Abstract

Inflammatory bowel disease (IBD) is characterized by flares of inflammation with a periodic need for increased medication and sometimes even surgery. The aetiology of IBD is partly attributed to a deregulated immune response to gut microbiome dysbiosis. Cross-sectional studies have revealed microbial signatures for different IBD subtypes, including ulcerative colitis, colonic Crohn's disease and ileal Crohn's disease. Although IBD is dynamic, microbiome studies have primarily focused on single time points or a few individuals. Here, we dissect the long-term dynamic behaviour of the gut microbiome in IBD and differentiate this from normal variation. Microbiomes of IBD subjects fluctuate more than those of healthy individuals, based on deviation from a newly defined healthy plane (HP). Ileal Crohn's disease subjects deviated most from the HP, especially subjects with surgical resection. Intriguingly, the microbiomes of some IBD subjects periodically visited the HP then deviated away from it. Inflammation was not directly correlated with distance to the healthy plane, but there was some correlation between observed dramatic fluctuations in the gut microbiome and intensified medication due to a flare of the disease. These results will help guide therapies that will redirect the gut microbiome towards a healthy state and maintain remission in IBD.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    et al. Diversity of the human intestinal microbial flora. Science 308, 1635–1638 (2005).

  2. 2.

    et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009).

  3. 3.

    Human Microbiome Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  4. 4.

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

  5. 5.

    et al. Gut microbiome composition is linked to whole grain-induced immunological improvements. ISME J. 7, 269–280 (2013).

  6. 6.

    et al. Temporal variability is a personalized feature of the human microbiome. Genome Biol. 15, 531 (2014).

  7. 7.

    et al. Same exposure but two radically different responses to antibiotics: resilience of the salivary microbiome versus long-term microbial shifts in feces. mBio 6, e01693-15 (2015).

  8. 8.

    et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).

  9. 9.

    et al. Twin studies reveal specific imbalances in the mucosa-associated microbiota of patients with ileal Crohn's disease. Inflamm. Bowel Dis. 15, 653–660 (2009).

  10. 10.

    et al. A pyrosequencing study in twins shows that gastrointestinal microbial profiles vary with inflammatory bowel disease phenotypes. Gastroenterology 139, 1844–1854 (2010).

  11. 11.

    et al. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe 15, 382–392 (2014).

  12. 12.

    et al. Alterations in the intestinal microbiome (dysbiosis) as a predictor of relapse after infliximab withdrawal in Crohn's disease. Inflamm. Bowel Dis. 20, 978–986 (2014).

  13. 13.

    et al. Fecal microbial composition of ulcerative colitis and Crohn's disease patients in remission and subsequent exacerbation. PLoS ONE 9, e90981 (2014).

  14. 14.

    et al. Multiphasic analysis of the temporal development of the distal gut microbiota in patients following ileal pouch anal anastomosis. Microbiome 1, 9 (2013).

  15. 15.

    et al. Unstable composition of the fecal microbiota in ulcerative colitis during clinical remission. Am. J. Gastroenterol. 103, 643–648 (2008).

  16. 16.

    et al. Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491, 119–124 (2012).

  17. 17.

    et al. Moving pictures of the human microbiome. Genome Biol. 12, R50 (2011).

  18. 18.

    , , & The gut microbiota in IBD. Nat. Rev. Gastroenterol. Hepatol. 9, 599–608 (2012).

  19. 19.

    The utility of biomarkers in the diagnosis and therapy of inflammatory bowel disease. Gastroenterology 140, 1817–1826 (2011).

  20. 20.

    et al. Fecal microbiota in pediatric inflammatory bowel disease and its relation to inflammation. Am. J. Gastroenterol. 110, 921–930 (2015).

  21. 21.

    et al. Gut microbiome composition and function in experimental colitis during active disease and treatment-induced remission. ISME J. 8, 1403–1417 (2014).

  22. 22.

    et al. Correlation of faecal calprotectin and lactoferrin with an endoscopic score for Crohn's disease and histological findings. Aliment. Pharmacol. Ther. 28, 1221–1229 (2008).

  23. 23.

    Random forests. Mach. Learn. 45, 5–32 (2001).

  24. 24.

    , & . Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  25. 25.

    et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 518, 197–206 (2015).

  26. 26.

    , , , & Human-associated microbial signatures: examining their predictive value. Cell Host Microbe 10, 292–296 (2011).

  27. 27.

    et al. Toward an integrated clinical, molecular and serological classification of inflammatory bowel disease: report of a working party of the 2005 Montreal World Congress of Gastroenterology. Can. J. Gastroenterol. 19(Suppl. A), 5A–36A (2005).

  28. 28.

    DNA Extraction Protocol (EMP, 2011);

  29. 29.

    et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl Acad. Sci. USA 108, 4516–4522 (2011).

  30. 30.

    et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

  31. 31.

    , & SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics 28, 3211–3217 (2012).

  32. 32.

    et al. An improved Greengenes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J. 6, 610–618 (2012).

  33. 33.

    & Unifrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71, 8228–8235 (2005).

  34. 34.

    , , & EMPeror: a tool for visualizing high-throughput microbial community data. Gigascience 2, 16 (2013).

  35. 35.

    & R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).

  36. 36.

    & Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

  37. 37.

    & Classification and regression by randomForest. R News 2, 18–22 (2002).

  38. 38.

    . et al. vegan: Community Ecology Package. R package v. 2.4 (2016);

Download references

Acknowledgements

This research was partially supported by the United States National Institutes of Health (grant NIH IU54DE023798-01), the Pacific Northwest National Laboratory (contract DE-AC05-76RLO1830) and by the Crohn's and Colitis Foundation of America. Financial support was also provided by the Örebro University Hospital Research Foundation (grant OLL-507001), the Swedish Foundation For Strategic Research (grant RB13-0160) and the Swedish Research Council (521-2011-2764). Additional support was provided by a grant to Juniata College from the Howard Hughes Medical Institute through the Precollege and Undergraduate Science Education Program and the National Science Foundation (NSF award no. DBI-1248096). R.K. was a Howard Hughes Institute Early Career Scientist for part of the duration of this project.

Author information

Affiliations

  1. Department of Gastroenterology, Faculty of Medicine and Health, Örebro University, Örebro SE-701 82, Sweden

    • Jonas Halfvarson
  2. Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA

    • Colin J. Brislawn
    •  & Janet K. Jansson
  3. Juniata College, Huntingdon, Pennsylvania 16652, USA

    • Regina Lamendella
    • , Erin E. McClure
    •  & Mitchell F. Dunklebarger
  4. Department of Computer Science and Engineering, University of California, San Diego, California 92093, USA

    • Yoshiki Vázquez-Baeza
    •  & Rob Knight
  5. Max Planck Institute, 72076 Tübingen, Germany

    • William A. Walters
  6. National Security Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, USA

    • Lisa M. Bramer
  7. Clinical Epidemiology Unit, Department of Medicine Solna, Karolinska Institutet, SE-171 76 Stockholm, Sweden

    • Mauro D'Amato
  8. BioDonostia Health Research Institute San Sebastian, and IKERBASQUE Basque Foundation for Science Bilbao 48011, Spain

    • Mauro D'Amato
  9. Department of Biosciences and Nutrition, Karolinska Institutet, SE-171 77, Stockholm Sweden

    • Ferdinando Bonfiglio
  10. Department of Pediatrics, University of California, San Diego, California 92093, USA

    • Daniel McDonald
    •  & Rob Knight
  11. Center for Microbiome Innovation, University of California, San Diego, California 92093, USA

    • Antonio Gonzalez
    •  & Rob Knight

Authors

  1. Search for Jonas Halfvarson in:

  2. Search for Colin J. Brislawn in:

  3. Search for Regina Lamendella in:

  4. Search for Yoshiki Vázquez-Baeza in:

  5. Search for William A. Walters in:

  6. Search for Lisa M. Bramer in:

  7. Search for Mauro D'Amato in:

  8. Search for Ferdinando Bonfiglio in:

  9. Search for Daniel McDonald in:

  10. Search for Antonio Gonzalez in:

  11. Search for Erin E. McClure in:

  12. Search for Mitchell F. Dunklebarger in:

  13. Search for Rob Knight in:

  14. Search for Janet K. Jansson in:

Contributions

J.H. and J.K.J. designed the study. J.H. carried out the clinical study and collected samples and clinical data. C.J.B., Y.V.-B., W.A.W., D.M., M.D'A., F.B., A.G., R.L., E.E.M., L.M.B. and M.F.D. performed bioinformatics and statistical analyses of the data. C.J.B., Y.V.-B., W.A.W. and A.G. produced the figures. J.H., C.J.B., Y.V.-B., J.K.J. and R.K. wrote the majority of the text with input from all co-authors.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Janet K. Jansson.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    Legend for Supplementary Video 1, Legend for Supplementary Dataset 1, Supplementary Tables 1–4 and Supplementary Figures 1–4.

Text files

  1. 1.

    Supplementary Dataset 1

    Clinical metadata for patients and samples.

Videos

  1. 1.

    Supplementary Video 1

    Dynamics of healthy controls and IBD subtypes within ordinated UniFrac space.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nmicrobiol.2017.4

Further reading