13

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

    Article  Google Scholar 

  14. 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).

    CAS  Article  Google Scholar 

  15. 15

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

    CAS  Article  Google Scholar 

  16. 16

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

    CAS  Article  Google Scholar 

  17. 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).

    CAS  Article  Google Scholar 

  18. 18

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

    Article  Google Scholar 

  19. 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).

    CAS  Article  Google Scholar 

  20. 20

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

    CAS  Article  Google Scholar 

  21. 21

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

    CAS  Article  Google Scholar 

  22. 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).

    CAS  Article  Google Scholar 

  23. 23

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

    CAS  Article  Google Scholar 

  24. 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  Google Scholar 

  25. 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).

    CAS  Article  Google Scholar 

  26. 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. 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. 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. 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).

    CAS  Article  Google Scholar 

  30. 30

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

    CAS  Article  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.

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Authors

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 or Claire Rogel-Gaillard or Jun Wang.

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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)

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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

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